Anda Senberga, Liena Krastina, Vilija Matuleviciute ... · HVAC Heating, Ventilation and Air Conditioning IE Indoor Environment ISO International Organization for Standardization

OFFICE BUILDING SIMULATIONS:

ZONING AND INTERNAL LOADS

INFLUENCE OF ZONE PARTITIONING IN BSIM

&

INTERNAL LOAD COMPARISON WITH TYPICAL SIMULATION INPUTS

Anda Senberga, Liena Krastina, Vilija Matuleviciute

Building Energy Design

Department of Civil Engineering

Aalborg University

This dissertation is submitted for the degree of Master of Building Energy Design

January 2019

I

Building Energy Design

Department of Civil Engineering

Thomas Manns Vej 23 Dk-9220 Aalborg East

Denmark

Phone: +45 9940 8484

E-mail: [email protected]

Title: Office Building Simulations: Zoning and Internal Loads

Semester: 4th semester

Semester theme: Master’s thesis

Project period: Autumn Semester, 2018

ECTS: 30

Supervisor: Rasmus Lund Jensen and Torben Østergård

Project group: BED4

Anda Senberga

Liena Krastina

Vilija Matuleviciute

Number printed: -

Report: 44 Pages

Appendices: 100 Pages

Total: 162 Pages

By signing this document, each member of the group confirms participation on equal terms in the process of

writing the project. Thus, each member of the group is responsible for the all contents of the project.

SYNOPSIS:

The accuracy of the BPS model’s results

depends on the input data and the zoning

complexity of the building.

This paper investigates the zonal

configurations, the significance of the

thermal mass and internal load

variations in the zone, and their impact

on the annual heating and cooling

demand and thermal comfort.

The collected real-time people- and plug

load profiles from an office building in

Denmark resulted in lower loads than

predefined by the national standards and

industry guides.

As a result of this project, a guide

regarding modelling an office building

is provided.

mailto:[email protected]

III

The last two years have been the most informative, happiest, hurtful, thoughtful, altering,

moulding, powerful, accepting years of my life. [1]

IV

PREFACE

This report is the result of the project carried out during the 4th semester of the Master of Science

(MSc) in Technology in Civil Engineering - Building Energy Design at Aalborg University in

Denmark.

The workload corresponds to 30 ECTS credits accomplished from September 2018 to January

2019.

This project was carried out under supervision of Associate Professor Rasmus Lund Jensen and

Industrial PhD graduate Torben Østergaard.

Software programs used for the project are BSim version 7.16.8.11 issued by Danish Building

Research Institute Aalborg University, BTR Netcom Energy-Net Version 2.3, Logitech

Webcam Software Version 2.51.828.0, Autoit Version v3.3.14.3, Autodesk Revit 2018 and

Microsoft Office package.

V

ABSTRACT

To develop a complex building energy simulation model requires time and work to collect

enough data to produce accurate results during the design phase of a project.

This paper provides two main studies. The first study involves intensive building performance

simulation work. Its purpose is to investigate how different building zoning strategies and the

model complexity impact the predicted annual heating and cooling demand in office buildings.

It also investigates the importance of internal loads and thermal mass regarding the annual

energy use and thermal comfort.

The second study involves a real-time occupancy- and plug load detection in an office building

situated in Denmark. Energy-related occupant behaviour is vital when designing and

controlling building systems. Occupant behaviour in the building simulations is often simplified

as static schedules based on the national standards, and their dynamic behaviour is neglected.

The more dynamic internal load profiles, described by the Danish Industry guide, are suggesting

a 100% occupancy during the office working hours, while the monitored occupancy in the

office results in lower hourly peak loads. Moreover, while the monitored plug loads follow the

pattern mentioned in the guide, the average hourly peak load is lower by 55%.

This paper results in a guideline on how to simulate office buildings for annual heating and

cooling needs considering the thermal zoning and the application of the internal loads capacity.

The primary goal of the recommendation is to advise building engineers on the use of modelling

to improve the accuracy of design-phase BPS models.

Keywords: thermal zoning, occupant behaviour, equipment use, plug load profiles, people load

profiles, office buildings, dynamic simulation models, stochastic modelling, building

performance simulations, energy use, peak heating load, peak cooling load, overheating risk.

VI

ACKNOWLEDGEMENTS

We would like to express our gratitude to Rasmus Lund Jensen, Associate Professor of the

Department of Civil Engineering at Aalborg University, and Torben Østergård, Specialist at

MOE & Postdoc at Aalborg University, for managing and guiding throughout the semester.

Their guidance and insight into the topics, their thought-out questions and input in discussions

have been invaluable in our research. We would also like to express our gratitude to the

employees of MOEs’ Aarhus office, for their patience and collaboration during the semester

that has been truly valuable.

VII

CONTENTS

1 INTRODUCTION .............................................................................................................. 1

1.1 BACKGROUND .............................................................................................................. 1

1.2 PROBLEM FORMULATION ............................................................................................. 2

1.3 METHODOLOGY ........................................................................................................... 3

1.4 LIMITATIONS ................................................................................................................ 4

2 THERMAL ZONING IMPACT ON BPS MODEL RESULTS ..................................... 5

2.1 INTRODUCTION ............................................................................................................ 5

2.2 LITERATURE RESEARCH: PREVIOUS STUDIES .............................................................. 6

2.3 CASE STUDIES .............................................................................................................. 7

2.3.1 Thermal Zoning Methodology and Complexity of Models .................................................8

2.3.2 Internal Loads and Systems ..............................................................................................11

2.4 RESULTS AND DISCUSSION ........................................................................................ 11

2.4.1 Heating, cooling demand and overheating risk ................................................................12

2.4.2 Peak load for heating and cooling ...................................................................................17

2.5 FINDINGS .................................................................................................................... 18

3 OPEN-PLAN OFFICE INTERNAL LOADS’ PROFILES .......................................... 20

3.1 INTRODUCTION .......................................................................................................... 20

3.2 CASE STUDY: OPEN-PLAN OFFICE ............................................................................. 21

3.3 RESULT PRESENTATION ............................................................................................. 23

3.3.1 Occupancy and Plug Load Profiles for the Case Study ...................................................23

3.3.2 Comparison with the Danish Predefined Daily Profiles ..................................................26

3.3.3 Comparison with the Example Case Studies Simulation Profiles ....................................27

3.3.4 Comparison with the Data from the Electric Utility Company ........................................29

3.4 FINDINGS .................................................................................................................... 31

4 STUDY OF INTERNAL LOADS AND THERMAL MASS VARIATIONS ............. 33

4.1 INTRODUCTION .......................................................................................................... 33

4.2 BUILDING LEVEL ANALYSIS ...................................................................................... 34

4.3 ROOM LEVEL ANALYSIS ............................................................................................ 36

4.4 FINDINGS .................................................................................................................... 39

5 CONCLUSION ................................................................................................................. 41

6 GUIDE ON MODELLING OFFICE BUILDINGS ...................................................... 43

6.1 GENERAL FOR MODEL ZONING ................................................................................... 43

6.2 SYSTEMS .................................................................................................................... 43

6.3 THERMAL ZONING METHODS ..................................................................................... 44

6.4 EQUIPMENT LOADS AND OCCUPANCY ........................................................................ 44

VIII

APPENDICES:

7 APPENDIX: THERMAL ZONING ................................................................................... 45

8 APPENDIX: LITERATURE REVIEW: OFFICE INTERNAL LOADS’ PROFILES ...... 97

9 APPENDIX: MEASUREMENT CAMPAIGN: OPEN-PLAN OFFICE INTERNAL LOADS’

PROFILES ................................................................................................................................. 110

10 APPENDIX: STUDY OF INTERNAL LOADS AND THERMAL MASS VARIATIONS138

11 REFERENCES................................................................................................................ 145

IX

LIST OF ABBREVIATIONS AND ACRONYMS

AAU Aalborg University

ACH Air Change Rate per Hour

ACR Air Change Rate

ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers

BES Building Energy Simulations

BI-SBi Branchevejledning for Indeklimaberegninger SBi

BLE Bluetooth Low Energy

BPS Building Performance Simulations

BR Building Regulations

CCTV Closed-Circuit Television

DIGIE Danish Industry Guide for Indoor Environmental Calculations

DRY Design Reference Year

DS/EN Danish Standards, translated in English

EUI Energy Use Intensity

GHG Greenhouse Gas

GPS Global Position Systems

HVAC Heating, Ventilation and Air Conditioning

IE Indoor Environment

ISO International Organization for Standardization

IW Internal Walls

IWC Internal Walls with Cooling

K Kelvin

LCD Liquid Crystal Display

LEED Leadership in Energy and Environmental Design

MB Megabyte

NIW No Internal Walls

X

NIWC No Internal Walls with Cooling

PAK ‘Pakhusene’ building

PDF Portable Document Format

PIR/ IR Passive/ Active Infrared Tracking;

PPL Plug and Process Load

REF Reference

RFID Radio-frequency identification

RGB Red-Green-Blue

RH Relative Humidity

SP Setpoint

TLS Typical Load Shapes

VAV Variable Air Volume

Chapter 1: Introduction

January 2019 1

1 INTRODUCTION

This chapter presents the thesis content by forming the basis of the research problem formulated

on an analysis of the challenges regarding building performance simulations. It introduces the

investigation background, research questions and structure of the thesis. The scope of this

project is formed by its focus areas and restrictive conditions. Last, but not least, to facilitate

the understanding of the project material, its structure is introduced via a flowchart.

1.1 Background

By accounting for roughly 40% of the global energy use, the buildings have large direct and

indirect impacts on the environment. The greenhouse gas (GHG) emission reduction potential

in the building sector can be achieved at lower cost than other sectors of the economy, therefore

improved building energy performance is a crucial subject of various energy policies

worldwide. To achieve sustainable expectations for energy services and real reductions in their

consumption, actual use of buildings needs to be included in policies, as peoples’ everyday

practices in buildings are entangled with its energy consumption.[2]

Building performance simulations (BPS) are used for the evaluation of the design options, their

performance and compliance with legislative codes. The design opportunities are greatest in the

early design stages, where engineers face a challenge to support good, building performance

improving decisions. Currently, BPS is an evaluative tool, the design flow exchange between

engineers and architects is iterative and time-consuming due to highly detailed models and their

deterministic inputs. Nowadays the common practice mostly utilises a single simulation or

parametric variation approaches, where a specific, minimum and maximum values for a chosen

Office Building Simulations: Zoning and Internal Loads

2 Anda Senberga, Liena Krastina, Vilija Matuleviciute

design input is evaluated. [3] To perform a BPS, the modeller needs to zone the building, define

its resolution and input parameters, e.g. occupancy and equipment schedules and profiles.

There are several stages of model resolution, and the three major dimensions are: temporal,

spatial and occupancy. Temporal resolution refers to the timing precision of events modelled.

Spatial resolution refers to physical scale precision, e.g., whether the model predicts a number

of people in a building or zone. Occupancy resolution refers to means of how the model

specifies people, e.g. models or sensors that may specify whether at least one occupant is

present vs. others including the activity that the occupant is engaged in [4].

1.2 Problem formulation

This paper has three main problem formulations:

1. This project seeks more insight into the different, inconsistent and subjective portioning of

thermal zones when assessing building performance. Two office buildings in Denmark are used

in this research.

The goal of this study is to investigate the differences between the thermal zoning strategies

practised in Denmark and the international approach’s impact on the indoor environment in

office buildings in relation to:

o the overheating hours;

o energy consumption for the heating and cooling systems; and sizing the heating

and cooling systems based on their peak loads.

2. It is often required of building to perform well with the predefined standard scenarios without

considerations to the future occupants and their work style. Such assumptions may be an over-

or underestimation and can lead to unnecessary construction costs and poor building

performance. Thus, the industry could benefit from insight into actual internal loads (and its

variance) and how to improve the assumptions related to predefined profiles and schedules.

The problem formulation of this study is to investigate the accuracy of the predefined building

occupancy and equipment schedules.

3. The objective of the third study is to analyse the internal loads' variation impact on different

zoning strategies’ proposals. This will comprise the following sub-objectives:

o Observation of the thermal mass variations, the standby and weekend equipment

load variations on a building level;


January 2019 3

o The change of internal load profiles, the peak loads, and their impact on the

different zoning strategies on a room level.

1.3 Methodology

The thesis is divided into five chapters. A flowchart, Figure 1-1, visualizes the organisation of

the thesis: Chapter1, Introduction, presents the background on the paper and defines the specific

goals to be addressed.

Figure 1-1 The organisation of the thesis

Chapter 2, Thermal Zoning Impact on BPS Model Results, investigates the influence of various

zonal configuration methodologies on heating and cooling demand. In order to fulfil the

objective, dynamic simulations are performed to examine the impact of different thermal zoning

strategies applied to the case building models. It analyses the consequences of simulating

designated areas vs the whole building simulation, compares the variations between single and

multi-zone models and reports the complexity and time input for the latter. Simulations are

performed for two case office buildings located in Denmark.

Chapter 3, Open-Plan Office Internal Loads’ Profiles, compares the simulation results for

typically used predefined internal load profile inputs with ones established based on the

measured occupancy and plug load data. It is based on the registered internal load patterns and

energy use by several workspaces in an open-plan office for a month. Occupancy profile data

was collected by several image-based occupancy sensors. Energy usage was collected by

registering plug load at a workstation level. The measurement campaign was performed for

office space in Aarhus, Denmark.


Chapter 2: Thermal Zoning Impact on

BPS Model Results

Chapter 3: Open-Plan Office Internal

Loads’ Profiles

Chapter 4: Study of Internal Load and

Thermal Mass Variations

Chapter 5: Conclusion



Chapter 4, Study of Internal Loads and Thermal Mass Variations, investigates performance

metrics for alternative internal loads on specific zoning strategies and their impact on the

predicted heating and cooling demand. Based on the findings in previous chapters, a chosen

zoning strategy and altered internal load profiles are applied to multiple dynamic simulations.

It investigates the influence of various parameter variations in the same case study buildings.

Chapter 5, Conclusion, discusses the results of the zonal simulations, the office internal load

measurements and offers recommendations for dynamic office building BPS modelling.

1.4 Limitations

Few limitations are affecting the results of the study, and they are as follows:

• There are two study cases received for the thermal zoning analysis. They have similar

internal layout and space types. It cannot be assumed that the study can be representative

of any type/shape of the building.

• Office’s occupancy measurements are performed in a part of the consulting engineers’

company’s occupied space. The metered open plan office is only a quarter of the

company. Therefore, the complete assessment of the company cannot be provided. The

user load measurement can only account for the specified office space itself.

• Equipment availability resulted in a smaller sampling for the study, and the results may

not be applicable to all open plan offices but can be used under certain conditions.

• For reasons of brevity, the 2 case studies for thermal zoning are used, and more zoning

strategies are performed instead. Due to its location, only one open plan office is chosen

for the internal loads’ measurements. The data collection was limited to approximately

one month, contributing to the fact that the results might not be applicable for the whole

year.

Chapter 2: Thermal Zoning Impact on BPS Model Results

January 2019 5

2 THERMAL ZONING IMPACT ON

BPS MODEL RESULTS

This chapter covers the analysis of different thermal zoning strategies and its impact on the

predicted/simulated indoor environment and annual heating and cooling demand in the two case

office buildings. The analysis is based on the Building Performance Simulations (BPS) of the

case studies.

2.1 Introduction

‘A major goal of any simulation is to take something that is extremely complex (a building) and to

model it as simply as possible yet as accurately as necessary. One of the most critical is the zoning of

the building. The more complex the building, the more important this step becomes.’ [5]

The first step to describe the building in the building simulation model is to zone the building,

keeping in mind that zone represents 1) a mass of air the heat balance is performed upon, and

2) surfaces, scheduled loads and their controls that provide energy/heat transfer between the

zones. [5]

The general concepts behind the different thermal zoning methods for BES modelling consider

the number of zones being proportional to the energy model complexity. The aggregation of

the rooms into zones has a significant role in the energy consumption and the overheating hours'

prediction, as the energy models’ zones are an abstract space and are not limited by the

enclosure or continuity of the building space or within the floor.[6]

Based on the Danish Industry Guide for Indoor Environmental Calculations (DIGIE) [7], the

Danish practice is to select the number of critical rooms for dynamic simulations dependant on

the building. The guide states that in buildings with large variations identifying is challenging,

and the choice of amount of the critical rooms should be reasonable in relation to the floor area.

It refers to the Performance Description for Construction and Landscape guide [8] for a detailed

description of the standard analysis. However, this document states that it is BR15 that defines

the simulation. DIGIE suggests that a building should not be dimensioned based on the maximal

loads as this often may create a poor indoor environment in areas where it is not occurring.

Furthermore, it states that whole building simulations are rarely used due to:



• Complex input data,

• Risk of errors due to lack of quality input data.

Based on the Danish Building Regulations 2015, the DIGIE [7] and the international standard

ISO 13790:2006 [9], the thermal zoning can be executed as follows:

• Simulation of the ‘Critical’ room acc. to BR15 Ch. 6.2 (2)) – Danish practice of a single

zone modelling [10] [7];

• Whole building as a single zone if the defined criteria are met [9];

• Building divided into several zones (multi-zoning) without thermal coupling between

the zones if the defined criteria are met [9]. (Appendix 7.1)

2.2 Literature Research: Previous Studies

Bleil De Souza et al. [11] study of a speculative office building, noted that the simulation

settings should not affect the heating and cooling demand extent, but only the distribution of it.

The study suggested three zoning strategies - a ‘single zone’, ‘5-zone’ and ‘an office in use’

model, and two approaches of internal gain and ventilation rates are set up - ‘speculative1’ and

‘existing layout2’. The results obtained suggested that the ‘5-zone’ model underestimated the

heating demand and overestimated the cooling demand much more than a ‘single zone’ model

when compared to the building-in-use model, at the same time, the total annual demands being

close to the building-in-use model. The ‘speculative’ settings produced a consistent range of

total annual demands while the ‘existing’ case study layout stayed in the middle of this range.

[11]

Rivalin et al. [12] studied the different zoning impact on annual energy consumption and

concluded that it is acceptable to make simplifications, including the grouping of rooms on

different floors in one thermal zone. A single -zone model overestimated the heating and

cooling demand by 6% and 11% respectively, whereas all the simplified models provided

similar results within the margin of ±1% compared to the 49-zone model that is the closest to

be to the actual building in use. The study suggested that the single zone model can be used for

1 ‘Speculative’ settings - the extreme literature settings (minimum and maximum).

2 ‘Existing’ settings - the standard literature settings by the different activities in the office.


January 2019 7

a quick assessment of a building energy consumption while having the same temperature

setpoints in all the spaces is acceptable but not very accurate. [12]

The approach of zone multiplier in buildings with repetitive floor plans was used by Ellis et al.

[13] when simulating the high rise building and showed a small offset in the annual heating and

cooling demand. Kim et al. investigated the energy use after applying three zoning strategies –

zone by room, a whole floor as a single zone, and zoning based on the solar gains [14]. They

concluded that the simplification of the models for the multi-story buildings is feasible.

Bres et al. [15] reported 2 studies of 5 different apartment buildings, where one representative

floor per building was simulated with a purpose to evaluate the impact of the variations in

simulation zoning and HVAC zoning in comparison to the simulation results of the one-zone-

per-room model. The authors suggested that the aggregating rooms with different usage may

lead to more significant errors, when grouping conditioned and non-conditioned rooms. The

room-based zoning simulations pointed out to be the most accurate, while the zoning, based on

the function, led to most unacceptably high underheating. [15]

Refer to Appendix 7.2 for more detailed information about the findings in previous studies

about the different zoning impact.

2.3 Case studies

For this paper, the two office buildings, further referred to as Alfa and Beta, were chosen as the

case studies to examine if and how the different thermal zoning strategies can influence the

BPS. They are recently built office buildings, located in Kolding and Aarhus, Denmark,

respectively.

Both buildings are similar in the layout planning with open space offices taking the most of the

area space. Alfa is a two-story office building with heavy external and light internal

constructions, mainly consisting of open space offices around the perimeter, meeting rooms and

other shared spaces located in the core, and a big canteen area on the ground floor. The Beta

building is a 10-story office building with heavy, both external and internal constructions, with

a repetitive floor plan layout, consisting of open space offices and meeting rooms around the

perimeter, and other rooms/common spaces located in the core. Besides, the ground floor plan

consists of fitness centre facilities and a big canteen area.

It was decided to continue to work further with the 1st-floor plan layouts as it represents the

typical room type distribution of an office building, shown in Figure 2-1 and Figure 2-2. In Alfa

it is the top floor, whereas in Beta it is an internal floor level.



Figure 2-1 Alfa case study 1st-floor layout

Figure 2-2 Beta case study 1st-floor layout

2.3.1 Thermal Zoning Methodology and Complexity of Models

Bres et al. [15] in their study paper of investigating the impact of the zoning in BPS talked about

zoning the building in the perimeter/core, functional, orientational and orientational-functional

zoning in comparison to the zone-per-room zoning. Kaplan et al. [5] and Lisa et al. [12]

mentioned five different criteria to determine the thermal zones:

• usage,

• type of controls,

• solar gains - orientation,

• perimeter or interior location,

• and the HVAC system type. [5]

The approach for both Alfa and Beta case buildings consists of 5 phases, which combine 8

different BPS models. All zoning strategies have the internal layout walls. Also, the three

models are created with no internal walls in the same thermal zone for the 5, 2 and 1-zone

models. The zonal configurations of the two case studies are as follow:


January 2019 9

• 1-zone model – the whole building is considered a uniform single thermal zone;

• 2-zone model – the building is split into two thermal zones consisting of the perimeter

and the core;

• 5-zone model – core is one zone; open offices split between the two E and W

orientations; central hallway area is merged into one zone;

• 12-zone model – the building is split into 12 zones, based on the usage and orientation

– core rooms are merged by type and usage, and the open space offices are split between

the South and North orientations;

• 14-zone model - building-in-use model – the building is divided into multiple thermal

zones depending on the purpose and orientation of each room. Few rooms in the core

are merged based on the usage and system setpoints. All offices are split between the

four compass orientations. This model is furthermore referred to as the baseline model;

• ’Critical’ room model – few ‘critical’ rooms are chosen based on the occupancy

patterns, HVAC system distribution, orientation and heat gains, and each is treated as a

single zone model. This model is furthermore referred to as the reference model.

The BSim model complexity is defined by estimating the work used to create the simulation

models for Alfa (Table 2-1) and Beta (Table 2-2). The ‘Geometry setup time’ includes the

planning of the thermal zones in the building and modelling it in the BSim. This also includes

everything that relates to setting the constructions and defining the construction layers’

properties. For the 1-,2- and 5-zone models the geometry set up time differs for models without

(left column) and with (right column) the internal walls.

The ‘Setup time’ describes the estimated time that it takes to manually input all systems’

characteristic loads, controls and schedules from scratch, as well as the quality check of all the

inputs, and their aggregation into different zoning strategies. The work included the collection

of the input data for system inputs from both the reference models and the standards where the

properties were not specified in the reference models. To minimise the risk of errors during the

model setup process, the coarser zoning models were aggregated from the most complex – the

14-zone baseline model.



Models Geometry setup time* Setup time* Computational file size Computational time3

‘Critical’ room

2 h 0,5 h 2,84 MB 4 s

14-zone

5 ¼ h 4 h 34,6 MB 145 s

12-zone

5 h 3,25 h 29,7 MB 139 s

5-zone

1 ha 3 hb 1,5 h 12,6 MB 127 s

2-zone

1 ha 3 hb 0,5 h 5,29 MB 120 s

1-zone

0,5 ha 3 hb 0,25 h 2,84 MB 104 s

*estimated time; a model without internal walls; b model with internal walls

Table 2-1 Alfa: BSim model complexity

Models Geometry setup time* Setup time* Computational file size Computational time [s]

‘Critical room’

2,5 h 0,5 h 2,84 MB 12

14-zone

6,5 h 6 h 34,6 MB 124 s

12-zone

5,5 h 4 h 29,7 MB 124 s

5-zone

1 ha 3 hb 2 h 12,6 MB 114 s

2-zone

1 ha 3 hb 0,5 h 5,29 MB 108 s

1-zone

0,5 ha 3 hb 0,5 h 2,84 MB 99 s

*estimated time; a model without internal walls; b model with internal walls

Table 2-2 Beta: BSim model complexity

For more information about the zoning phases and BSim models refer to Appendix 7.3 Zonal

Configurations.

3 Computational time depends on the computer type and its system properties. For this study a computer with these

system properties were used: Windows 10, 64-bit; CPU: Intel Core i5 7th Gen, 2,50 GHz; RAM: 12 GB.


January 2019 11

2.3.2 Internal Loads and Systems

This section covers the general information about the internal loads and the systems present in

both case buildings. More detailed information about the system loads and their schedules can

be found in Appendix 7.4 BSim Input Data.

System Alfa Beta

People 0,1 kW/person; 7-18 from Mon-Fri

Equipment + Lighting 0,15 kW/workstation; during occupancy +

standby mode on weekends, nights

0,11 kW/ workstation; during

occupancy

Heating during occupancy + 1h before all year

Winter 21°C / Summer 22°C Day 22°C / Night 19°C

Infiltration ACR 0,14 Specified by room type

Ventilation Comfort / Night

Table 2-3 Summaries of the system inputs for Alfa and Beta case

There is no mechanical cooling present in either of the buildings. Later in the study, it was

introduced with the purpose to investigate the cooling load capacity for the different zonal

configuration models.

The open space offices and the meeting rooms inputs were known from the reference simulation

models. The day profile for the lounge/ kitchenette, the toilets and the other rooms were created

by using the standard inputs. First, the 14-zone model was modelled with all systems for each

thermal zone. The lower resolution models’ inputs were created by gradually connecting the

rooms into courser zoning. For example, for the ‘People Load’, the total number of people was

summed up, and a new occupancy profile was expressed in percentage based on the new peak

load in the grouped thermal zone.

The slight uncertainty in the BSim software was detected during the day profile input process

for people and equipment in percentage as the software allows to input only a whole number.

As for the 5-zone model, Perimeter North, the daily error results in 0,6 kWh lower electricity

usage for one workday or weekend, resulting in 218,4 kWh yearly. For more information, see

Appendix 7.4.4 Uncertainty Analysis.

The 14-zone baseline building models were created by keeping a set of parameters as constant

and having them as a boundary to the reference models of ‘critical’ room. The parameters and

the models' validation can be found in Appendix 7.5 Baseline Building Models Validation.

2.4 Results and Discussion

The most considerable part of the total energy consumption in the building is consumed by its

HVAC system that regulates the indoor environment [16], which is the primary quality in the

user’s satisfaction [17] [18]. The thermal comfort in the offices is connected to the cooling



demand that increases its usage in the commercial sector [19]. Lightweight constructions cannot

dissipate the heat, leading to higher risk of overheating [20], whereas the heavier constructions

succeed in stabilising the indoor temperature and reduces the cooling need [21]–[24].

To examine the influence of simplifications related to the altered layout or simulation of chosen

single rooms, HVAC system distribution and internal loads, a parametric study of single and

several multi-zone models was performed for Alfa and Beta buildings, and the results were

compared to the reference building models. The change of strategy, in theory, is supposed to

affect the distribution but not the magnitude of the overall demand for heating or cooling. Thus,

all models should deliver similar results due to the isolation of potential uncertainties. Therefore

the influence of the thermal zoning strategy on the overheating hours and energy consumption

used for heating and cooling were investigated by BPS analysis.[11]

To differentiate the impact of the different zoning strategies applied to the two case buildings,

the annual heating and cooling demand was chosen as the dependent variables of all simulation

runs performed with BSim. The objective of this study was to analyse the overheating risk

associated with the different thermal mass variation as well as its impact on the annual heating

and cooling demand considering different zoning strategies.

2.4.1 Heating, cooling demand and overheating risk

The influence of the thermal mass variations in the zoning models where analysed regarding

the energy use for heating and cooling of the thermal zoning models for Alfa and Beta case

building. In Alfa, the internal partitions are made of lightweight gypsum walls and internal

glazing. Whereas in Beta the internal walls are composed of lightweight concrete. This is

important to remember as the heating demand is influenced by the thermal mass present in the

zone. Since the people and equipment load that includes lighting is consistent throughout the

different zoning strategies in both cases, then the change in both, heating and cooling demand

can be observed. This is investigated in all models with applied mechanical cooling in relation

to the baseline model of each case study.

Alfa building 14-zone model with the total floor area of 942,7 m² has a total heating and cooling

demand of 2147 and 8096 kWh/year, respectively. For Beta 14-zone model, with 848,7 m² the

total heating and cooling demand are 5573 and 3690 kWh/year, respectively. The energy

demand in different zoning models for both cases is expressed in kWh/m²/year.

The Graph 2-1 a), shows the heating demand in Alfa simulation models, where the baseline

model resulted in 2,4 kWh/m² for heating demand, which, compared to the Beta case study


January 2019 13

baseline model, (Graph 2-2 a)), with heating demand of 6,7 kWh/m², is more than two times

lower. The Alfa 1- and the 2-zone models without the internal walls result in a similar heating

demand with only a 6% difference in between due to extra thermal mass in the 2-zone model

increasing the heating demand. The more complex the zoning strategy becomes, the more

thermal mass is present in the models, decreasing the heating demand proportionally. When

Alfa building, modelled as 1-zone including the internal walls, resulted in 52% higher heating

demand than the baseline model. The 2-zone model with internal walls, resulted in less heating

need than the 1-zone model due to the core located rooms being merged therefore minimising

the total heating need. The 12-zone model results in 1% lower heating demand than the baseline

model due to the little differences between the two zoning strategies. It can be concluded that

for Alfa case, the coarser the zoning, the higher the offset in heating need from the baseline

model.

For more detailed information refer to Appendix 7.6.1 Heating, Cooling Demand and

Overheating Risk.

a) heating b) cooling

Graph 2-1 Alfa:The difference in the heating and the cooling demand in kWh/m²/year in different

zoning models compared to the baseline (14-zone) model

a) heating b) cooling

Graph 2-2 Beta: The difference in the heating and the cooling demand in kWh/m² in different zoning

models compared to the baseline (14-zone) model

46%

-52%-26%

-46%

2%

-17%-1%

-100%

-60%

-20%

20%

60%

1-I

WC

1-N

IWC

2-I

WC

2-N

IWC

5-IW

C

5-N

IWC

12

-IW

C

Baseline 2,4 kWh/m²/year

-37% -41%

-14% -19% -16% -22%

13%

-100%

-60%

-20%

20%

60%

1-I

WC

1-N

IWC

2-I

WC

2-N

IWC

5-I

WC

5-N

IWC

12

-IW

C


8%

-25%

57%

-26%

59%

-26%

6%

-100%

-60%

-20%

20%

60%

1-IW

C

1-N

IWC

2-IW

C

2-N

IWC

5-IW

C

5-N

IWC

12-I

WC

B-I

WC

Baseline 6,7 kWh/m²/year -74%

-56%-67% -62%

-94% -93%

-10%

-100%

-60%

-20%

20%

60%

1-IW

C

1-N

IWC

2-IW

C

2-N

IWC

5-IW

C

5-N

IWC

12-I

WC




The abbreviations used in the graphs above and furthermore in this report to describe the BPS

models thermal mass configuration and the application of the mechanical cooling:

o IW – Internal Walls;

o IWC – Internal Walls with Cooling;

o NIW – No Internal Walls;

o 1, 2, 5, 12 – stands for the zoning phase describing the number of thermal zones in it;

o B – abbreviates the most complex 14-zone models, referred to as the ‘Baseline’ models.

In the Beta BPS 1-,2-, and 5-zone models, without the internal walls, it was observed that the

heating demand, compared to the baseline model, was lower by 25-26%, that can be based on

the amount of the thermal mass in the thermal zones (Graph 2-2 a)). The heating setpoints for

the whole building are the same in all zoning strategies, but the heating power is different based

on the heating needed to reach the defined setpoint within the thermal zone. The 1-zone model

with internal walls resulted in 8% overheating compared to the baseline model. The 2-zone and

5-zone models resulted in 57% and 59% respectively higher heating need than the baseline

model.

Also, as shown in Graph 2-1, b), the energy needed for mechanical cooling for Alfa to keep the

overheating hours below the maximum allowed above 26°C and 27°C during the occupancy

hours, is two times higher than in the Beta baseline model (Graph 2-2 b)). This is clear evidence

of the thermal mass importance and influence in the building simulation models.

Graph 2-3 Alfa: Total overheating hours for different zoning models before and after applying

mechanical cooling and the cooling energy needed to reduce the overheating

164 171324 311

1597 15761713 1703

66 60 131 119

13921370

1486 1469

0 0 0 071 64 75 71

0 0 0 0 16 14 39 39

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1-IW 1-NIW 2-IW 2-NIW 5-IW 5-NIW 12-IW B-IW

Co

oli

ng

dem

and

[kW

h/m

²/y

ear]

Ov

erhea

tin

g ho

urs

>26°C >27°C >26°C(Cool) >27°C(Cool) qCooling


January 2019 15

Graph 2-4 Beta: Total overheating hours for different zoning models before and after applying

mechanical cooling, and the cooling load to reduce the overheating risk

The relation between the heating and cooling demand and the reduction of the overheating hours

is analysed in this section. In both cases, the overheating hours increase with the increase of the

model complexity. In Alfa building, as mentioned before, the internal thermal mass is smaller.

Therefore it is more sensitive to the temperature fluctuations, increasing the risk of overheating.

As shown in Graph 2-3 the overheating hours go over 1500 in the 5-, 12-zone and baseline

model resulting in the vast increase of the need for cooling, which in this case is much higher

than the heating need itself, see Graph 2-1. The opposite happens in Beta building models,

Graph 2-4. The cooling need is lower than the heating need due to higher thermal mass being

able to absorb the heat produced from external and internal factors resulting in overall lower

overheating hours in the more complex zoning models. Thus being above the maximum allowed

overheating hours in all models but the baseline model.

In Alfa 12-zone and baseline model, (Graph 2-5 a)) 4 and 14 hours above 27°C are detected in

the same thermal zones ’10 Other E’ and ’13 Lounge/Kitch. E’, respectively.

The Danish practice of evaluating indoor thermal comfort by choosing a few ‘critical’ rooms

was compared with the respective rooms in the baseline model. The reference rooms (REF)

chosen as critical rooms in Alfa building resulted in lower overheating hours than the same

rooms designed in the baseline BPS model (Graph 2-5 a)). After the mechanical cooling system

was applied, the overheat hours dropped to 0 for the reference rooms and their equivalent rooms

in the baseline model, with an exception for the small meeting rooms that resulted in 26 hours

above the 26°C (Graph 2-5 b)).

160

43 52

387 391

9411075

3 0 10 17

163 156

572

872

0 0 0 0 0 030

113

0 0 0 0 0 0 8 81

0

2

4

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8

10

0

500

1000

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2000

1-IW 1-NIW 2-IW 2-NIW 5-IW 5-NIW 12-IW B-IW

Co

olin

g d

eman

d

[kW

h/m

²/y

ear]

Ov

erh

eati

ng

ho

urs

>26°C >27°C >26°C(Cool) >27°C(Cool) qCooling



a)

b)

Graph 2-5 Alfa: overheating hours within the individual thermal zone in a 14-zone model in relation

to the respective reference ‘critical’ room, a) no cooling system and b) with a cooling system. REF –

stands for the reference model ‘critical’ rooms.

Overall, the Beta model had lower overheating compared to Alfa due to the applied lower

people and equipment loads. After the application of the mechanical cooling, the baseline model

in Beta still resulted in 13 and 56 overheating hours above the maximum limit for 26°C and

27°C, respectively. The risk of overheating was detected in the ‘Small Meeting rooms SW’

(Graph 2-6 a)).

In the case of Beta, (Graph 2-6 b)) all reference ‘critical rooms’ resulted in lower overheating

hours but one – ‘Offices SE’, where it had higher overheating risk than the same room in the

baseline model.

a)

0 1 0148

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1800

Off

ices

SW

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urs >26°C >27°C

REF >26°C REF >27°C

0

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ices

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urs >26°C >27°C


0 45

901

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Off

ices

W 1

Off

ices

N 2

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ices

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ices

SE

4

Meeti

ng

r. S

5

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all

m. r

. SW

6

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ng

r. W

7

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all

m. r.

C.

8

Kit

ch. S

9

Toi

lets

C.

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S 1

1

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er N

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C. 1

3

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urs >26°C >27°C



January 2019 17

b)

Graph 2-6 Beta: overheating hours within the individual thermal zone in a 14-zone model in relation

to the respective reference ‘critical’ room, a) no cooling system and b) with a cooling system

2.4.2 Peak load for heating and cooling

This section reports how zoning of the building influenced the peak heating and cooling loads

in both case buildings.

Finding the peak loads for heating and cooling is essential while sizing the HVAC system in

the early design stages. The oversizing of the system increases the initial and the operational

costs and produce a huge energy waste, while under-sizing of the system will affect the quality

and comfort of the indoor environment. The amount of the heat lost from the building to the

outdoors at the design indoor and outdoor conditions is represented by the peak heating load

that the HVAC system must compensate to maintain the desired indoor climate for the

occupants. Whereas the peak cooling load tells the amount of the heat gained by the building

from both internal and external environment at the design conditions that need to be removed

by the HVAC system to maintain the same occupant comfort.

The peak load per zoning model was found considering hourly heating and cooling demand for

each thermal zone throughout the year. From each Alfa and Beta models, the highest thermal

zone demand was chosen, and the peak load per m² was expressed. The heating load between

the models with and without cooling system resulted in very close demand, and it was decided

to continue with the models including the mechanical cooling system.

Alfa simulation models’ results are shown in Graph 2-7. For Beta case, BPS models, the peak

load for heating and cooling thermal zone demand is shown in Graph 2-8. In both study cases,

the REF1 and the REF2 represent the open plan offices, and their peak loads are similar. The

rest REF models are the two types of meeting rooms with varying peak loads. Between the

multi-zoning models, the tendency was observed in the baseline and 12-zone models to have

32

1517

0

30

60

90

120

Off

ices

W 1

Off

ices

N 2

Off

ices

E 3

Off

ices

SE

4

Mee

ting

r. S

5

Sm

all

m. r.

SW

6

Meeti

ng

r. W

7

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all

m. r

. C.

8

Kit

ch. S

9

Toil

ets

C.

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Oth

er

S 1

1

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N 1

2

Oth

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C. 1

3

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C. 1

4

Ov

erh

eati

ng

ho

urs >26°C >27°C




the highest heating and cooling demand and to decrease towards the whole building as a single

zone model (1-IW and 1-NIW).

Graph 2-7 Alfa: Hourly peak demand for heating and cooling load for different zoning models

Graph 2-8 Beta: Hourly peak demand for heating and cooling load for different zoning models

When comparing 5-zone, 2-zone and 1-zone models with (IW) and without (NIW) internal

thermal zones’ layout, a different trend was observed. In the Alfa case models, both heating and

cooling hourly peak demands were slightly higher in the IWC models than in the NIWC model

cases. The Beta case study models resulted in higher heating demand in 5-IWC, 2-NIWC and

1-IWC while cooling peaks stayed almost the same.

Refer to Appendix 7.6.2 for the full hourly peak load for heating and cooling for all Alfa and

Beta models for each thermal zone.

2.5 Findings

This chapter presented a study on the influence of the thermal zoning strategies on the annual

heating demand and thermal comfort in the two case office buildings in Denmark. The

simulations of the two cases, Alfa and Beta, have shown that the annual heating demand is very

dependent on the complexity of the BPS models.

The two case buildings with different internal thermal mass characteristics have proven the

theory of its importance to be considered in the early design phase of the buildings. The ability

to absorb and release heat controls the thermal comfort in the building. According to the study

results, Alfa baseline model with light thermal mass needs roughly 65% less heating per m²

than Beta baseline model with heavier thermal mass. Even though Beta has a heavy thermal

mass, there is no standby equipment load, resulting in the heating need still being high. From

0,04 0,04 0,04

0,24

0,10 0,10

0,05 0,04 0,04 0,030,04

0,030,01 0,02

0,00

0,07

0,18 0,18

0,09 0,09

0,07 0,07 0,07 0,07

0,00

0,05

0,10

0,15

0,20

0,25

REF 1 REF 2 REF 3 REF 4 Baseline 12-IWC 5-IWC 5-NIWC 2-IWC 2-NIWC 1-IWC 1-NIWC

Pea

k l

oad

[k

Wh

/m²] qheating

qcooling

0,04 0,040,07

0,20

0,10

0,17 0,16

0,11

0,050,03 0,04

0,02 0,020,03 0,03

0,10

0,27

0,01

0,35

0,17

0,06 0,06 0,06 0,06 0,06 0,06

0,0

0,1

0,2

0,3

0,4

REF 1 REF 2 REF 3 REF 4 REF 5 Baseline 12-IWC 5-IWC 5-NIWC 2-IWC 2-NIWC 1-IWC 1-NIWC

Pea

k l

oad

[k

Wh

/m²] qheating

qcooling


January 2019 19

the other hand, to minimise the overheating risk, Beta resulted in 50% lower cooling demand

than Alfa.

Alfa 1-, 2- and 5- zone models without modelled internal walls resulted in heating demand

lower than in the baseline model, where it decreased with the increase of the zoning complexity.

However, in Beta case, the zoning models that included the design of the internal walls resulted

in 8%, 57% and 59% higher heating demand than the baseline model, where the 1-zone model

was with the smallest offset. Respectively the 1-, 2- and 5- zone models without the modelled

internal walls resulted in 25-26% lower heating demand than the baseline model.

The overheating risk has been evaluated within all BPS models for both case studies. Moreover,

the tendency of overheating hours increasing with the increase of the complexity of the zoning

strategies is present in both cases.

It is worth mentioning that the comparison between the Danish approach of performing building

simulations on few ‘critical’ rooms and the international approach of different zoning strategies,

without mechanical cooling, resulted in lower overheating risk. If both cases were to be

combined, then 8 out of 9 ‘critical’ rooms compared to the same room in the respective baseline

model, resulted in lower overheating hours. Although, another type of rooms in the baseline

model resulted in much higher overheating risk than the reference rooms. The choice of

evaluating the thermal comfort in the ‘critical’ rooms, being offices and meeting rooms, is

considered reasonable.



3 OPEN-PLAN OFFICE

INTERNAL LOADS’ PROFILES

This chapter is structured as follows: background about the importance of the internal load

profiles, presentation of the case study, the process of the measurement campaign and data

collection, and comparison between typical predefined profiles and actual occupancy and

workstation loads.

3.1 Introduction

When performing the BPS for a building project at its design phase, there are a lot of

uncertainties concerning the selection of parameters. After the simulation model is zoned, the

inputs for the internal gains, including the occupancy and plug load and their profiles, are

critical to model heating and cooling loads. Hong and Lin researched the behaviour influence

on energy savings by the work styles in a single story office building resulting in up to 50%

less energy used with the energy-saving work style and up to 90% more with an energy-wasteful

work style [25]. The standard-based assumptions can overestimate or underestimate the energy

needed, resulting in improperly sized HVAC equipment. [4], [26]–[29]

Figure 3-1 A comparison between the predicted energy consumption modelled during the design

phase and the actual measured energy consumption for Leadership in Energy and Environmental

Design (LEED) certified buildings in the United States. EUI - Energy Use Intensity [30]

Yan et al. [30] presented Figure 3-1,it shows that even though there is over and underprediction,

the models are quite good estimators for energy use. The occupancy patterns are mostly

designed as static schedules, that is not accurately representing their active and passive

Chapter 3: Open-Plan Office Internal Loads’ Profiles

January 2019 21

behaviour. Meng et al. [31] and De Wilde [32] support buildings’ occupants being one of the

principal factors for the performance gap, affecting both, occupancy and plug load profiles. To

improve the results of BPS, the possible discrepancies between the predefined profiles and

estimated schedules and their loads should be analysed.

The chapter was organised into three main parts, corresponding to the related theory,

measurement campaign and comparison with the existing internal loads’ profiles. First, the

effect of occupancy and equipment load profiles was analysed by the literature research

described above. Second, the measurements of the case study office’s occupancy and plug loads

were processed to develop the patterns of the observed loads. Third, this paper compared the

on-site collected data to the predefined schedules as well as the data from the electric utility

company.

In order to fulfil this chapter’ objective, the following research steps were formulated:

o Extraction of field measurements of office space for different day types

(workday and weekend) and internal load profiles’ creation with determined

peak load;

o Investigation of the discrepancies between the Danish standard predefined

people and plug load profiles vs. on-field metered data of occupancy and plug

load;

o Comparison of people and plug load profiles with the on-field metered data and

case studies (Alfa and Beta) simulation inputs;

o Seasonal variation assessment of the data from the electric utility company.

3.2 Case Study: Open-Plan Office

The chosen case study of open plan office comprised 16 functional workstations with one user

each resulting in 8,9 m²/occupant for 16 users. The Danish industry guidelines BI-SBi [7]

recommend 6 to 10 m²/person whereas the European design criteria for the indoor environment

suggests 14 m²/person for the landscaped office [33]. The occupants of this specific office space

are employees of the consultancy company MOE within Buildings, Energy & Industry and

Infrastructure. The example of the case study office room can be seen in Figure 3-2. The regular

workstation set up was a laptop and its docking station, 1 or 2 energy efficient LCD monitors

and few miscellaneous power items (desk lamp, phone or headphone charger, etc.). This space

was selected due to the representative behaviour of an open space office, according to the

employee’s statements. The occupants mostly had stationary tasks, with some away-from-desk



duties. (see Appendix 9.1.1 Case Study - Assessment and Selection for a more detailed

description of the case study office).

Figure 3-2 Chosen office space of the ‘Pakhusene’ building for the case study

The method used for metering the occupancy in the landscape office was chosen to be the image

recording technique with blurred pictures. The three web-cameras with remote access were

placed at the ceiling level. The view range and areas are visualised in Figure 3-3. The cameras

were mounted vertically to reduce the occurrence of occlusions, as well as it lessened the chance

for recognition of individual occupants [34] [35].

Figure 3-3 View area of the three cameras of the field measurements in the chosen multiple person

office

Plug load measurement was performed for 8 randomly selected workstations. The metering

equipment was placed on the floor with cables running to and along the ceiling, creating no

disturbance for occupants without changing their usual work habits.


January 2019 23

The main characteristics of the occupancy sensing and plug metering are gathered in Table 3-1.

Occupancy Counting Plug Loads

Used for MOE internal loads’ profiles

Start of the monitoring period 23.10.2018 31.10.2018

End of the monitoring period 02.12.2018 02.12.2018

Working days 29 23

Weekend days 12 10

Main characteristics

Techniques Web-Cameras Energy Metering System

Placement Ceiling Ceiling / Workstation

Recording Interval 5 min 1 min

Table 3-1 Overview of monitoring period for office internal loads measurements at MOE for

occupancy counting and plug metering

For more information, see Appendices 8.2.1 Methods for Detecting Occupancy Presence and

Count and 8.2.2 Plug Load Metering related to the literature research about the different sensing

techniques, and Appendix 9.1.2 Data Collection Strategy for more detailed information about

the MOE case study measurement campaign.

3.3 Result Presentation

The hourly internal loads’ profiles for occupancy and plug loads were created from the observed

open plan office. These profiles can be used in the building simulation programs such as BSim.

The workday and weekend usage can be calculated by multiplying the calculated hourly usage

by the peak or maximum usage determined.

3.3.1 Occupancy and Plug Load Profiles for the Case Study

The final occupancy profile derived from the MOE’s metering campaign, shown in Graph 3-1.

The percentages indicate the fraction of people being present in the office after averaging the

measured workdays. The peak load of 16 occupants was chosen as the maximum of 16

workstations were used at the time of measurements. No office employees were observed

during the weekends. To be present and the continues work at the workstation were standard

for some of the users. The users could be described mostly as sedentary type, working at their

workstations, with some meetings or out of office tasks. Some employees were attending a

meeting or working from a different location.

For other occupants, as a part of the job, was the meeting attendance and work from a different

location.



Graph 3-1 Obtained hourly occupancy profile from MOE case study

The plug load metering resulted in the profile, shown in Graph 3-2, with the % indicating

varying hourly energy usage for the workday, while the weekend had the standby consumption

of 15%. The graph indicates the morning load is starting at 7 am and reaching its daily usage

from 10 am to 2 pm with the peak usage after the lunch break. The end of the workday steadily

decreases, and at 7 pm the workday standby of 20% starts.

Graph 3-2 Obtained hourly plug load profile from MOE case study

As the plug load metering was performed for the workstation usage, the peak power

consumption for an averaged office stations was determined to be at 34,9 W. If modelling the

office by the different user types, the separate load stations can be modelled with peak loads of

78,5 W for high or intensive usage, 40,6 W for medium or average user and 16,4 W for Low

energy user type.

See Appendix 9.2 Data Processing and Visualization – Occupancy Counting

for detailed information on how the raw measurement campaign data was processed to the

internal loads’ profiles of occupancy and plug loads.

0% 0% 0% 0% 0% 0% 0%

12%

40%45% 48%

38% 39%

49%44%

33%

11%

2% 0% 0% 0% 0% 0% 0%

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Hour

Workday

Weekend

19% 20% 20% 20% 20% 19% 20%

37%

83%

99% 96% 96%100%

93%

83%

71%

41%

24%20% 20% 20% 20% 19% 19%

15%

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40%

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Workday

Weekend


January 2019 25

For higher resolution profiles for 5-min, 15-min, 30-min intervals refer to Appendices:

• 9.2.6 Higher Resolution People Load Profiles

• 9.3.9 Higher Resolution Equipment Load Profiles

3.3.1.1 Occupancy and Equipment Correlation

From the Danish industry guidelines for indoor climate calculations guide “Branchevejledning

for indeklimaberegninger SBi” (later referred as BI-SBi) [7], the patterns for internal loads of

occupancy and equipment usage were modelled similarly. The comparison was made with the

measured data to see if the case study office space correlated in between the occupancy and the

plug loads. Graph 3-3 shows both profiles plotted together in %. There is an evident correlation

between the occupancy and the plug loads, especially during the arrival and departure periods.

The occupancy during the day was detected lower due to the lunch hour, but the equipment

usage had not changed much from 10 am to 2 pm.

Graph 3-3 Occupancy and plug load hourly profiles for a workday, in %

The workday occupancy count of 5-min interval was plotted together with the plug load use in

the same resolution. Graph 3-4 illustrates the correlation between the averaged office

attendance and the averaged power usage for a workday.

Graph 3-4 Occupancy and plug load 5-min profiles for a workday

20%

40%

60%

80%

100%

0%

10%

20%

30%

40%

50%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Plu

g L

oad

Pro

file

Occu

pan

cy

Pro

file

Hour

Occupancy

Plug load

1 9

17

25

33

41

49

57

65

73

81

89

97

10

5

11

3

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1

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9

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En

erg

y U

Se [

Wh

]

Peo

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Lo

ad

[N

o]

Hour

Occupancy

Plug load

24



3.3.2 Comparison with the Danish Predefined Daily Profiles

The open plan office profiles from the Danish Industry guidelines for indoor climate

calculations [7]4 were compared to the measured occupancy and equipment data. The metered

office had no user load during the weekend, contrary to the receptacle usage. Graph 3-5 shows

the plotted profiles, with the predefined guide, plotted with the three usage levels: ‘High,

‘Regular’, and ‘Low’. The observed office resulted in obviously lower usage of the space

(Graph 3-5 a)). The occupancy peak load was set to a maximum of 16 users as mentioned

before, and the created MOE’s profile increased as high as 50% with a lunch break around 40%.

The ‘Low’ schedule from BI-SBi guide was modelled at 70 to 30% respectively.

a) Occupancy

b) Equipment

Graph 3-5 Comparison of open plan office internal loads’ profiles between the standard predefined

profiles from “Danish industry guidelines for indoor climate calculations” to the field evidence,

workday

On Graph 3-5 b), the equipment profiles are demonstrated. The averaged peak load from MOE’s

measurements was determined to be 34,9 W for the office level, while the different peaks could

4 Refer to Appendix 8.1 Published Predefined Internal Load Schedules for more information about the Danish as

well as international guidelines

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

BI-SBi High BI-SBi Regular BI-SBi Low MOE

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

HourBI-SBi High BI-SBi Regular BI-SBi Low MOE


January 2019 27

be used for the station level resolution. The Danish guidelines would suggest the peak load for

a workstation of at least 80 W. The fluctuations of the predefined plug load profile for a

workday are static from 9 am to 4 pm, while MOE’s profile has the highest consumption

between 10 am and 2pm.

Table 3-2 shows the occupancy daily hourly sum for a workday for the metered office and the

Danish guidelines. By using the same peak load of 16 people, the created MOE’s profile

resulted in a much lower total daily presence. The suggested equipment peak load of 80 W led

to higher results even with BI-SBi Low profile.

Occupancy Plug Load

Peak load

[No]

Total

[No]

Peak load

[W]

Workday

[Wh]

Weekend

[Wh]

MOE

16

58 34,9 376 126

BI-SBi H 153

80

720 -

BI-SBi R 122 576 -

BI-SBi L 104 496 -

Table 3-2 Occupancy and equipment usage daily sum from the hourly profiles for the MOE case study

and the Danish guidelines (BI-SBi)

Please, see Appendix 8.1.3 about the internal loads’ profiles comparison between the actual and

predefined schedules.

3.3.3 Comparison with the Example Case Studies Simulation Profiles

The two case studies Alfa and Beta (from Chapter 2 Thermal Zoning Impact on BPS Model

Results) were used to compare their internal loads' profiles, used in the simulation models, to

the created MOE profiles. The Alfa case occupancy profiles have longer work hours, and it is

assumed that 100% of office users are present every day, with no exceptions (Graph 3-6). The

Beta case building was simulated at 85% during the work hours and 25% during lunch. The

MOE campaign data has much lower profiles.

Graph 3-6 Comparison of open plan office occupancy profiles between the Alfa and Beta case studies

simulation profiles to the field evidence, workday

It can be seen on Graph 3-7 that the Alfa case plug load is simulated at 100% during workhours

from 8 am to 5 pm with the standby usage set at 26%. There is no lunch break included for plug

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Alfa Beta MOE



usage. The Beta case profiles have a similar pattern to their occupancy profiles, except the first

work hour is assumed not to have the equipment usage. There is also the summer schedule used

for the Beta simulations, with the plug usage decreasing from 85% to 50%. MOE’s profile

follows the Alfa case profiles, with a slower rise in the morning, the evening usage is gradually

decreasing from 1 pm to 7 pm and the night load is slightly lower - at 19%.

Graph 3-7 Comparison of open plan office equipment profiles between the Alfa and Beta case

studies simulation profiles to the field evidence, workday

The weekend plug load profiles are demonstrated on Graph 3-8. Both Alfa and MOE have

steady usage throughout the day at 26% and 15% respectively. The Beta simulation model

excluded weekend usage throughout the year.

Graph 3-8 Comparison of open plan office equipment profiles between the Alfa and Beta case

studies simulation profiles to the field evidence, weekend

For a clearer comparison, the daily office occupancy and energy usage for a workstation were

calculated for the Alfa, Beta and MOE profiles. Table 3-3 demonstrates that by Alfa building

usage accounting for constant occupants’ attendance and electricity usage it results in the

highest daily office usage. The Beta case open-plan office was designed by using lower profiles

and decreased equipment peak load. The plug load usage resulted in being lower by almost 3

times for the workday. Nevertheless, the MOE case occupancy presence was detected to be two

times lower than the Beta simulation models, and the same was noted for the daily equipment

usage.

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Alfa Beta Beta (Summer) MOE

0%

50%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Alfa (Weekend) MOE (Weekend)


January 2019 29

Occupancy Plug Load

Peak load

[No]

Total

[No]

Peak load

[W]

Workday

[Wh]

Weekend

[Wh]

MOE 16 58 34,9 376 126

Alfa

18

207 150 2046 936

Beta 136 110

737 -

Beta (Summer)

136 440 -

Table 3-3 Occupancy and equipment usage daily sum from the hourly profiles for the MOE, Alfa and

Beta case studies

3.3.4 Comparison with the Data from the Electric Utility Company

The seasonal variation was inspected from the yearly energy consumption. The data received

included the total consumption of plug loads and lighting for two building storeys, both divided

into half floor sections. Figure 3-4 demonstrates the floor layouts to be mostly office space at

the perimeter, and core mostly including other non-occupied rooms. The four data sets were

named as PAK1, PAK2, PAK3 and PAK4.

Figure 3-4 The ‘Pakhusene’ building 1st (left) and 2nd (right) floor plans

Graph 3-9 shows the four sets of the annual energy consumption with regards to their average

usage. The trend of energy consumption increasing slowly throughout the year is observed in

PAK 1 and PAK 4, and remaining around the same usage level in PAK 2 and PAK 3, with the

drop during July, the vacation period. However, no seasonal change in energy usage between

winter and summer was observed. See Appendix 9.3.10 Data from the Electric Utility

Company for the separate PAK data sets yearly plots.

Graph 3-9 Yearly consumption of the "Pakhusene" building four data sets

0

500

1.000

1.500

2.000

Feb Mar Apr May Jun Jul Aug Sep Oct Nov

En

ergy

Use

[k

Wh]

PAK 1 PAK 2 PAK 3 PAK 4 PAK Average



The daily energy consumption was analysed for the same period as the MOE on-site plug load

metering. The 2018 data from the 1st February until 2nd December inclusive was averaged for

the four NRGi data sets. The separate energy usage profiles were created by finding each PAK

set peak value and expressing the hourly usage into percentages. This was investigated to

observe the pattern of the workdays and the weekends.

Graph 3-10 Hourly workday profile of the "Pakhusene" building four data sets and the metered

campaign data

Graph 3-10 displays the data for the workdays. It was unknown to which PAK data set the MOE

measurement campaign office belongs. Nevertheless, the profiles between the metered and

NRGi registered electricity consumption are closely similar. The PAK data had earlier start and

later stop time for equipment usage. The standby energy consumption was observed to be

around 20% for MOE’s case, while PAK sets were from 26% up to as high as 49%. The

difference can be caused by the MOE data including only the plug loads from workstations,

while the PAK sets had the consumption from all office spaces, e.g. general lighting and

equipment in the printer rooms.

Graph 3-11 Hourly weekend profile of the "Pakhusene" building four data sets and the metered

campaign data

Graph 3-11 displays the plug load profiles for the weekend. The four PAK data sets have a soft

peak before lunchtime, while the metered data stays stable during the day. The registered

weekend plug use in the MOE office is constant at 15%, while the total floor registered

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

PAK 1 PAK 2 PAK 3 PAK 4 MOE

0%

25%

50%

75%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

PAK 1 PAK 2 PAK 3 PAK 4 MOE


January 2019 31

consumption varied from 22% to 46% at standby use, and the usage during the daytime was

noted as well.

The vacation period was noted during the Easter period in March/April, and summer holidays

in July. The profiles were created and the comparison to the PAK weekend usage revealed the

similarity in a daily pattern. Some office spaces had few occupants coming in, with minimal

usage, others had an exactly same daily profile as for the weekend, while one data set indicated

occupants were turning off their equipment thus lowering the standby loads. (See Appendix

9.3.10.1 Daily Consumption Comparison for the Metered Period for the vacation period

compared with the weekend profile.)

Similar daily electricity usage patterns were observed throughout the year 2018. As the daily

consumption is similar, there is no seasonal change observed until now, and MOE data could

be extrapolated for the whole year. However, the holiday period must be adjusted as calendar

holidays, employee vacations, and other non-typical events.

3.4 Findings

In this paper, a typical open plan office was approached for obtaining its occupancy pattern and

equipment energy use. The data collection for the MOE case study started on 23rd October for

user counting, and the plug load metering began on 31st October. The last day of the official

measuring campaign was the 2nd December 2018. The people load and averaged workday

equipment use analysis was performed for the office level, in addition to the plug loads being

categorised to the workstation level (High, Medium, Low) by the amount of the energy usage.

The developed internal loads’ profiles revealed the high correlation of the workday pattern for

occupant’s presence and receptacle load usage. The occupancy profile resulted in up to 50%

average attendance, with a lunch break between 12 to 13 o’clock. The weekend measurements

exposed the energy use as a standby mode, slightly higher than at nights during the workday.

The profiles were created for an hourly schedule to be used in BPS models, but the higher

resolution was calculated for 5-min, 15-min and 30-min intervals.

The next step was the investigation of the Danish predefined internal loads’ profiles vs. on-field

metered data. The data in Graph 3-5 exposed the overestimation of occupancy for the MOE

study case if using the guideline’s suggestions. Moreover, by using the specified appliance

power to calculate the workstation’s load resulted in a higher total daily and averaged

consumption when compared to the created MOE plug load averaged office profile.



The internal loads’ profiles from the MOE case revealed the decrease in hourly input for the

occupancy compared to the Alfa and Beta simulation models’ inputs. While the Alfa profiles

were created with 100% hourly usage for most of the office hours, the standby night use was

designed as the weekend usage at 26%. The Beta building had one occupancy profile, with the

varying equipment usage for summer and the rest of the year. However, there was no standby

usage created. To conclude, the MOE office internal loads’ measurements resulted in lower

office usage than simulations predicted.

The data from the electric utility company (NRGi) was investigated to assess if there was the

seasonal variation between the equipment usage. As the building was occupied from February

2018, less than a year of data was available at the time. The first half of the year had similar

consumption (except the vacation periods in April and July). From August, the monthly

consumption was slowly increasing towards the end for the year, though the three out of four

PAK data sets had slightly lower usage in November compared to October. The seasonal

variation was too early to detect for the MOE office. Ideally, future studies would be

recommended to examine the longer building operation period data. In the meantime, the

separate hourly workday profiles were created for NRGi data. After plotting these PAK profiles

together with the MOE metered data, similar patterns were observed, even though the PAK data

has earlier start and later end for the equipment usage. The standby load was observed lower in

the MOE profiles.

Chapter 4: Study of Internal Loads and Thermal Mass Variations

January 2019 33

4 STUDY OF INTERNAL LOADS

AND THERMAL MASS

VARIATIONS

This chapter is organized as follows: a basic overview of the investigations performed,

continued with the chosen zoning configurations of the building and alternating the internal

loads for occupancy and equipment, and thermal mass variations. This part contains analyses

for building and room level simulation models.

4.1 Introduction

This chapter reports the variation in equipment load, and heating and cooling demand results in

Alfa and Beta 14-zone models with cooling for the whole building and its thermal zones for

two parameter changes - equipment load and thermal mass. In order to fulfil the aim, the

following is simulated:

1. At the building level:

a. the thermal mass was varied from light internal constructions to heavy, and the

other way around;

b. the equipment load schedules were simulated with standby and a weekend load

and without;

2. At the room level:

c. The people and equipment load profiles discovered during the measurement

campaign were applied to the open space offices.

There was already defined equipment load during the standby and weekend hours in Alfa case,

while day profiles in Beta did not consider any. These loads affect internal heat gains in the

building; therefore the equipment day profiles were altered to add or remove standby/weekend

hours and influence on energy use investigated. The internal thermal mass variation was altered

to both - light and heavy building construction. These changes were applied to all thermal zones

and constructions in both case study buildings.



The investigation on a room level reports the change in the heating and cooling demand after

the application of the internal load profiles, resulted from the measurement campaign, to Alfa

and Beta 12- and 14-zone BPS models.

To evaluate the impact of the thermal mass, and the equipment load variations in the building

level BPS model results, four sets of BSim simulations were established, as shown in Table

4-1. The boundaries like occupancy profiles and equipment peak loads were kept for both cases.

This study considered the 14-zone BPS models.

Equipment Internal thermal mass

SET 1 Standby/weekend Light

SET 2 Standby/weekend Heavy

SET 3 No standby Light

SET 4 No standby Heavy

Table 4-1 Building level: BSim simulation sets

To evaluate the change in the heating and cooling demand on a room level, the internal load

profiles were varied in Alfa and Beta 12- and 14-zone BPS models. Both the people and

equipment load profiles and the equipment peak load per workstation, resulting from the

measurement campaign, were applied to both case studies.

Internal load profiles

SET 1 Design load profiles from the zoning chapter

SET 2 MOE’s load profiles

Table 4-2 Room level: BSim simulation sets

4.2 Building Level Analysis

The altered equipment load profiles in comparison with the designed loads for Alfa and Beta

are shown in Table 4-3. For this investigation in both cases a standby/weekend load of 10%

was applied, unless specified differently in Baseline model inputs. The internal wall

constructions in thermal mass variations were plasterboard drywall and lightweight concrete,

shown in Table 4-4.

Equipment load day profiles Alfa, Beta

Standby Baseline Day Profile + 10% equipment load during unoccupied hours

Non-standby Baseline Day profile - any equipment load during unoccupied hours

Table 4-3 Building level: Equipment load profiles


January 2019 35

Alfa, Light Beta

Light thickness 0,12 m (plasterboard) thickness 0,12 m (plasterboard)

Heavy thickness 0,18 m (concrete) thickness 0,25 m (lightweight concrete)

Table 4-4 Building level: Internal wall constructions5

It was observed that the smallest energy demand for space heating was in variations with heavy

internal constructions and equipment load including standby and weekend mode. The opposite

was observed in the BPS model with light constructions and equipment loads without standby

and weekend mode. It can be attributed to internal heat gains and large thermal mass

contributing to the heating of the building. The SET1 with light internal walls and equipment

loads with standby/weekend mode resulted in higher internal heat gains, therefore, decreased

the heating demand. The biggest drop in heating demand was also observed in SET2 with

standby/weekend mode.

a) Alfa b) Beta

Graph 4-1 Difference in the heating, cooling demand in kWh/m²/year with regards to the altered

plug loads profiles compared to the baseline (14-zone) models

As illustrated in Graph 4-1, SET1 and SET2, a tendency of decreased heating and increased

cooling demand was observed when compared to the Baseline model, which was assumed to

have 100% of the demand. It can be attributed to additional internal heat gains resulting from

the added hours of equipment load. Regarding the equipment load, the additional hours in Beta

case resulted in 32% increase, whereas Alfa case had 2% increase due to the baseline model

already defining a large portion of the standby and weekend hours in most of the thermal zones.

SET1 with light internal constructions decreased heating demand by 7% in Alfa and 24% in

Beta and increased cooling demand by 2% and 16% respectively due to the additional

5 See Appendix 10.1.1 Internal Wall Construction

66%

29%

-7%

-54%

-24% -24%

2% 2%

-24%

-45%

2%

-24%

-80%

-40%

0%

40%

80%

No Stanby,Light (3)

No Standby,Heavy (4)

Standby,Light (1)

Standby,Heavy (2)

Heating Equipment Cooling


19%

0%

-24%-38%

0%

0%

32% 32%

-6%

0%

16%31%

-80%

-40%

0%

40%

80%

No Standby,Light (3)

No Standby,Heavy (4)

Standby,Light (1)

Standby,Heavy (2)

Heating Equipment Cooling




equipment load. SET2 with heavy internal constructions resulted in decreased heating demand

by 54% for Alfa and 38% for Beta due to additional thermal mass storing the energy. The

cooling demand decreased by 24% for Alfa as a result of added mass aiding the cooling, and

for Beta cases increased by 31% due to the added internal heat gains.

Alterations in SET3, non-standby and light internal thermal mass variation, decreased energy

demand for cooling and equipment by 24% for Alfa case. The Beta case resulted in no decrease

for equipment and 6% decrease for cooling. Heating demand increased by 66% in Alfa and

19% in Beta when compared to the Baseline model. Variations in Alfa case were caused mainly

by removing the equipment load for standby and weekends, resulting in smaller internal heat

gains. The Beta case revealed a similar tendency. However, the baseline model had no standby

loads, and the increase in the heating demand could be attributed to the change in thermal mass

only. SET4 with no standby and heavy internal constructions resulted in a 29% increase in

heating and 45% decrease in cooling demands in Alfa case due to the larger thermal mass acting

as a battery for internal heat gains. The Beta case has no change, as this set has identical

parameters with the baseline model.

Refer to Appendix 10.2.1 Energy Demand for the annual heating, cooling and equipment

energy demand in kWh/year for the Alfa and Beta 14-zone models as a whole and every thermal

zone.

4.3 Room Level Analysis

The people load profile, observed at MOE’s office building, in comparison with the designed

loads for both case studies is shown in Graph 4-2 a). The actual people load, expressed as a

percentage, was applied to all thermal zones that included open offices in Alfa and Beta models.

a) People load (with daily peak load) b) Equipment (with the daily peak load)

Graph 4-2 Internal load profiles created at MOE’s and the design profiles of Alfa and Beta from

previous zoning chapter

8

18

15

0

5

10

15

20

1 3 5 7 9 11 13 15 17 19 21 23

Peo

ple

Lo

ad

[N

o]

Hour

MOE (16) Alfa (18) Beta (18)

Standby 7

35Standby 39

150

94

55

0

50

100

150

1 3 5 7 9 11 13 15 17 19 21 23

Plu

g L

oad

[W

/sta

tio

n]

Hour

MOE (34,9) Alfa (150)

Beta - Winter (110) Beta - Summer (110)


January 2019 37

The equipment loads measured at MOE’s (Graph 4-2 in b)), resulted in hourly peak load of 35

W per workstation when Alfa and Beta were designed with an hourly peak load of 150 W and

110 W per workstation, respectively. The designed plug load profiles in Alfa considered 100%

during the occupancy and 26% for standby and weekend loads. The equipment profiles in Beta

had two settings, winter and summer, without considering any standby or weekend mode. For

this investigation, both cases had been applied MOE’s equipment profiles and peak load of 35

W per workstation in all offices.

After MOE’s profiles have been applied to all offices in Alfa 14-zone models, it reduced people

loads by roughly 41% and equipment loads by 49% resulting in an increase of heating demand

by 72%, while the cooling demand decreased by 39% (Graph 4-3 a)). In Beta case, people and

the equipment loads decreased by 42% by 7%, respectively. It resulted in a 22% increase in the

heating demand and 8% increase in the cooling demand (Graph 4-3 b)). The impact on the

individual offices is shown in the following Table 4-5. See Appendix 10.2.1 Energy Demand

for more information, including the 12-zone model results.

a) Alfa b) Beta

Graph 4-3 Total heating and cooling load per m² in the 14-zone models in relation to the change of

the people and equipment load profiles

a) Alfa 14 With zoning chapter load profiles With MOE’s load profiles Part of the original demand* qHeating qCooling qHeating qCooling qHeating qCooling

[kWh/m²/year] [kWh/m²/year]

Offices S1 0,80 8,41 8,09 0,05 ↑ 1014% ↓ 1%

Offices S2 1,03 5,50 8,77 0,01 ↑ 851% ↓ 0,1%

Offices N1 1,29 0,37 11,19 0,00 ↑ 869% ↓ 0%

Offices N2 1,70 0,04 12,74 0,00 ↑ 751% ↓ 0%

b) Beta 14 With zoning chapter load profiles With MOE’s load profiles Part of the original demand* qHeating qCooling qHeating qCooling qHeating qCooling

[kWh/m²/year] [kWh/m²/year]

Offices SW 1,45 0,06 3,67 0,08 ↑ 253% ↑ 139%

Offices N1 2,23 0,00 4,83 0,00 ↑ 217% 0%

Offices N2 1,69 0,00 4,12 0,00 ↑ 244% 0%

Offices E 0,63 1,53 1,97 0,14 ↑ 313% ↓ 9%

Table 4-5 The impact on the heating and cooling loads in kWh/m²/year in all offices in the 14-zone

models in both cases after applying MOE’s internal load profiles. The change is calculated in

percentage taking the designed load profiles as 100%

2,4

↑ 72%8,6

↓ 39%

0

50

100

0

2

4

6

8

10

Alfa 14 MOE 14

Peo

ple

-eq

uip

men

t

load

[k

Wh

/m²/

yea

r]

En

ergy

dem

and

[kW

h/m

²/y

ear]

qHeating qCooling

6,7

↑ 22%

4,3 ↑ 8%

0

50

100

0

2

4

6

8

10

Beta 14 MOE 14

Peo

ple

-eq

uip

men

t

load

[kW

h/m

²/y

ear]

En

ergy

dem

and

[kW

h/m

²/y

ear]

qHeating qCooling



Graph 4-4 shows the decrease in the total overheating hours in Alfa and a slight increase in Beta

models after the reduction of the internal gains from people and equipment. The increase of

overheating risk in Beta is due to a workday being longer by two hours after applying MOE’s

load profile, see Graph 4-2 in b).

a) Alfa b) Beta

Graph 4-4 The total overheating hours in Alfa and Beta models before and after applying MOE’s

measured occupancy and equipment load profiles

In the Alfa models without the mechanical cooling, the overheating hours above 26°C and 27°C

(Graph 4-5 a)) in the south offices with designed load profiles were within limits. Even though

in the 14-zone model the overheating hours dropped below the maximum allowed limit, slight

overheating was still detected. However, after applying MOE’s load profiles, the overheating

hours in the south offices dropped to or nearly to zero. Same was observed in the North facing

offices.

a) Alfa b) Beta

Graph 4-5 Overheating hours in the offices

Due to the change in load profiles, the change in overheating hours in Beta South offices was

not detected. North offices, however, slightly increased the overheating hours, exceeding the

maximum limit by 4 hours above 27°C (Graph 4-5, b)). Refer to Appendix 10.2.2 for the more

detailed overheating hours, including the 12-zone models.

The impact of the peak heating and cooling loads were also analysed in both case studies’

offices for Alfa (Table 4-6) and Beta case (Table 4-7). With MOE’s load profiles, the peak

heating demand in Alfa 14-zone model doubled while peak cooling dropped from 0,04

1703

↓ 17%1469

↓ 19%

71 ↓ 17%39↓ 18%

0

500

1000

1500

2000

Alfa 14 MOE 14

Ov

erh

eati

ng

ho

urs

[-]

>26°C >27°C >26°C(Cool) >27°C(Cool)

1075↑ 18%

872↑ 17%

113 ↓ 18%81↓ 14%

0

500

1000

1500

2000

Beta 14 MOE 14

Over

hea

ting

hours

[-]

>26°C >27°C >26°C(Cool) >27°C(Cool)

352

240

1 00 0 022

4729

0 00 0 0 2

0

100

200

300

400

Offices S1 Offices S2 Offices N1 Offices N2Ov

erh

eati

ng

ho

urs

[-]

14 Alfa >26°C 14 MOE >26°C14 Alfa >27°C 14 MOE >27°C

0 0 0

82

0 0

57

110 0 0 20 029

0

0

100

200

300

400

Offices SW Offices N1 Offices N2 Offices EOv

erh

eati

ng

ho

urs

[-]

14 Beta > 26°C 14 MOE >26°C14 Beta >27°C" 14 MOE >27°C


January 2019 39

kW/m²/year to 0-0,01 kW/m²/year. In Beta 14-zone, the peak heating load increased by 25%.

The peak cooling load in South and North offices remained the same while it decreased by 1/3

in the East offices.

See Appendix 10.2.3 for peak loads in the 12-zone models.

With design load profiles from the zoning chapter With Moe’s load profiles

14-zone model

Thermal Zone Area Heating Cooling Heating Cooling

[m²] [kW/m²/year] [kW/m²/year]

Offices S1 134,2 0,02 0,04 ↑ 0,04 ↓ 0,01

Offices S2 126,7 0,03 0,04 ↑ 0,04 ↓ 0,00

Offices N1 154,0 0,03 0,01 ↑ 0,05 ↓ 0,00

Offices N2 143,8 0,04 0,01 ↑ 0,05 ↓ 0,00

Table 4-6 Alfa: change in peak heating and cooling loads in the offices in the 14-zone model

With design load profiles from the zoning chapter With Moe’s load profiles

14-zone model

Thermal Zone Area Heating Cooling Heating Cooling

[m²] [kW/m²/year] [kW/m²/year]

Offices SW 137,5 0,04 0,01 ↑ 0,05 0,01

Offices N1 104,1 0,04 0,00 ↑ 0,05 0,00

Offices N2 139,7 0,04 0,00 ↑ 0,05 0,00

Offices E 48,2 0,03 0,03 ↑ 0,05 ↓ 0,02

Table 4-7 Beta: change in peak heating and cooling loads in the offices in the 14-zone model

4.4 Findings

After altering the equipment day profiles, schedules and internal wall construction type for a

building level in the baseline model for Alfa and Beta cases, to maintain comfort the heating

demand increased by 66% and 19% when compared to the baseline for SET3, and it resulted in

the highest demand. SET3 in Beta case revealed a 19% increase in heating and 6% drop in

cooling and again had the highest heating demand. Change to a heavy internal mass with added

equipment load in SET4 decreased both - heating and cooling demands, furthermore, it stayed

the same as in the baseline BPS model in Beta case. When compared to the baseline, SET1 with

standby and weekend equipment loads revealed the tendency of decreased heating and

increased cooling demands for both case buildings. Demands for heating dropped by 7% and

24% and cooling increased by 2% and 16% for Alfa and Beta cases respectively. The tendency

was repeating in SET2 results where additional internal mass was added. SET2 resulted in 54%

and 38% decrease in heating demand and decrease of 24% and increase of 31% in cooling

demand for Alfa and Beta cases respectively.

After applying MOE’s people and plug load profiles to all the open plan offices in Alfa and

Beta building models, it decreased the internal loads at the same time increasing the heating



demand and decreasing the cooling need. In Alfa offices, in the 14-zone model, the internal

gains were reduced by 41% (people) and 49% (equipment) and proportionally increased the

total heating demand by 72% to maintain the indoor thermal comfort. The 14-zone model,

without the active mechanical cooling, resulted in the total overheating risk reduction of 17%

(hours >26°C) and 19% (hours >27°C). The peak heating demand doubled while peak cooling

demand dropped by approximately 75%. In Beta 14-zone model, the internal loads decreased

by 42% (people) and 7% (equipment) increasing the heating demand by 22%. The model

resulted in a moderate increase of overheating hours in the North offices. The peak heating load

increased by 25% and 1/3 decrease in the peak cooling load.

Chapter 5: Conclusion

January 2019 41

5 CONCLUSION

The Building Performance Simulations (BPS) models are a relatively quick tool to evaluate the

indoor environment in the buildings and estimate predicted energy demand and to size the

HVAC system in the early stages of the project. It can require extensive work regarding input

data collection, building zoning and modelling considerations. Also, this research has

demonstrated it has a great influence on the BPS result accuracy.

To assess the thermal comfort in the building, the Danish national standards and industry guides

suggest one or more single zone simulations of the rooms representing the most critical

conditions regarding internal and external heat gains. The international standards present an

idea of zoning the building according to several parameters including the function of the space,

internal and external heat gains and HVAC system settings. Considering that the most complex

BPS model is the closest to the actual building-in-use, the other zonal configurations simulation

results were compared to it in the two office buildings’ cases. This study has demonstrated that

the one-zone models, with the internal layout, resulted in an overestimation of the heating

demand by 48% in the Alfa case, that has light internal thermal mass with standby equipment

loads, and 8% in the Beta case, that has heavy thermal mass without standby equipment loads.

Meanwhile, the same study without the internal layout resulted in a considerable

underestimation of the heating demand.

It is clear from the study of the thermal mass and the equipment loads’ variations that heating

and cooling demand decreases with the increase of the thermal mass. It has also demonstrated

that if the equipment loads are designed with the standby and weekend mode, the heating

demand drops. The opposite occurs if the standby mode is neglected, resulting in the

overestimation in the heating demand.

The risk of overheating increases with the increase of the complexity of the BPS models in both

cases. The overheating in the ‘critical rooms’ was detected to be higher than in the respective

rooms designed in the most complex model. Choosing to design a ‘critical room’ could lead to

the oversizing of the HVAC system that would increase the energy waste during the buildings’

operational stage.



Occupant behaviour in the building simulations is often simplified as static schedules based on

the national standards, and their dynamic behaviour is ignored. The investigation of the office

occupant presence and the equipment usage at their workstation level resulted in lower hourly

peak loads than what the industry guide suggests, even though the average daily profile

followed the same pattern. The measurement campaign also revealed that the highest average

user presence in the office was approximately 50% during the day. When the case office space

uses the predefined internal loads' profiles, it results in higher internal heat gains minimising

the heating need. The actual monitored loads being lower increases the heating demand.

More case studies should be examined to test the results’ robustness for the different building

zonal configurations as well as to define the range in the energy use for heating and cooling.

Future work could include indoor environmental parameter measurements and energy use

assessment for the building-in-use, and a comparison of this metered data with zoning strategies

simulated in this thesis. Also, there is a need for monitored data for internal loads’ profiles and

their hourly peak loads in the office building-level and various types of plug loads, e.g. printers,

lounge equipment etc. The occupancy in an office- or a building level should be monitored to

investigate the people load profiles for other types of spaces.

Chapter 6: Guide on Modelling Office Buildings

January 2019 43

6 GUIDE ON MODELLING

OFFICE BUILDINGS

These recommendations are developed to aid the process of performing dynamic building

performance simulation. This guide is based on the research and analysis performed in the thesis

and aim to improve the simulation results’ accuracy and reliability. The recommendations

delimit model physical setup and baseline assumptions, e.g. geometry representation, weather

data, material database etc.

6.1 General for model zoning

• The thermal zoning of a building depends on its purpose and can be executed as:

o Simulation of the ‘Critical’ rooms - a single zone modelling [10] [7];

o Whole building as a single zone (one zone model) [9];

o Building divided into several zones (multi-zoning) [9].

• If you have the resources and time, perform the complex multi-zone simulation.

However, as the complexity increases, so does uncertainty of errors. [7]

• The chosen BPS zoning method and the combination of input parameters and internal

heat gains affect the thermal behaviour of the building. For example, if the internal wall

constructions are not modelled, the heating demand is underestimated. Internal wall

modelling is not an overwhelming task as it seems, as not all the modelled rooms need

to be a separate thermal zone. A single thermal zone model can be modelled with all the

internal constructions, as they have a significant impact on the energy demand results.

6.2 Systems

• The heating system should be running 24/7 with different setpoints rather than turned

on/off, which results in under- or overheating, as the system attempts to reach the

designed setpoints.

• Infiltration rate, as it is a load on a building, should be scheduled, e.g. applied with 100%

performance during the occupancy and lowered for unoccupied/standby/weekend

modes.



6.3 Thermal Zoning methods

• Combining thermally similar zones (function, schedules, orientation) have a significant

impact on overheating risk. Combining larger zones, e.g. offices, underestimates the

overheating hours.

• Critical room method also underestimates the overheating risk as it has a uniform

ambient environment with a fixed room temperature setpoint.

• Whole building modelled as a single zone with internal walls results in overestimated

heating and underestimated cooling demands [36]. However, it may be used for a quick

assessment of the building energy consumption [12] in early stages, and the simulation

must include:

o internal construction layout;

o heating system running full days during the heating season;

o equipment standby/weekend mode.

• The more simplified zoning method, without the internal constructions, the lower the

heating demand, if compared to the most complex model resembling the building-in-

use.

• When BPS models are created, a thorough system input quality assurance has to be

performed: check inputs, outputs, and their influence on the energy performance results.

6.4 Equipment loads and occupancy

• Zone-dependant, occupant activity schedule, has a large impact on the heat gains.

Uniform distribution of the people loads, e.g. in single zone model, results in an

underestimation of the heating demand.

• For modelling the internal load inputs as equipment and people loads - the user type

should be considered with regards to energy saving- or wasting profiles.

• Similar considerations towards the user type - a management type that is rarely at the

workstation uses less of the power than a consulting engineer. It has an impact on the

peak load energy use.

• Systems should be consistent with one another, e.g. occupancy and equipment loads -

with larger occupancy the power use increases and it should be reflected in schedules.

• Standby and weekend equipment loads should be included.

Chapter 7: Appendix: Thermal zoning

January 2019 45

7 APPENDIX: THERMAL ZONING

7 APPENDIX: THERMAL ZONING ............................................................................... 45

7.1 INTERNATIONAL STANDARD ISO 13790:2006 ........................................................... 46

7.2 LITERATURE RESEARCH: PREVIOUS STUDIES ............................................................ 47

7.3 ZONAL CONFIGURATIONS .......................................................................................... 49

7.3.1 Alfa: The Zoning Models ..................................................................................................50

7.3.2 Beta: The Zoning Models .................................................................................................53

7.4 BSIM INPUT DATA ..................................................................................................... 57

7.4.1 Weather Data ...................................................................................................................57

7.4.2 Alfa: BSim Inputs ..............................................................................................................58

7.4.3 Beta: BSim Inputs .............................................................................................................70

7.4.4 Uncertainty Analysis ........................................................................................................82

7.5 BASELINE BUILDING MODELS VALIDATION.............................................................. 82

7.6 PARAMETRIC SIMULATIONS ....................................................................................... 91

7.6.1 Heating, Cooling Demand and Overheating Risk ............................................................91

7.6.2 Hourly Peak Demand for Heating and Cooling Load ......................................................95



7.1 International Standard ISO 13790:2006

ISO 13790:2006 [9] defines several criteria to be met to design the building as a single-zone

model or multi-zone model with or without thermal coupling between the zones:

• Criteria for creating a single zone BES model - to be able to create a single zone model

either for the whole building or the ‘critical’ room, a certain criterion must be met. If

one or more criteria do not apply, a single thermal zone model cannot be used, and the

building must be divided into multiple thermal zones:

o Temperature SP differ max 4 K

o Spaces are either all cooled mechanically or not, and temperature SP do not differ

more than 4 K

o The same heating/cooling system

o Min. 80% of the space is serviced by the same ventilation system

o The air amount supply in all spaces differs no more than a factor of 4 within the 80%

of the total floor area. [1]

• Criteria for creating a multi-zone BES model - there are few criteria to follow to zone a

BES model to provide an accurate representation of the actual systems and at the same

time meet all conditions within the simulation model:

o Usage/function – similar internal loads (people, equipment, lights) and their usage

schedules;

o Temperature control – same heating and cooling temperature SP and the same

thermostat schedules;

o Solar gains: similar solar gains and exposure based on the room orientation, similar

shading strategy; the glazing openings that do not vary more than 45°; unglazed

exterior zones can be combined if the other criteria are met;

o Perimeter or core location – perimeter zones separated from internal zones (based

on different heating and cooling needs in the zones);

o HVAC system distribution – rooms that are served by the same HVAC system can

be combined in the same zone.

The calculation methods of multi-zone modelling:

• without thermal coupling between the zones - each zone is calculation independent

using a single zone procedure and assumes adiabatic boundaries between the zones;

• with thermal coupling between the zones:


January 2019 47

o Different HVAC systems in each zone;

o Purpose, orientation;

o Air transfer properties and direction.

7.2 Literature Research: Previous studies

Bleil De Souza et al. [11] study of a speculative office building notes that the simulation settings

should not affect the heating and cooling demand extent, but only the distribution of it. They

also indicate that the thermal behaviour in the building is affected by the methods of thermal

zoning and the relationship between the different combinations of input parameters and internal

gains, and ventilation rate. The study suggested three zoning strategies - a ‘single zone’, ‘5-

zone’ and ‘an office in use’ model, and two approaches of internal gain and ventilation rates

were set up - ‘speculative’ and ‘existing layout’:

• ‘Speculative’ settings - where the extreme literature settings for internal gains and

ventilation rates are used to define the range between the minimum and maximum loads

of the office;

• ‘Existing’ settings - where standard literature settings for the internal gains and

ventilation rates were used considering the different activities in the office.

The results obtained suggested that the ‘5-zone’ model underestimated the heating demand and

overestimated the cooling demand much more than a ‘single zone’ model when compared to

the building-in-use model, at the same time, the total annual demands being close to the

building-in-use model. The ‘speculative’ settings, produced a consistent range of total annual

demands, while the ‘existing’ case study layout, stayed in the middle of this range. [11]

Rivalin et al. [12] studied the different zoning impact on annual energy consumption and

concluded that it is acceptable to make simplifications, including the grouping of rooms on

different floors in one thermal zone. 5 cases were studied. The complex 49-zone model

considered a common HVAC system, same occupancy profile on each floor, and building

orientation, where the derived simpler models considered a shared HVAC system and/or

grouped floors independent on the occupancy profiles, and orientation. A single -zone model

overestimated the heating and cooling demand by 6% and 11% respectively, whereas all the

simplified models provided similar results within the margin of ±1% compared to the 49-zone

model that was the closest to be to the actual building in use. In the separation of zones, based

on the building orientation, even for the important glazing rates, the difference did not exceed

1%. The difference between the simulation model with airflow transfers between the zones and

a model without it was 1% for cooling demand and no change in the heating demand for the



most complex 49-zone model. Allowing the heat transfer between the zones in the 21-zone

model by removing the floor partitions, resulted in a difference of 3% in heat demand and none

in cooling demand compared to the same model without the heat transfer. The study suggested

that the single zone model can be used for a quick assessment of a building energy consumption

while having the same temperature setpoints in all the spaces is acceptable but not very accurate.

[12]

In buildings with repetitive floor plans, zone multiplier can be applied. This approach was used

by Ellis et al. [13] when simulating a high rise building and shows a small offset in the annual

heating and cooling demand. The case building results in less than 1% error when only one

floor is simulated and multiplied with the total number of floors. The maximum error in the

annual energy demand between any two floors was reported as 7%. [13]

Kim et al. investigated the energy use after applying three zoning strategies – zone by room, a

whole floor as a single zone, and zoning based on the solar gains. Simulations result in similar

load patterns for floors in the middle of the building and more distinctive values for the ground

and top floors. Therefore they suggested that the simplification of the models for the middle

spaces is feasible. [14]

Bres et al. [15] report 2 studies of 5 different apartment buildings, where 1 representative floor

per building is simulated with a purpose to evaluate the impact of the variations in simulation

zoning and HVAC zoning in comparison to the simulation results of the one-zone-per-room

model. The zoning was based on:

• ideal (unknown) HVAC system considering the variations of the internal loads:

o uniform - the same internal loads were simulated in all zones;

o majority - internal loads corresponded the space use with the largest area in

aggregated rooms;

o interpolated - area-weighted from aggregated rooms.

• a known HVAC system - where heating control was not needed, and the temperatures

could deviate from the SP.

The simulations with the ideal (unknown) HVAC system, with the uniform internal loads'

variations, led to the underestimation of the heating loads. When the same internal (uniform)

loads were simulated in all thermal zones, it resulted in the lowest errors, particularly if the

thermal zoning was based on the orientation. The simulations with internal walls corresponding

to the majority of the space use compensated the exterior-dominated deviation assessed with

uniform internal loads, in the interpolated approach - the opposite occurred. The authors also


January 2019 49

suggested that the aggregating rooms with different usage may lead to greater errors, when

grouping conditioned and non-conditioned rooms.

The simulations with defined (known) HVAC system increases underheating with a coarser

HVAC zoning. The room-based zoning simulations pointed out to be the most accurate while

the zoning, based on the function, led to most unacceptably high underheating. [15]

7.3 Zonal Configurations

This chapter describes the different thermal zoning strategies for the case buildings. The

environment in a thermal zone is created by interactions between the building construction, its

thermal loads and the outdoor climate forming a heat balance for the thermal zone. The theory

behind the heat balance is utilised by the simplified BPS models, that consider the dynamic heat

exchanges between the outdoor air, the thermal capacity of building construction, the internal

surfaces, the air in the room and internal thermal loads. [37]

The reference models as ‘Critical rooms’ were received from the collaborating company MOE.

For the Alfa and Beta, their 1st-floor plan was chosen to be modelled. The building geometry

was simplified regarding wall and room alignments. The key geometrical characteristics, such

as the net floor area and volume, the window to floor ratio, orientation etc., were modelled

according to the architectural design drawings. All created zonal phases have the internal layout

constructions designed. Also, the phases three to five also included the secondary zoning

strategy –no internal walls in the same thermal zone, decreasing the model complexity.

Alfa and Beta case studies were modelled similarly, therefore the BSim input data

considerations in detail are explained only for Alfa case.



7.3.1 Alfa: The Zoning Models

7.3.1.1 Alfa: The Reference Models

The reference building models consist of four critical rooms provided by MOE - a small

meeting/ interview room, two landscape office spaces facing North and South, and a core

located meeting room. The rooms are illustrated with the red outline in Figure 7-1.

Figure 7-1 Alfa:1st-floor plan with indicated reference rooms

7.3.1.2 Alfa: Phase 1 – 14-zone model

A ‘Phase 1’ refers to a 14-zone baseline model where the floor was divided into thermal zones

according to the architectural design layout - a separate thermal zone for each of the open plan

offices, while core located meeting rooms, South located small meeting rooms, and another

closely located same type of rooms were joined by function and orientation.

This zoning strategy is illustrated in Figure 7-2, with the top figure showing the zoned floor

plan, with different colours indicating a separate thermal zone. The bottom figure is the BSim

model plan.

Figure 7-2 Alfa:14-zone model geometry


January 2019 51


A ‘Phase 2’ refers to a 12-zone model where the floor was divided into thermal zones similar

to 14-zone model, except the open plan offices were joined by the orientation. This zoning

strategy can be seen in Figure 7-3.


7.3.1.4 Alfa: Phase 3 – 5-zone model Orientation

A ‘Phase 3’ refers to a 5-zone model where the core of the building was constructed as one

zone, and remaining spaces were joined into four separate thermal zones dependant on their

principal orientation. The orientational zoning still considered the existing layout, as the rooms

were not divided to separate spaces, but they were assigned to the most fitting thermal zones.

The 5-zone model strategy is shown in Figure 7-4. The bottom figures are demonstrating the

two BSim models created: no internal layout on the left, and the actual layout on the right side.





A ‘Phase 4’ refers to a 2-zone model where the core remained identical to the previous phase,

and the remaining spaces were joined into a separate thermal zone regardless of their principal

orientation.

The 2-zone model strategy is shown in Figure 7-5. Same as the phase 3 model, two BSim

designs are presented. The bottom left figure has few internal walls modelled in the thermal

zone called ‘1 Perimeter’ due to the simulation programs’ limitations and restrictions. They

were designed as the air walls – a virtual partition, as openings throughout the whole wall area.



A ‘Phase 5’ refers to a model where all the offices, meeting and other spaces were joined into

a single, uniform zone.

The 1-zone model zoning is demonstrated in Figure 7-6, with the same idea as the 5 and 2-zone

models, by having two internal layout strategies for the BSim simulations.



January 2019 53

7.3.2 Beta: The Zoning Models

7.3.2.1 Beta: The Reference Models

The reference building models consist of five critical rooms provided by MOE - two landscape

offices facing NW/NE and SW/SE, and three meeting rooms facing SW, SW/NE and SE-NE.

The rooms are illustrated with the red outline in Figure 7-7, where the orientation of the building

in the architectural design drawings is 225°.

Figure 7-7 Beta:1st-floor plan with indicated reference rooms

The models received represent rooms on various floors; therefore the test-runs with adjusting

the height of the ‘critical room’ models were performed. The change in the heating and cooling

demand resulted in an insignificant difference. Therefore it was assumed that the parameter

inputs were valid to be used in further phases.

7.3.2.2 Beta: Phase 1 – 14-zone model

The ‘Phase 1’ refers to a 14-zone baseline model and can be seen in Figure 7-8.



Figure 7-8 Beta:14 - zone model geometry


The ‘Phase 4’ model of 12-zones is demonstrated in Figure 7-9. The rooms ‘2 Offices N’ and

‘3 Offices E’ were joined into one thermal zone. The meeting rooms with numbers’5’ and ‘6’

were assigned to one thermal zone as well.


January 2019 55

Figure 7-9 Beta:12 - zone model geometry


The 5-zone model from ‘Phase 3’ is illustrated in Figure 7-10. There are several air-walls

modelled in the so-called no internal layout BSim model due to the limitations of the software.

Figure 7-10 Beta:5-zone model geometry0




Figure 7-11 illustrates the ‘Phase 4’ of 2-zone model.

Figure 7-11 Beta:2-zone model geometry

7.3.2.6 Beta: Phase 5 – 1 zone model

A single or 1-zone model from ‘Phase 5’ is shown in Figure 7-12.


January 2019 57

Figure 7-12 Beta:1- zone model geometry

7.4 BSim Input Data

The information and the data needed to run the dynamic simulations in BSim is collected in this

chapter for the two case office buildings. This chapter describes the systems and the input

parameters used in the Alfa and Beta models.

7.4.1 Weather Data

The reference building simulations have been performed using the Design Reference Year

(DRY) 2013 weather data as the mandatory weather data to be used for all simulations in

Denmark. DRY13 is also used in all the BSim simulations performed during this study.



7.4.2 Alfa: BSim Inputs

7.4.2.1 Database

Construction U-Value G-Value Lt

[W/m²K] [-] [-]

Window 0,52 1,55 0,37 0,45

Skylight 1 1,55 0,52 0,68

Table 7-1 Alfa: Windows properties

Layer Thickness Conductivity Resistance

[m] [W/mK] [m²K/W]

Rse 0,04

Brick 0,108 0,68 0,16

Rock wool 31 0,19 0,031 6,129

Concrete 0,18 2,1 0,086

Rsi 0,13

Total 0,478 6,55

U- value 0,15 W/m²K

Table 7-2 Alfa: Exterior wall construction


[m] [W/mK] [m²K/W]

Plasterboard 0,025 0,2 0,125

Cavity 0,07/0,095 - -

Plasterboard 0,025 0,2 0,125

Total 120/145 0,25


Table 7-3 Alfa: Internal wall construction


[m] [W/mK] [m²K/W]

Linoleum 0,01 0,2 0,05

Reinforced concrete 0,47 2,1 0,22

Total 0,48 0,27

U- value 3,7 W/m²K

Table 7-4 Alfa: Floor partition construction


[m] [W/mK] [m²K/W]

Rse 0,04

Expanded polystyrene 0,3 0,045 6,67


Linoleum 0,01 0,2 0,05

Rsi 0,17

Total 0,45 7


Table 7-5 Alfa: Ground deck construction


[m] [W/mK] [m²K/W]

Rse 0,04

Cork tiles 0,04 0,045 0,889

Concrete 0,4 1,5 0,26

Glass wool insulation 37 0,3 0,037 8,11

Rsi 0,10

Total 0,74 9,297


Table 7-6 Alfa: Roof construction


January 2019 59

7.4.2.2 Systems

The provided BSim models with its defined input data of the ‘critical’ rooms are used as a

reference for the case study to have the consistency throughout the simulation models used in

this paper. The baseline model (14-zone) inputs are assigned first, and the coarser models are

using the aggregated values.

7.4.2.2.1 ‘People Load’

‘People load’ is describing the convective and radiative heat and moisture production from

occupants in the current thermal zone. The number of people varies in the zone over time and

is described by ‘Day Profile’ as a percentage of the nominal or maximum number in an hour.

The people load number was assumed by the PDF drawings with furnishing layout received

together with the ‘Critical room’ models.

Table 7-7 demonstrates the different ‘Day Profile’ inputs for the Alfa building room types for

all zoning strategies. The open space offices and the meeting rooms are known from the

reference simulation models with a full occupancy load for the working hours 7 am to 6 pm

(meaning work starts at 6 am and finishes at 6 pm and 50% during the lunch break). The day

profile for kitchenette is 100% during the lunch hours from 12 am to 1 pm and 5% during the

rest of the office working hours. The toilets have a constant people load of 20% throughout the

office hours. The other rooms have increased loads during lunch time.

The heat load from the people is 0,1 kW as defined in the BSim program.



14-zone 5-zone 2-zone 1-zone

Th

erm

al

zon

e

Off

ices

SW

Off

ices

SE

Off

ices

NW

Off

ices

NE

Mee

tin

g

roo

ms

C.

Sm

all

mee

tin

g r

. S

Oth

er N

Oth

er W

Oth

er W

Oth

er E

Oth

er C

.

Lo

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c

h.

N

Lo

un

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Kit

c

h.

E

To

ilet

s C

.

Per

imet

er S

Per

imet

er N

Per

imet

er E

Per

imet

er W

Co

re

Per

imet

er

Co

re

Wh

ole

bu

ild

ing

1

2

3

4

5

6

7

8

9

10

11

12

13

14

1

2

3

4

5

1

2

1

Load 20 18 23 21 24 4 0 0 12 12 4 18 18 6 43,4 44,9 27 12 27,78 107,2 27,8 135

Hour % % % % % % % % % % % % % % % % % % % % % %

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

8 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

9 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

10 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

11 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

12 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

13 50 50 50 50 50 50 0 0 50 75 50 100 100 20 50 89 26 89 52 88 52 81

14 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

15 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

16 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

17 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

18 100 100 100 100 100 100 0 0 100 50 100 5 5 20 100 100 26 100 100 100 100 100

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 7-7 Alfa: 'People load' inputs for the 14, 5, 2 and 1 zone models


January 2019 61

7.4.2.2.2 ‘Equipment Load’

‘Equipment’ system describes the internal heat production from equipment, and appliances in

the thermal zone and the variation over time. In BSim, the electrical lighting loads can be

modelled as a separate system, though, to control the loads throughout the models, the lighting

loads are included in the ‘Equipment’ system.

The heat load from the equipment for offices and meeting rooms is given in kW per workstation

from the reference simulation models. The heat load in Lounge - Kitchenette is assumed based

on the average equipment heat loads provided by a producer and calculated per m² [38]. In the

toilets, an average heat load for commonly used hand dryers is applied, one in the accessible

toilet and one in the other toilets [38]. The general lighting is set for the workdays only, and for

the other type of rooms, not defined in the reference models, the open office schedule is applied.

Table 7-8 shows the combined load of equipment and lighting for the 14-zone model rooms,

later aggregated to the courser models.

14-zone

Th

erm

al z

on

e

Off

ices

SW

Off

ices

SE

Off

ices

NW

Off

ices

NE

Mee

tin

g r

oo

ms

C.

Sm

all

mee

tin

g r

. S

Oth

er N

Oth

er W

Oth

er W

Oth

er E

Oth

er C

.

Lo

un

ge/

Kit

ch.

N

Lo

un

ge/

Kit

ch.

E

To

ilet

s C

.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Plug Load 2,70 2,55 3,10 2,89 1,20 0,20 0,00 0,00 0,50 0,50 2,00 2,01 1,14 4,60

Lighting Load 0,83 0,78 0,95 0,88 0,5 0,18 0,23 0,23 0,23 0,18 0,52 0,28 0,16 0,23

Table 7-8 Alfa: Plug and lighting load in kW for 14-zone model

The ‘Part to Air’ value between 0 and 1 specifies the amount of the heat emission to the room

air by convection, while the rest accounts for the radiative heat to the surfaces of the room. [39]

The offices and meeting rooms have a fixed value based on the reference model, while all the

other type of spaces has been assigned the same value as the meeting rooms.

Table 7-9 shows the different thermal zones hourly equipment load profiles for the work days,

including the out of the office hours and night hours, further referred to as ‘Standby’ mode

equipment loads as shown in the table mentioned above. The weekend load profiles for the

room type have the same constant load as standby load. The open offices’ ‘Day Profile’

schedule has a standby heat delivery of 20%, the kitchenette – lounge - 4%, the toilets – 5%,

and the other rooms are set to 3-4% for the 14-zone model.

The hourly equipment load for the 5-zone model is calculated in the same way as the people

loads, where all the rooms’ equipment loads based on the orientation and loads of the core



located rooms are grouped and summed up. The final profile for the thermal zone is then

expressed in the percentage based on the zones’ new peak load.

The 2-zone model includes the sum of the perimeter and the core located rooms’ equipment

loads and is expressed in percentage based on the peak load in the thermal zone. The 1-zone

model considers the sum of all the equipment loads in the building. Moreover, the load profile

is expressed in percentage based on the whole building’s daily peak load.


January 2019 63

14-zone 5-zone 2-zone 1-zone

Th

erm

al

zon

e

Off

ices

SW

Off

ices

SE

Off

ices

NW

Off

ices

NE

Mee

tin

g

roo

ms

C.

Sm

all

mee

tin

g r

. S

Oth

er N

Oth

er W

Oth

er W

Oth

er E

Oth

er C

.

Lo

un

ge/

Kit

ch.

N

Lo

un

ge/

Kit

ch.

E

To

ilet

s C

.

Per

imet

er S

Per

imet

er N

Per

imet

er E

Per

imet

er W

Co

re

Per

imet

er

Co

re

Wh

ole

bu

ild

ing

1

2

3

4

5

6

7

8

9

10

11

12

13

14

1

2

3

4

5

1

2

1

Load 3,53 3,33 4,05 3,77 1,70 0,38 0,23 0,23 0,73 0,68 2,52 2,29 1,30 4,60 7,57 10,21 1,58 0,46 3,44 19,72 3,44 22,56

Hour % % % % % % % % % % % % % % % % % % % % % %

1 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

2 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

3 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

4 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

5 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

6 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

7 43 43 43 43 29 47 100 100 35 30 25 17 17 10 45 38 27 84 39 41 39 42

8 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

9 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

10 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

11 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

12 100 100 100 100 100 100 100 100 45 68 37 21 21 24 100 82 46 100 100 87 100 91

13 100 100 100 100 65 74 100 100 45 41 37 100 100 24 99 100 100 100 83 100 83 100

14 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

15 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

16 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

17 100 100 100 100 100 100 100 100 45 41 37 21 21 24 100 82 35 100 100 86 100 91

18 100 100 100 100 29 47 100 100 45 41 37 21 21 24 97 82 35 100 65 85 65 84

19 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

20 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

21 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

22 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

23 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

24 20 20 20 20 0 0 0 0 3 4 4 4 4 5 19 16 5 5 9 16 9 15

Table 7-9 Alfa: ‘Equipment load' inputs for the 14, 5, 2 and 1 zone models for a workday



7.4.2.2.3 ‘Heating’

This system describes the operation of the radiators and convectors in the building and is

expressed in the energy needed to reach the desired temperature setpoint, see Table 7-10. Based

on the reference model input data, the heating season is in operation all year around. The

temperature setpoint for the winter season is 21°C and 22°C in the summer season. In the

offices and the other common spaces, the heating system starts operating one hour before the

occupancy time. In the meeting rooms, and the toilets and other purpose rooms, the heating

system operates within the same time frame as in the offices, see Table 7-7.

The maximum heating power for the offices and the meeting rooms is taken from the reference

BSim models and is converted in kW/m2. The lounge – kitchenette heating power is based on

the office’s heating load per m² as for perimeter located rooms, located on East and West

facades. The heating load for toilets is based on the same kW/m² as for the core located meeting

rooms as they have the same temperature setpoints. The ‘Part to air’ value of 0,6 is fixed in all

room types and is taken from the reference model.

Room Load Part to Air

[-]

Temperature SP Schedule

[kW/m²] Winter [°C] Summer [°C] [-]

Open space offices 0,05 0,6 21 22 06-18

Meeting rooms 0,031 0,6 21 22 06-18

Small meeting rooms 2 0,6 21 22 06-18

Lounge - Kitchenette 0,05 0,6 21 22 06-18

Toilets 0,031 0,6 21 22 06-18

Other - common spaces 0,063 0,6 21 22 06-18

Table 7-10 Alfa: ‘Heating’ loads and schedules

The ‘Heating’ system loads for the different zoning models is a sum of the rooms’ heating loads

in the zone as shown in Table 7-11.


January 2019 65

Zoning

strategy

Orientation Load Zoning

strategy

Orientation Load Zoning

strategy

Orientation Load

[kW] [kW] [kW]

14-zone

model 12-zone

model 1-zone

model - 82,4

Offices SW 6,65 Offices S 12,9 2-zone

model

Offices SE 6,28 Offices N 14,9 Perimeter N,E,S,W 79,4

Offices NW 7,63 Meeting

rooms - 1,84 Core - 2,98

Offices NE 7,12 Small meeting

r. S 40,9

5-zone

model

Meeting

rooms - 1,84 Other N 2,34 Perimeter S 53,8

Small meeting

r. S 40,9 Other W 1,29 Perimeter N 18,1

Other N 2,34 Other W 2,34 Perimeter E 3,79

Other W 1,29 Other E 2,52 Perimeter W 3,64

Other W 2,34 Other - 0,5 Core - 2,98

Other E 2,52 Lounge/Kitch. N 2,24 Other - 0,5 Lounge/Kitch. SE 1,27

Lounge/Kitch. N 2,24 Toilets - 1,14 Lounge/Kitch. SE 1,27

Toilets - 1,14

Table 7-11 Alfa: ‘Heating’ input data for different zoning strategies

7.4.2.2.4 ‘Infiltration’

The infiltration rate of 0,14 is based on the reference BSim models and applied for all room

types located by the external façade (Table 7-12), and the same value is used in different zoning

models (Table 7-13). During the working hours, the rooms were assigned the full infiltration

rate, while for a non-working hour the rate was reduced to 60%.

Table 7-12 Alfa: ‘Infiltration’ for different room types

Table 7-13 Alfa: ‘Infiltration’ system input data for different zoning strategies

Room Basic air change

[1/h]

Schedule

Workdays Weekend

Open space offices 0,14 60% 1-24; 100% 7-18 60% 1-24

Meeting rooms - - -

Small meeting rooms 0,14 60% 1-24; 100% 7-18 60% 1-24

Lounge - Kitchenette 0,14 60% 1-24; 100% 7-18 60% 1-24

Toilets - - -

Other - common spaces 0,14 60% 1-24; 100% 7-18 60% 1-24

Zoning strategy Basic air change Schedule

[1/h] Workdays Weekend

1-zone model 0,14 60% 1-24; 100% 8-17 60% 1-24

2-zone model 0,14 60% 1-24; 100% 8-17 60% 1-24

5-zone model 0,14 60% 1-24; 100% 8-17 60% 1-24

12-zone model 0,14 60% 1-24; 100% 8-17 60% 1-24

14-zone model 0,14 60% 1-24; 100% 8-17 60% 1-24



7.4.2.2.5 ‘Ventilation’

There is a balanced mechanical ventilation system in the building. The minimum heat recovery

is set to 85%. The fan airflow for input and output is calculated in l/s/m² using the reference

model data for the offices and the meeting rooms. For the rooms not defined in the reference

models, as the lounge – kitchenette and the other common spaces, a minimum value of 0,35

l/s/m² of fresh air supply is applied and it is based on BR15 minimum airflow requirement per

m² mentioned in Chapter 6.3.1.3 Buildings other than domestic buildings. [10] The minimum

extraction flow determined by BR15 is also applied for toilets. The total efficiency of the fan

and ‘Part to Air’ value is fixed to 0,5, based on the reference models.

It is assumed that the ventilation system has only one heating coil while in practice there is a

preheating coil and a reheating coil, which means that the description and the control of the

system are approximate. The ‘Max Power’ describes the maximum power that can be released

by the heating coil and the heating requirement is calculated based on the temperature set points.

There is also a cooling coil present in the ventilation unit, and it is assumed to have a constant

temperature independent from the cooling need or the released cooling power. [39] The ‘Max

Power’ is defined as -10 kW, and the value is taken from the reference models.


January 2019 67

System

component

Unit

Op

en s

pac

e

off

ices

Mee

tin

g r

oom

s

Sm

all

mee

tin

g

roo

ms

Lo

un

ge

-

Kit

chen

ette

To

ilet

s

Oth

er -

co

mm

on

spac

es

Core Per.

Fan airflow

input/output

[l/s/m²] 1,356 1,407 2,147 0,350 15 0,350

Pressure rise Input [Pa] 900 900 900 900 900 900

Output [Pa] 600 600 600 600 600 600

Total effect [-] 0,7 0,7 0,7 0,7 0,7 0,7

Part to air [-] 0,5 0,5 0,5 0,5 0,5 0,5

Heating coil Max Power [kW] 5 2 2 5 2 5

Cooling coil Max Power [kW] -10 -10 -10 -10 -10 -10

Surface temp. [°C] 5 5 5 5 5 5

VAV factor Winter [-] 3 3 3 3 3 3

Summer [-] 3 3 3 3 3 3

Night [-] 3 3 3 3 3 3

Min. inlet

temp. SP

Winter [°C] 17 17 17 17 17 17

Summer [°C] 17 17 17 17 17 17

Night [°C] 14 14 14 14 14 14

Max. inlet

temp. SP

Winter [°C] 22 22 22 22 22 22

Summer [°C] 24 24 24 24 24 24

Indoor air

temperature

SP

Winter [°C] 21 21 21 21 21 21

Summer [°C] 23,5 23,5 23,5 23,5 23,5 23,5

Night cooling

summer/winter

[°C] 24,5/

22

24,5/

22

24,5/

22

24,5/

22

24,5/

22

24,5/

22

Schedule Winter [-] 06-19 07-18 06-19 06-19 06-18 06-18 06-19

Summer [-] 06-19 07-18 06-19 06-19 06-18 06-18 06-19

Night cooling [-] 01-05;

23-24

01-05;

23-24

01-06;

22-24

01-05;

23-24

01-05;

23-24;

01-05;

23-24

01-05;

23-24

Table 7-14 Alfa: ‘Ventilation’ loads and schedules for a different type of rooms

VAV control is set for ‘Winter’, ‘Summer’ and ‘Night Cooling’ with defined minimum,

maximum inlet temperatures and their operating schedules taken from the reference models and

applied for all room types. The indoor air temperature SP for the summer period that lasts from

May to September is set to 23,5°C in all the rooms. The winter season, October to March, indoor

temperature SP is set to 21°C in all the rooms, see Table 7-14. The night cooling is active

throughout the year with a cooling SP of 24,5°C and 22°C for summer and winter seasons,

respectively. In all the rooms it is operating from 01-05/23-24 while in the small meeting rooms

the cooling is active in the time frame from 01-06/22-24.

The ‘Ventilation’ system input data for different zoning strategies is shown in the following

Table 7-15.



Zoning strategy Fans

[m³/s]

Heating

coil

[kW]

VAV factor Minimum inlet temp, Max. inlet temp, Indoor air temperature SP Schedule

[-] [°C] [°C] [°C] [-]

Win

ter

Su

mm

er

Nig

ht

Win

ter

Su

mm

er

Nig

ht

Win

ter

Su

mm

er

Win

ter

Su

mm

er

Co

oli

ng

(su

mm

er/

win

ter)

Win

ter

Su

mm

er

Nig

ht

1-zone model 1,624 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

2-zone model

Perimeter 0,907 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Core 0,717 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-05; 23-24

5-zone model

Perimeter S 0,408 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Perimeter N 0,456 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Perimeter E 0,023 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Perimeter W 0,020 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Core 0,717 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-05; 23-24

12-zone model

Offices S 0,378 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Offices N 0,434 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Meeting rooms C. 0,146 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-05; 23-24

Small meeting rooms S 0,030 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-06; 22-24

Other N 0,013 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-18 6-19 01-05; 23-24

Other W 0,013 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-18 6-19 01-05; 23-24

Other W 0,014 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-18 6-19 01-05; 23-24

Other E 0,020 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-19 7-20 01-06; 23-24

Other C. 0,016 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-18 6-19 01-05; 23-24

Lounge/Kitch. N 0,009 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-18 6-19 01-05; 23-24

Lounge/Kitch. E 0,551 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-18 6-19 01-05; 23-24


January 2019 69

Zoning strategy Fans

[m³/s]

Heating

coil

[kW]

VAV factor Minimum inlet temp, Max inlet temp, Indoor air temperature SP Schedule

[°C] [°C] [°C] [°C] [-]

Win

ter

Su

mm

er

Nig

ht

Win

ter

Su

mm

er

Nig

ht

Win

ter

Su

mm

er

Win

ter

Su

mm

er

Co

oli

ng

(su

mm

er/

win

ter)

Win

ter

Su

mm

er

Nig

ht

14-zone model

Offices SW 0,194 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Offices SE 0,184 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Offices NW 0,223 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Offices NE 0,208 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Meeting rooms C. 0,146 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-05; 23-24

Small meeting rooms S 0,030 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-06; 22-24

Other N 0,013 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Other W 0,013 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Other W 0,014 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Other E 0,020 2 3 3 3 17 17 14 22 22 21 23,5 24,5/22 7-18 7-18 01-05; 23-24

Other C. 0,016 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Lounge/Kitch. N 0,009 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Lounge/Kitch. E 0,551 5 3 3 3 17 17 14 22 24 21 23,5 24,5/22 6-19 6-19 01-05; 23-24

Table 7-15 Alfa: ‘Ventilation’ input data for different zoning models



7.4.3 Beta: BSim Inputs

The same strategy for system inputs was used for the Beta case as for

Alfa models.

7.4.3.1 Database

Construction U-Value G-Value Lt

[W/m²K] [-] [-]

Window 0,7 1,6 0,36 0,61

Table 7-16 Beta: Window properties


[m] [W/mK] [m²K/W]

Rse 0,04


Mineral wool 39 0,3 0,039 7,69


Rsi 0,13

Total 0,6 8,002


Table 7-17 Beta: Exterior wall construction


[m] [W/mK] [m²K/W]

Lightweight concrete 0,25 0,35 0,714


Table 7-18 Beta: Internal wall construction


[m] [W/mK] [m²K/W]

Rse 0,04

Insulation 0,1 0,039 2,56


Rsi 0,17

Total 0,250 2,844


Table 7-19 Beta: Ground deck construction


[m] [W/mK] [m²K/W]

Wood board 0,02 0,14 1,16

Lightweight concrete 0,08 0,25 0,32


Insulation 0,04 0,039 1,026

Total 0,36 2,75


Table 7-20 Beta: Floor partition construction


[m] [W/mK] [m²K/W]

Rse

Roofing felt 0,01 0,5 0,04

Plywood 0,16 0,12 1,33

Mineral wool 39 0,175 0,039 4,49

Wood distance lists 0,025 0,12 0,21

Rsi 0,10

Total 0,37 6,17


Table 7-21 Beta: Roof construction


January 2019 71

7.4.3.2 Systems

7.4.3.2.1 ‘People Load’

The different ‘Day Profile’ inputs for the open space offices and the meeting rooms are known

from the reference simulation models. The office occupancy profile is constant throughout the

year. The meeting rooms have a more sophisticated schedule for the winter season (lasting from

week 01-27/33-52), a profile for every 1/3 weeks (week 1, week 4, week 7 and so on), and a

lower people profile every 2/3 weeks (weeks 2-3, weeks 5-6 and so on). The summer schedule

is set for the weeks 28-32. The lounge-kitchenette, the toilets and the other room inputs were

created in the same manner as for Alfa, except the toilets that were designed with the summer

schedule as well.



14-zone

Th

erm

al

zon

e

Off

ices

W

Off

ices

N

Off

ices

E

Off

ices

SE

Mee

tin

g

roo

ms

S

Sm

all

mee

tin

g r

.

SW

Mee

tin

g

roo

ms

W

Sm

all

mee

tin

g r

. C

.

Lo

un

ge/

Kit

ch.

S

To

ilet

s C

.

Oth

er S

Oth

er N

Oth

er C

.

Oth

er C

.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Load[No.] 18 14 20 8 12 6 12 6 6 5 0 0 0 0

Schedule

1/3 2/3 Sum 1/3 2/3 Sum 1/3 2/3 Sum Win Sum

Win Sum

Hour % % % % % % % % % % % % % % % % % % % % % %

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 85 85 85 85 0 0 0 0 0 0 0 0 0 0 0 0 20 5 0 0 0 0

9 85 85 85 85 0 0 0 0 0 0 0 0 0 34 0 5 20 5 0 0 0 0

10 85 85 85 85 16 25 16 16 25 16 16 25 16 100 64 5 20 5 0 0 0 0

11 85 85 85 85 41 50 25 41 50 25 41 50 25 34 0 5 20 5 0 0 0 0

12 85 85 85 85 0 0 0 0 0 0 0 0 0 100 0 5 20 5 0 0 0 0

13 25 25 25 25 16 16 16 16 16 16 16 16 16 25 0 100 20 5 0 0 0 0

14 85 85 85 85 80 50 41 80 50 41 80 50 41 34 0 5 20 5 0 0 0 0

15 85 85 85 85 16 16 16 16 16 16 16 16 16 67 0 5 20 5 0 0 0 0

16 85 85 85 85 25 25 16 25 25 16 25 25 16 16 64 5 20 5 0 0 0 0

17 50 50 50 50 0 0 0 0 0 0 0 0 0 17 17 5 20 5 0 0 0 0

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 7-22 Beta: 'People load' inputs for the 14-zone model


January 2019 73

5-zone 2-zone 1-zone

1

2 3 4

5

1

2

1

Th

erm

al

zon

e

Per

imet

er

S

Per

imet

er

N

Per

imet

er

E

Per

imet

er

W

Co

re

Per

imet

er

Co

re

Wh

ole

bu

ild

ing

1

2

3

4

5

1

2

1

Load[No.] 9,9 11,9 24 30 7 75,3 7 78

Schedule 1/3 2/3 Sum

1/3 2/3 Sum Win Sum 1/3 2/3 Sum Win Sum 1/3 2/3 Sum

Hour % % % % % % % % % % % % % % % % % %

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 0 0 0 100 100 52 52 52 14 4 68 68 68 14 4 66 66 65

9 3 3 3 100 100 52 52 52 43 4 68 68 68 43 4 69 69 `66

10 22 33 22 100 100 61 67 61 100 58 75 78 75 100 58 81 84 77

11 53 64 33 100 100 76 82 67 43 4 84 88 78 43 4 85 89 75

12 3 3 3 100 100 52 52 52 100 4 68 68 68 100 4 74 74 66

13 80 80 80 29 29 25 25 25 36 4 34 34 34 36 4 36 36 33

14 100 64 53 100 100 100 82 76 43 4 100 88 84 43 4 100 89 82

15 22 22 22 100 100 61 61 61 72 4 75 75 75 72 4 78 78 72

16 33 33 22 100 100 67 67 61 28 58 78 78 75 28 58 78 78 77

17 3 3 3 59 59 30 30 30 29 18 40 40 40 29 18 41 41 40

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 7-23 Beta: 'People load' inputs for the 5, 2 and 1-zone models



7.4.3.2.2 ‘Equipment Load’

The heat load from the equipment for offices, meeting rooms, lounge, toilets and others were

created as for Alfa building. In the toilets, there are assumed to be two hand dryers, same type

as for the Alfa case, one in the accessible toilet and one in the other toilets.

Table 7-24 shows the load of equipment and lighting for the 14-zone model rooms, later

aggregated to the courser models.

14-zone

Th

erm

al z

on

e

Off

ices

W

Off

ices

N

Off

ices

E

Off

ices

SE

Mee

tin

g r

oo

ms

S

Sm

all

Mee

tin

g

roo

ms

SW

Mee

tin

g r

oo

ms

W

Sm

all

mee

tin

g

roo

ms

C.

Lo

un

ge/

Kit

ch.

S

To

ilet

s C

.

Oth

er S

Oth

er N

Oth

er C

.

Oth

er C

.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Plug Load 1,98 1,54 2,20 0,88 0,43 0,10 0,43 0,43 3,20 2,30 0,00 0,00 0,00 0,00

Lighting Load 1,15 0,87 1,16 0,39 0,18 0,11 0,13 0,08 0,44 0,32 0,55 0,31 0,78 0,14

Table 7-24 Beta: Plug and lighting load in kW for-zone model

Table 7-25 shows the 14-zone model hourly equipment load profiles for the workdays. This

case building does not consider the ‘standby’ equipment loads during the off-office hours or

weekends. The offices and the meeting rooms are designed without the standby loads from the

appliances according to the reference models, therefore no standby mode was not applied to

any rooms. The ‘Equipment’ input data for other zoning strategies for 5, 2 and 1-zone models

is shown in the following Table 7-26. The winter season is from January-April and October to

December, and the summer season includes May to September months.


January 2019 75

14-zone

Th

erm

al

zon

e

Off

ices

W

Off

ices

N

Off

ices

E

Off

ices

SE

Mee

tin

g

roo

ms

S

Sm

all

mee

tin

g

r. S

W

Mee

tin

g

roo

ms

W

Sm

all

mee

tin

g

r. C

.

Lo

un

ge/

Kit

ch.

S

To

ilet

s C

.

Oth

er S

Oth

er N

Oth

er C

.

Oth

er C

.

1 2

3 4 5 6 7 8 9

10

11

12

13

14

Load [No.] 3,13 2,41 3,36 1,27 0,61 0,21 0,56 0,51 3,64 2,62 0,55 0,31 0,78 0,14

Schedule Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum

Hour % % % % % % % % % % % % % % % % % % % % % % % % %

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 37 37 36 36 35 35 31 31 29 29 53 53 23 23 15 15 21 17 21 16 100 100 100 100 100

9 91 68 90 68 90 67 90 65 36 36 58 58 30 30 24 24 21 17 21 16 100 100 100 100 100

10 91 68 90 68 90 67 90 65 47 36 65 58 42 30 36 24 21 17 21 16 100 100 100 100 100

11 91 68 90 68 90 67 90 65 64 57 76 72 61 54 58 49 21 17 21 16 100 100 100 100 100

12 91 68 90 68 90 67 90 65 36 36 58 58 30 30 24 24 21 17 21 16 100 100 100 100 100

13 53 53 52 52 51 51 48 48 47 36 65 58 42 30 36 24 100 100 100 100 100 100 100 100 100

14 91 68 90 68 90 67 90 65 82 64 88 76 81 61 79 58 21 17 21 16 100 100 100 100 100

15 91 68 90 68 90 67 90 65 47 36 65 58 42 30 36 24 21 17 21 16 100 100 100 100 100

16 91 68 90 68 90 67 90 65 47 36 65 58 42 30 36 24 21 17 21 16 100 100 100 100 100

17 68 53 68 52 67 51 65 48 36 36 58 58 30 30 24 24 21 17 21 16 100 100 100 100 100

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 7-25 Beta: ‘Equipment load' inputs for the 14-zone model for the workday



5-zone 2-zone 1-zone

Th

erm

al

zon

e

Per

imet

er

S

Per

imet

er

N

Per

imet

er

E

Per

imet

er

W

Co

re

Per

imet

er

Co

re

Wh

ole

bu

ild

ing

1

2

3

4

5

1

2

1

Load [No.] 4,37 2,49 4,17 3,58 3,72 11,94 3,72 14,10

Schedule Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum Win Sum

Hour % % %

%

% % % % % % % % % %

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 32 28 47 47 37 37 42 42 42 38 47 46 42 38 51 49

9 33 29 100 78 100 74 90 71 43 40 95 74 43 40 92 73

10 34 29 100 78 100 74 93 71 44 40 96 74 44 40 93 73

11 36 32 100 78 100 74 96 75 47 43 98 77 47 43 95 76

12 33 29 100 78 100 74 90 71 43 40 95 74 43 40 92 73

13 100 99 63 63 56 56 59 57 100 98 87 86 100 98 100 99

14 39 33 100 78 100 74 100 77 50 44 100 77 50 44 98 77

15 34 29 100 78 100 74 93 71 44 40 96 74 44 40 93 73

16 34 29 100 78 100 74 93 71 44 40 96 74 44 40 93 73

17 33 29 78 63 74 56 71 57 43 40 75 60 43 40 75 61

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table 7-26 Beta: ‘Equipment load' inputs for the 5, 2 and 1-zone models for the workday


January 2019 77

7.4.3.2.3 ‘Heating’

This heating system is set to a maximum load of 20 kW in all rooms, to allow the system to use

the energy needed to reach the desired temperature setpoints. Based on the reference model

input data, the heating system is operating throughout the year, but during workdays only. The

temperature setpoint for the working hours, 08-17, is set to 22°C and 19°C outside the office

hours (Table 7-27). The ‘Part to air’ value of 0,6 is fixed and taken from the reference models.

Room Load

MAX

Part to Air

[-]

Temperature SP Schedule

Monday-Friday

[kW] Day [°C] Night [°C] Day Night

Open space offices 20 0,6 22 19 08-17 01-07, 18-24

Meeting rooms 20 0,6 22 19 08-17 01-07, 18-24

Small meeting rooms 20 0,6 22 19 08-17 01-07, 18-24

Lounge - Kitchenette 20 0,6 22 19 08-17 01-07, 18-24

Toilets 20 0,6 22 19 08-17 01-07, 18-24

Other - common spaces 20 0,6 22 19 08-17 01-07, 18-24

Table 7-27 Beta: ‘Heating’ loads and schedules

As the inputs are the same for all type of the rooms, the same values are also applied for the

different zoning models.

7.4.3.2.4 ‘Infiltration’

The infiltration rates are defined for the open offices and meeting rooms and are based on the

reference BSim models, see Table 7-28. The lounge-kitchenette, located by the façade has been

applied open office air change rate, ACR, while the other rooms and spaces located by the

façade has a value of the small meeting rooms. The ACR in the 12-zone and 14-zone models

are in accordance with the room type and location as shown in Table 7-28, where the core

located rooms do not have an infiltration rate set. The 1-zone, 2-zone and the 5-zone models

have the ACR of the open offices, and the infiltration schedule is set for open offices from 8

am to 5 pm for perimeter located thermal zones.

Table 7-28 Beta: ‘Infiltration’ rate by room types

Room Basic air change

[1/h]

Schedule

Workdays Weekend

Open space offices 0,1196 60% 1-7, 100% 8-17, 60% 18-24 60% 1-24

Meeting rooms 0,1230 60% 1-7, 100% 8-17, 60% 18-24 60% 1-24

Small meeting rooms 0,1154 60% 1-7, 100% 8-17, 60% 18-24 60% 1-24

Lounge - Kitchenette 0,1196 60% 1-7, 100% 8-17, 60% 18-24 60% 1-24

Toilets - - -

Other - common spaces 0,1154 60% 1-7, 100% 8-17, 60% 18-24 60% 1-24



7.4.3.2.5 ‘Ventilation’

There is a balanced mechanical ventilation system in the building. The minimum heat recovery

is 80%. The fan airflow for input and output is calculated in l/s/m² using the reference model

data for the offices and the meeting rooms. Same as for Alfa case building, for the rooms not

defined in the reference models, as the lounge – kitchenette and the other common spaces, a

minimum value of 0,35 l/s/m² of fresh air supply is applied. The minimum extraction flow

determined by BR15 is also applied for toilets. The total efficiency of the fan and ‘Part to Air’

value is fixed to 0,5, based on the reference models.

System component Unit

Op

en s

pac

e

off

ices

Mee

tin

g r

oom

s

Sm

all

mee

tin

g

roo

ms

Lo

un

ge

-

Kit

chen

ette

To

ilet

s

Oth

er -

co

mm

on

spac

es

Fan airflow Input/Output [l/s/m²] 1,372 0,944 0,944 0,350 15 0,350

Pressure rise Input [Pa] 900 900 900 900 900 900

Output [Pa] 600 600 600 600 600 600

Total effect [-] 0,7 0,7 0,7 0,7 0,7 0,7

Part to air [-] 0,5 0,5 0,5 0,5 0,5 0,5

Heating coil Max Power [kW] 2 2 2 2 2 2

Cooling coil Max Power [kW] -10 -10 -10 -10 -10 -10

Surface temp. [°C] 8 8 8 8 8 8

VAV factor Comfort [-] 3,7/6* 4,97 3 3 3 3

Part of nominal flow Night ventil. [-] 1,5 1,5 1,5 1,5 1,5 1,5

Min. inlet temp. SP Comfort [°C] 18 18 18 18 18 18

Night ventil [°C] 16 16 16 16 16 16

Max. inlet temp. SP Comfort [°C] 25 25 25 25 25 25

Indoor air

temperature SP

Comfort [°C] 22 22 22 22 22 22

Cooling [°C] 23,5 23,5 23,5 23,5 23,5 23,5

Night ventil. [°C] 19 19 19 19 19 19

Schedule Comfort [-] 08-17 08-17 08-17 08-17 08-17 08-17

Night ventil [-] 01-07;

22-24

01-07;

22-24

01-07;

22-24

01-07;

22-24

01-07;

22-24;

01-07;

22-24

*VAV factor of 3,7 for North and East oriented open offices, and 6 for South oriented open offices, based on

reference models.

Table 7-29 Beta: ‘Ventilation’ loads and schedules for a different type of rooms

Same as in Alfa case it is assumed that the ventilation system has only one heating coil while

in practice there is a preheating coil and a reheating coil, which means that the description and

the control of the system are also approximate. The ‘Max Power’ is set to 2 kW, telling the

BSim that the heating coil can release it based on the heating need that depends on the

temperature set points. The ‘Max Power’ for the cooling coil is defined as -10kW, and the value

is taken from the reference models.

VAV control is set for the comfort ventilation and the night ventilation, with defined minimum,

maximum inlet temperatures as shown in Table 7-29 above and is applied to the office working


January 2019 79

hours from 8 am - 5 pm in all types of rooms. The indoor air temperature SP for the comfort

ventilation that lasts from January to December is set to 22°C in all the rooms. The night

ventilation is active throughout the year with a temperature SP of 19°C. In all the rooms it is

operating from 1 am-7 am/10pm-12 am.

The ‘Ventilation’ system input data for different zoning models is shown in the following Table

7-30.



Zoning strategy Fans Heating VAV factor Minimum inlet temp, Max. inlet temp, Indoor air temperature SP Schedule

coil [-] [°C] [°C] [°C] [-]

[m³/s] [kW]

Co

mfo

rt

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Nig

ht

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Co

oli

ng

Nig

ht

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Nig

ht

ven

tila

tio

n

1-zone model 1,465 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

2-zone model 1,465

Perimeter 0,730 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Core 0,735 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

5-zone model

Perimeter S 0,175 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Perimeter N 0,239 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Perimeter E 0,153 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Perimeter W 0,164 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Core 0,735 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

12-zone model

Offices W 0,198 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Offices NE 0,348 2 3.7 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Offices SE 0,067 2 3.7 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Meeting rooms S-

SW

0,035 2 4.97 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Meeting rooms W 0,017 2 4.97 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Small meeting

rooms C.

0,014 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Lounge/Kitch. S 0,022 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Toilets C. 0,675 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Other S 0,028 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Other N 0,015 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Other C. - - - - - - - - - - -

Other C. - - - - - - - - - - -


January 2019 81

Zoning strategy Fans Heating VAV factor Minimum inlet temp, Max inlet temp, Indoor air temperature SP

Schedule

coil [°C] [°C] [°C] [°C]

[-]

[m³/s] [kW]

Co

mfo

rt

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Nig

ht

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Co

oli

ng

Nig

ht

ven

tila

tio

n

Co

mfo

rt

ven

tila

tio

n

Nig

ht

ven

tila

tio

n

14-zone model

Offices W 0,198 2 6 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Offices N 0,150 2 3.7 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Offices E 0,199 2 3.7 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Offices SE 0,067 2 3.7 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Meeting rooms S 0,035 2 4.97 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Small Meeting

rooms SW

0,017 2 4.97 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Meeting rooms W 0,014 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Small meeting

rooms C.

0,022 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Lounge/Kitch. S 0,675 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Toilets C. 0,028 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Other S 0,015 2 3 18 16 25 22 23,5 19 Mon-Fri, 08-17 Mon-Fri, 1-07, 22-24

Other N - - - - - - - - - - -

Other C. - - - - - - - - - - -

Table 7-30 Beta: ‘Ventilation’ input data for different zoning models



7.4.4 Uncertainty Analysis

Uncertainties in building simulations generally arise from:

• Improper input parameters – due to lack of specification of material properties or system

parameters;

• Improper model assumptions and simplifications. [40]

The slight error was detected due to the BSim program allowing only whole number for people

and equipment % to be defined in the day profile. Table 7-31 shows the comparison between

the whole and rational (2 decimals) percentages of the equipment profile, taken from Table 7-9

(5-zone model, Perimeter North, workday). The daily error results in 0,6 kWh lower electricity

usage for one workday. When the profile is used every workday, resulting in 52 weeks or 260

workdays, the total difference becomes 156 kWh yearly. The weekend profile of 16% and

16,23% load corresponds to 62,4 kWh difference. The total mismatch for the Perimeter North

zone results in 218,4 kWh.

Hour Whole

[%]

Equipment

[kWh]

Rational

[%]

Equipment

[kWh]

Error

1-6 16% 1,63 16,23% 1,66 0,986

7 38% 3,88 37,90% 3,87 1,003

8 82% 8,37 82,29% 8,40 0,996

9 82% 8,37 82,29% 8,40 0,996

10 82% 8,37 82,29% 8,40 0,996

11 82% 8,37 82,29% 8,40 0,996

12 82% 8,37 82,29% 8,40 0,996

13 100% 10,21 100,00% 10,21 1,000

14 82% 8,37 82,29% 8,40 0,996

15 82% 8,37 82,29% 8,40 0,996

16 82% 8,37 82,29% 8,40 0,996

17 82% 8,37 82,29% 8,40 0,996

18 82% 8,37 82,29% 8,40 0,996

19-24 16% 1,63 16,23% 1,66 0,986

Peak Load 10,21

Sum

117,4 118,0 0,995

Table 7-31 An example of the Alfa case study equipment profiles of the 5-zone model, Perimeter

North. Comparison between whole to rational numbers

7.5 Baseline Building Models Validation

The creation of the baseline building model is very important during this study process. As

Kaplan et al. have mentioned, it is impossible to cover all the building aspects into a baseline

definition. However, it is possible to define the hierarchy of the input data sources for definition.

[5] To create the baseline building model, the reference BPS models’ inputs are used for the


January 2019 83

defined room types; otherwise, the local building codes such as Building Regulations 2015 and

other national standards are used.

For the simplified models to be representable of the reference rooms, the BPS models used

several identical boundary input parameters related to buildings’ geometry, physical

characteristics and occupants. Based on the reference models, a set of parameters are kept

constant, as described in the previous chapter. These parameters include:

a) the weather data file;

b) database;

c) function and occupancy;

d) equipment loads and schedules;

e) heating system loads and schedules;

f) ventilation ACR and schedules, regarding system input power per m² and temperature

setpoints;

g) infiltration rate;

h) temperature setpoints and schedules;

i) general lighting loads;

j) later applied cooling system loads and schedules.

The choice of ‘Critical Room,’ e.g., reference rooms, which is simulated as a single zone,

depends on its orientation, heat gains, HVAC system distribution, occupancy patterns etc. By

the Indoor Environment class the Alfa case building was designed to fulfil thermal comfort

requirements for the category B with the operative temperatures of 24,5 +/- 1,5°C in the cooling

season and 22,0 +/- 2°C in the heating season. The Beta case building was designed to comply

with category A thermal comfort design criteria for landscaped offices of 24,5 +/- 1°C during

the summer season, and 22,0 +/- 1°C during the winter season, set by DS/CEN/CR 1752:2001

- European and Danish Design Criteria for The Indoor Environment [33].

The reference rooms’ BSim model input settings were set to comply with the legislation

stipulating requirements for the indoor environment in Denmark. The temperature tolerances

permissible are set according to DS 474, thus overheating hours acceptable are no more than

100 hours above 26 °C, and 25 hours above 27 °C during a typical year [41]. The simulation

period for obtaining the data from reference models used by MOE was the calendar year 2010,

and as a boundary condition, it was used in further modelling phases.

The reference BSim models received from the collaboration with MOE’s office, where

normalised by combining lighting with equipment to have the same loads in all the zonal



configuration models. The external shading was also removed to assess the maximum solar heat

gains in all the models. There was also a decentral mechanical ventilation system set up in the

small meeting rooms in Beta reference models to comply with the overheating assessment that

was also removed to have the same boundary conditions and consistency in the developed

models for the zonal study.

Graph 7-1 shows the relation between the annual heating need per m² between the reference

rooms and the respective rooms in the baseline model, in a) open offices, b) meeting rooms and

c) small meeting rooms for both case buildings, Alfa and Beta. The open offices are located by

the perimeter of the building, in both case buildings. The meeting rooms are core located in

Alfa and perimeter located in Beta case, while the small meeting rooms are perimeter located

in both cases.

Alfa Beta

a) o

pen

off

ices

b)

mee

tin

g r

oom

s

c) s

mal

l m

eeti

ng

roo

ms

Graph 7-1 Annual heating demand per m² comparison between the reference rooms and the baseline

rooms

0,0

0,2

0,4

0,6

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/m

²]

Baseline - S1 Baseline - N1


REF - S REF - N

0,0

0,2

0,4

0,6

0,8

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/m

²]


. Baseline - N2

REF - S REF - N

0

0,25

0,5

0,75

1

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/m

²]

Baseline REF

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/m

²]

Baseline - SE Baseline - NEREF - SE REF - NE

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/m

²]

Baseline S REF S

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12

[kW

h/m

²]

Baseline - SW REF - SW


January 2019 85

In Graph 7-1 it can be observed that:

a) reference models for South and North oriented open offices have higher heating demand

than the same orientation offices in the baseline models, both for Alfa and Beta case.

The offset is caused due to the reference models being designed as a separate single

zone with a uniform ambient environment being affected by the weather data, whereas

the baseline model performance is affected by the heat exchange between the adjacent

thermal zones, stabilising and minimising the total heat demand. The peak in March for

the North oriented rooms is due to a lower mean outdoor temperature, increasing the

heating need consistently in both cases for reference and baseline models.

b) for the core located meeting rooms in Alfa case, the heating demand per m² in the

baseline modelled room is lower than for the reference room, which can also be

explained by the difference in designing the single zone reference model and the

multizone baseline model, keeping in mind that the inputs are based on the reference

model. In the multi-zone baseline simulation model, the hallways with the open offices,

adjacent to the meeting rooms, have skylights contributing with the external heat gains,

whereas in the reference model - there were not adjacent rooms designed that would

include the heat gains from the skylights, only a uniform ‘indoor’ environment applied.

In the baseline models, the open offices surrounding the meeting rooms have slightly

higher mean operative temperature leading to the heat exchange between the rooms and

minimising the heat demand in the meeting rooms as shown in Graph 7-2 (with the box

and whisker diagram graphically sorting the data through their quartiles. The ‘x’ marks

the mean value).

Graph 7-2 Alfa: mean operative temperature for the adjacent offices and meeting room in the

baseline model for the work time hours

In the Beta case, in the baseline model, the perimeter located West oriented meeting

room, result in lower heating demand than the respective room in the reference model.

The adjacent North-facing and South-facing offices’ mean operative temperature is



slightly lower than in the meeting room, see Graph 7-3. The small rise in the REF – SE

room heat demand in March is explained via the outdoor mean temperature drop.

Graph 7-3 Beta: Mean operative temperature for the adjacent offices to the small meeting room in

the baseline model for the work time hours

c) the baseline models of the small meeting rooms in the Alfa case, result in lower heating

demand than the reference model still following the trend. The room in the baseline

model is lower due to the adjacent thermal zones influencing the heat exchange and

minimising the heating need.

In the Beta baseline model, the small reference meeting rooms REF – SW and the

meeting room REF – SE are merged in one thermal zone due to the zoning

considerations described in previous chapter 7.3 Zonal Configurations. The heating

demand per m² in the baseline model stays in the range between the two reference

meeting rooms.

The same comparison was performed to the simulation models with the mechanical cooling

system applied. The monthly heating demand resulted in very close need.

The 14-zone baseline models were compared to the other zoning strategies. For Alfa, the

heating demand is shown in Table 7-32, and the cooling need is shown in Table 7-33. The Beta

case models are demonstrated in Table 7-34 for heating and Table 7-35 for cooling.


January 2019 87

12-ZONE 5-ZONE 2-ZONE 1-ZONE

Table 7-32 Alfa: Monthly heating demand comparison between the Baseline model (red lines) to other

zoning strategies (black lines)

0,0

0,2

0,4

1 2 3 4 5 6 7 8 9 101112

1 Offices S

0,0

1,0

1 3 5 7 9 11

1 Perimeter S

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 10 11 12

1 Perimeter

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

4 Small meeting r. S

0,0

0,2

0,4

1 2 3 4 5 6 7 8 9 101112

2 Offices N

0,0

1,0

1 3 5 7 9 11

2 Perimeter N

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

5 Other N

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

10 Lounge/Kitch. N

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

8 Other E

0,0

1,0

1 3 5 7 9 11

3 Perimeter E

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

11 Lounge/Kitch. E

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

6 Other W

0,0

1,0

1 3 5 7 9 11

4 Perimeter W

0,0

0,2

0,4

1 2 3 4 5 6 7 8 9 101112

7 Other W

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

3 Meeting rooms C.

0,0

1,0

1 3 5 7 9 11

5 Core

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

2 Core

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

9 Other C.

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

12 Toilets C.




Table 7-33 Alfa: Monthly cooling demand comparison between the Baseline model (red lines) to other


0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

1 Offices S

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter S

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

5,0

1 2 3 4 5 6 7 8 9 1011 12

4 Small meeting r. S.

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 1011 12

2 Offices N

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

2 Perimeter N

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 1011 12

5 Other N

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

10 Lounge/Kitch. N

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

8 Other E

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

3 Perimeter E

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

11 Lounge/Kitch. E

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

6 Other W

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

4 Perimeter W

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

7 Other W

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 1011 12

3 Meeting rooms C.

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

5 Core

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

2 Core

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

9 Other C.

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

12 Toilets C.


January 2019 89


Table 7-34 Beta: Monthly heating demand comparison between the Baseline model (red lines) to other


0,0

10,0

20,0

1 2 3 4 5 6 7 8 9 101112

4 Meeting rooms S-SW

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter S

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

7 Lounge/Kitch. S

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

9 Other S

0,0

5,0

1 2 3 4 5 6 7 8 9 101112

10 Other N

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

2 Perimeter N

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

2 Offices NE

0,0

0,2

0,4

1 2 3 4 5 6 7 8 9 101112

3 Offices SE

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

3 Perimeter E

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

1 Offices W

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

4 Perimeter W

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

5 Meeting rooms W

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

6 Small meeting r. C.

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

5 Core

0,0

5,0

1 2 3 4 5 6 7 8 9 101112

2 Core0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

8 Toilets C.

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

11 Other C.

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

12 Other C.




Table 7-35 Beta: Monthly cooling demand comparison between the Baseline model (red lines) to other


0,0

10,0

20,0

1 2 3 4 5 6 7 8 9 101112

4 Meeting rooms S-SW

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter S

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

1 Perimeter

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

7 Lounge/Kitch. S

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

9 Other S

0,0

5,0

10,0

1 2 3 4 5 6 7 8 9 101112

10 Other N

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

2 Perimeter N

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

2 Offices NE

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

3 Offices SE

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

3 Perimeter E

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

1 Offices W

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

4 Perimeter W

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

5 Meeting rooms W

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

6 Small meeting r. C.

0,0

1,0

2,0

1 2 3 4 5 6 7 8 9 101112

5 Core

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

2 Core0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

8 Toilets C.

0,0

0,5

1,0

1 2 3 4 5 6 7 8 9 101112

11 Other C.

0,0

2,0

4,0

1 2 3 4 5 6 7 8 9 101112

12 Other C.


January 2019 91

7.6 Parametric Simulations

To assess the significance and impact of the different zoning strategies on the indoor

environment - multiple simulation runs were performed to identify the thermal zoning

sensitivity to input parameter variations.

There were two criteria defined to be met for the parametric simulations:

• Changes to the input parameters must be consistent on the output results same as on the

baseline model [42]

• The effect each input has on the output must be ranked the same as for the baseline

model [42].

The objective of this study was to analyse the overheating hours associated with different

thermal mass variation in the zoning strategies applied to the two case office buildings via a

batch of dynamic building simulations were performed on these predefined zoning models. The

following parameter variations were considered and applied to the previously described thermal

zoning strategies to assess their response regarding heating and cooling demand:

• Different internal thermal mass application - zoning phase 3 to 5 are simulated with

internal walls and without internal walls

• Mechanical cooling application – all zoning models have been applied mechanical

cooling system with maximum power, to reduce the overheating to the limits of the

maximum allowed overheating hours above 26°C and 27°C, and the total cooling

demand per thermal zone is assessed.

7.6.1 Heating, Cooling Demand and Overheating Risk

The greatest part of the total energy consumption in the building is consumed by its HVAC

system that regulates the indoor environment [16], which is the primary quality in the user’s

satisfaction [17] [18]. The thermal comfort in the offices is connected to the cooling demand

that increases its usage in the commercial sector [19]. Lightweight constructions cannot

dissipate the heat, leading to higher risk of overheating [20], whereas the heavier constructions

succeed in stabilising the indoor temperature and reduces the cooling need [21][22] [23] [24].

Table 7-36 shows the difference in overheating hours for Alfa case building - a), that has light

internal walls, and the Beta case building - b), with lightweight concrete internal walls, see the

construction descriptions in chapter 7.4.2.1 for Alfa, and chapter 7.4.3.1 for Beta building. The

Beta has much lower overheating hours due to the bigger thermal mass compared to Alfa,

stabilising the indoor temperature fluctuations. In both cases, the overheating hours increase



with the complexity of the zoning models, where if comparing in between the same zoning

strategies with and without designed partition walls, the overheating is higher in the models

without partition walls. This tendency is consistent in both study cases that are supported by

the theory mentioned above.

Model >26°C >27°C

1-IW 164 66

1-NIW 171 60

2-IW 324 131

2-NIW 311 119

5-IW 1597 1392

5-NIW 1576 1370

12-IW 1713 1486

B-IW 1703 1469

Model >26°C >27°C

1-IW 16 3

1-NIW 0 0

2-IW 43 10

2-NIW 52 17

5-IW 387 163

5-NIW 391 156

12-IW 941 572

B-IW 1075 872

a) Alfa b) Beta

Model >26°C

(Cool)

>27°C

(Cool)

1-IWC 0 0

1-NIWC 0 0

2-IWC 0 0

2-NIWC 0 0

5-IWC 71 16

5-NIWC 64 14

12-IWC 75 39

B-IWC 71 39

Model >26°C

(Cool)

>27°C

(Cool)

1-IWC 0 0

1-NIWC 0 0

2-IWC 0 0

2-NIWC 0 0

5-IWC 0 0

5-NIWC 0 0

12-IWC 30 8

B-IWC 113 81

c) Alfa with mechanical cooling d) Beta with mechanical cooling

Table 7-36 Alfa: a) and c overheating hours); Beta: b) and d) overheating hours in all BSim models

The abbreviations used in the tables above to describe the BPS models thermal mass

configuration and the application of the mechanical cooling:

o IW – Internal Walls;

o IWC – Internal Walls with Cooling;

o NIW – No Internal Walls;

o 1, 2, 5, 12 – stands for the zoning phase describing the number of thermal zones in it;

o B – abbreviates the most complex 14-zone models, referred to as the ‘Baseline’ models.

The influence of the thermal mass variations in the zoning models was analysed regarding

annual energy demand for different systems applied to the thermal zones.


January 2019 93

Area qHeating qHeating (Cool) qCooling qPeople qEquipment HtCoil HtCoil (Cool) ClCoil ClCoil (Cool)

[m²] kWh kWh kWh kWh kWh kWh kWh kWh kWh

1-IW 943 3261 3281 5063 41613 80576 1138 1158 5352 4810

1-NIW 970 1087 1088 4951 41613 80576 922 923 5246 4701

2-IW 943 1662 1662 6995 41598 80780 2428 2436 8638 7725

2-NIW 954 1217 1217 6656 41598 80780 2441 2448 8771 7904

5-IW 943 2272 2292 6763 41641 80871 630 640 11326 10437

5-NIW 953 1859 1867 6379 41641 80871 590 598 11400 10560

12-IW 943 2128 2221 9180 41609 81517 1389 1417 13703 12771

B-IW 943 2147 2243 8096 41609 81517 1387 1417 15231 14329

Table 7-37 Alfa: Total energy demand for different systems

Table 7-37 shows the yearly energy usage of the different systems. The people load is not the heat load that is received from the people a 0,1 kW per

person. In this case, the baseline models are used as a reference to be compared. As seen in Graph 7-4, the annual heating demand for different zoning

models, then the models with the internal walls are resulting in smaller offset from the baseline model, varying from +/- 12% to 17%.

a) heating (in the model without cooling) b) heating (in the model with cooling) c) cooling

Graph 7-4 Alfa: a) annual heating demand comparison to the baseline model; b) annual heating demand comparison to the baseline model after the application of

the mechanical cooling; c) annual cooling demand comparison to the baseline model after the application of the mechanical cooling to reduce the overheating

46%

-53%

-26%

-46%

2%

-18%

-1%

-60%

-30%

0%

30%

60%

1-IW

C

1-N

IWC

2-IW

C

2-N

IWC

5-IW

C

5-N

IWC

12-I

WC

Baseline 2,3 kWh/m²/year46%

-52%

-26%

-46%

2%

-17%

-1%

-60%

-30%

0%

30%

60%

1-I

WC

1-N

IWC

2-I

WC

2-N

IWC

5-I

WC

5-N

IWC

12

-IW

C


-37% -41%

-14%-19% -16%

-22%

13%

-60%

-30%

0%

30%

60%

1-IW

C

1-N

IWC

2-IW

C

2-N

IWC

5-IW

C

5-N

IWC

12-I

WC




Overview of zoning strategies with/without internal walls and with/without mechanical cooling yearly demand is shown in Table 7-38.

Area qHeating qHeating (Cool) qCooling qPeople qEquipment HtCoil HtCoil (Cool) ClCoil ClCoil (Cool)

[m²] kWh kWh kWh kWh kWh kWh kWh kWh kWh

1-IW 849,8 6204 6204 978 14312 31912 1833 1833 2239 2193

1-NIW 878,7 4454 4454 1679 14312 31912 1027 1028 2532 2486

2-IW 849,8 8961 8967 1236 14323 31779 5392 5412 4017 3930

2-NIW 878,6 4384 4384 1443 14323 31779 3331 3336 4508 4396

5-IW 873,0 9093 9094 214 14333 31902 6646 6649 7766 7735

5-NIW 873,0 4381 4382 263 14333 31902 4421 4423 8103 8063

12-IW 848,7 5972 6090 3312 14559 31909 4285 4334 6927 6754

B-IW 848,7 5573 5720 3690 14559 31908 4510 4571 7349 7073

Table 7-38 Beta: Total energy demand for different systems

As seen in Graph 7-5, the zoning models yearly heating and cooling need per m² is compared to the baseline model, and the offset is presented in %.

a) heating (in the model without cooling) b) heating (in the model with cooling) c) cooling

Graph 7-5 Beta: a) annual heating demand comparison to the baseline model; b) annual heating demand comparison to the baseline model after the application of

the mechanical cooling; c) annual cooling demand comparison to the baseline model after the application of the mechanical cooling to reduce the overheating

11%

-23%

61%

-24%

63%

-24%

7%

-100%

-50%

0%

50%

1-IW

C

1-N

IWC

2-IW

C

2-N

IWC

5-IW

C

5-N

IWC

12-I

WC

B-I

WC


8%

-25%

57%

-26%

59%

-26%

6%

-100%

-50%

0%

50%

1-I

WC

1-N

IWC

2-I

WC

2-N

IWC

5-I

WC

5-N

IWC

12-I

WC

B-I

WC

Baseline 6,7 kWh/m²/year -74%

-56%-67% -62%

-94% -93%

-10%

-100%

-50%

0%

50%

1-I

WC

1-N

IWC

2-I

WC

2-N

IWC

5-I

WC

5-N

IWC

12

-IW

C



January 2019 95

7.6.2 Hourly Peak Demand for Heating and Cooling Load

Reference models 'Critical room'

Thermal Zone Heating Cooling

[kW/m²/year] [kW/m²/year]

REF1 - Offices S 0,038 0,012 REF2 - Offices N 0,037 0,016 REF3 - Meeting rooms 0,040 0,000 REF4 - Small meeting rooms 0,240 0,073

Table 7-39 Alfa: Hourly peak load for heating and cooling for reference models

Baseline 14-zone model 12-zone model

Thermal Zone Heating Cooling Thermal Zone Heating Cooling


1 Offices SW 0,023 0,045 1 Offices S 0,023 0,072 2 Offices SE 0,025 0,039 2 Offices N 0,033 0,039 3 Offices NW 0,031 0,011 3 Meeting rooms C. 0,048 0,005 4 Offices NE 0,035 0,006 4 Small meeting rooms S 0,102 0,075 5 Meeting rooms C. 0,048 0,020 5 Other N 0,061 0,012 6 Small meeting rooms S 0,102 0,075 6 Other W 0,059 0,045 7 Other N 0,061 0,012 7 Other W 0,036 0,055 8 Other W 0,059 0,045 8 Other E 0,042 0,086 9 Other W 0,035 0,056 9 Other C. 0,000 0,024 10 Other E 0,042 0,087 10 Lounge/Kitch. N 0,079 0,161 11 Other C. 0,000 0,031 11 Lounge/Kitch. E 0,046 0,178 12 Lounge/Kitch. N 0,079 0,164 12 Toilets C. 0,029 0,000 13 Lounge/Kitch. E 0,046 0,178

14 Toilets C. 0,029 0,000

Table 7-40 Alfa: Hourly peak load for heating and cooling for 14- and 12-zone models

5-zone model IW NIW

Thermal Zone Heating Cooling Heating Cooling

[kW/m²/year] [kW/m²/year] [kW/m²/year] [kW/m²/year]

1 Perimeter S 0,028 0,074 0,023 0,071

2 Perimeter N 0,037 0,041 0,030 0,039

3 Perimeter E 0,045 0,094 0,044 0,092

4 Perimeter W 0,051 0,037 0,044 0,038

5 Core 0,017 0,039 0,014 0,047

2-zone model IW NIW

Thermal Zone Heating

[kW/m²/year]

Cooling

[kW/m²/year]

Heating

[kW/m²/year]

Cooling

[kW/m²/year]

1 Perimeter 0,036 0,066 0,029 0,065

2 Core 0,029 0,041 0,017 0,045

1-zone model IW NIW



1 Perimeter 0,040 0,069 0,027 0,068

Table 7-41 Alfa: Hourly peak load for heating and cooling for 5-, 2- and 1-zone models



Reference models 'Critical room'

Thermal Zone Heating Cooling


REF1 - Office NW-NE 0,041 0,034

REF2 - Office SW-SE 0,036 0,027

REF3 - Small meeting r. SW 0,068 0,100

REF4 - Meeting room SE-NE 0,203 0,271

REF5 - Meeting room NE 0,101 0,012

Table 7-42 Beta: Hourly peak load for heating and cooling for reference models

Baseline 14-zone model 12-zone model

Thermal Zone Heating Cooling Thermal Zone Heating Cooling


1 Offices W 0,038 0,012 1 Offices W 0,038 0,011

2 Offices N 0,042 0,000 2 Offices NE 0,044 0,010

3 Offices E 0,040 0,000 3 Offices SE 0,031 0,025

4 Offices SE 0,031 0,028 4 Meeting rooms S-SW 0,163 0,170

5 Meeting rooms S 0,143 0,037 5 Meeting rooms W 0,106 0,000

6 Small meeting rooms SW 0,172 0,349 6 Small meeting rooms C. 0,128 0,033

7 Meeting rooms W 0,106 0,000 7 Lounge/Kitch. S 0,045 0,067

8 Small meeting rooms C. 0,128 0,029 8 Toilets C. 0,122 0,000

9 Lounge/Kitch. S 0,045 0,079 9 Other S 0,065 0,027

10 Toilets C. 0,124 0,000 10 Other N 0,058 0,079

11 Other S 0,064 0,139 11 Other C. 0,000 0,000

12 Other N 0,057 0,094 12 Other C. 0,000 0,019

13 Other C. 0,000 0,000 14 Other C. 0,000 0,019

Table 7-43 Beta: Hourly peak load for heating and cooling for 14- and 12-zone models

5-zone model IW NIW



1 Perimeter S 0,067 0,011 0,051 0,016

2 Perimeter N 0,055 0,000 0,052 0,000

3 Perimeter E 0,038 0,004 0,038 0,006

4 Perimeter W 0,053 0,060 0,044 0,061

5 Core 0,114 0,000 0,040 0,000

2-zone model IW NIW



1 Perimeter 0,030 0,056 0,029 0,057

2 Core 0,029 0,000 0,040 0,000

1-zone model IW NIW



1 Perimeter 0,023 0,058 0,022 0,057

Table 7-44 Beta: Hourly peak load for heating and cooling for 5-, 2- and 1-zone models

Chapter 8: Appendix: Literature Review: Office Internal Loads’ Profiles

January 2019 97

8 APPENDIX: LITERATURE

REVIEW: OFFICE INTERNAL

LOADS’ PROFILES

8 APPENDIX: LITERATURE REVIEW: OFFICE INTERNAL LOADS’ PROFILES97

8.1 PUBLISHED PREDEFINED INTERNAL LOAD SCHEDULES ............................................ 98

8.1.1 Guidelines in Denmark .................................................................................................... 98

8.1.2 Guidelines Abroad ........................................................................................................... 99

8.1.3 Discrepancies Between Actual and Predefined Schedules ............................................ 101

8.2 OCCUPANTS’ BEHAVIOUR INFLUENCES (INCLUDING THE USE OF APPLIANCES) .... 102

8.2.1 Methods for Detecting Occupancy Presence and Count ............................................... 103

8.2.2 Plug Load Metering ....................................................................................................... 108

8.3 SUMMARY OF THE FINDINGS OF THE LITERATURE REVIEW ................................... 109



8.1 Published Predefined Internal Load Schedules

By having satisfactory predefined profiles, the building design simulations can result in more

accurate predictions. There are only a few internal load profiles for occupancy and equipment

available online. One Danish guide is analysed in more detail, while some other international

standards are mentioned briefly.

The internal load profiles can be expressed in tables or graphs, and they consist of the hourly

factors for 24 hours. Abushakra et al. (2004) overviewed the existing literature and stated what

the Typical Load Shapes (TLS) are related to the internal load factors and schedules:

they […] “are hourly profiles (i.e., 0-23, or 1-24) that can be used in energy and cooling

simulation programs. These TLSs are usually either normalised as a fraction of one (0-1)

(where the user can multiply the resulting hourly schedule by the installed maximum intensity

of internal loads) or divided by the maximum use or the area.” [43]

8.1.1 Guidelines in Denmark

For the Danish industry, the guidelines for indoor climate calculations [7] are freely available

online and should be used in the design phase of internal loads for occupancy and equipment

profiles. The guide displays the profiles for different room types, with standard prerequisites as

loads per m² per person, and the typical equipment used. There are three categories for all

schedules: Low, Regular, and High level.

For these study cases, office buildings, the available profiles for this category are as follow:

• Multiple Person Offices;

• Single Offices;

• Meeting Rooms.

The example of people and plug load profiles for a multiple person office is shown in Graph

8-1 below. The high profile has the factor of 50% in the morning, lunchtime and last hour of

the day, while most of the time has 100% occupancy. The medium profile has 40% and 80%

respectively, while low level drops to 30 % and 70%. In all three cases, equipment schedules

omit the lunch break.


January 2019 99

Occupancy Profiles

Plug Load Profiles

a) High Load b) Regular Load c) Low Load

Graph 8-1 An open plan office profiles for occupancy and plug load for a workday from the Danish

guidelines [7]

For single office case, the office hours are set from 10 to 18 with 2 h interval, but the loads for

the day remains 100%, except the lunch break - it is suggested at 50%. Again, the plug load

profiles neglect the lunch time. Meeting room profiles are the same for occupants and plug

loads, having three 2 h peaks and two breaks with similar factors as for the multiple person

offices.

None of the scenarios for all three office types had equipment load assigned at night. Only

workdays are designed, while the weekends and holidays have no inputs.

SBi 213 “Guidelines for Buildings' Energy Calculations” (Danish Building Research Institute)

is made to comply with Dainsh Building Regulations. This guide defines the occupation time

to be 45 hours/week for other buildings, typically to be used in, e.g. offices, schools, workshops

and day-care centres. The occupation time is the period when the building is on duty for its

main intent and installations for heating, ventilation and lighting are working normally. The

working time is assumed to be from 8-17 five days a week. [44]

8.1.2 Guidelines Abroad

While reviewing the literature for a research topic, a few international guidelines were found

including the standardised occupancy load factors.

ASHRAE 90.1-2004 includes standardised internal load factors recommended for various

building types for weekday, Saturday and Sunday. Though, these factors for different office

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layout types are neglected. The presented schedules for occupancy, lighting, receptacle6, HVAC

system and service hot water are from ASHRAE Standard 90.1-1989 and its addendums. [45]

Research by Duarte et al. shows a reduction of 46% to the measured private office for an

average day and 12% for the open plan offices when compared to the ASHRAE profiles. [46]

ASHRAE Research Project RP 1093 developed the hourly schedules for receptacle load for

small, medium, and large office buildings. It is observed that the unoccupied time the plug load

usage minimum factor is 0,12 and 0,06 for weekday and weekend accordingly. Claridge et al.

study recommend modifying the predefined profiles, especially for the weekend use. The

electricity consumption factor does increase by the size of the building. When compared to

Danish industry guidelines, the size of the building can be associated with Low, Regular and

High level. [47]

Massachusetts is the most energy efficient state in the U.S for the eight straight years. Mass

Save is a collaborative working closely with the Massachusetts Department of Energy

Resources guiding through energy savings. [48] The technical review guide developed by the

energy efficiency program administrators provides the hourly simulation guidelines to improve

modelling accuracy. They have designed the various schedules for occupancy, lighting and

equipment for different space types for building types such as offices, elementary and high

schools. [49]. Graph 8-2 shows the daily profiles for an open plan office for occupancy and

plug loads. There is standby load modelled for the night and weekend use.

6 Receptacle loads – describes the amount of load produced by equipment plugged into an outlet (also referred to

as plug loads), it also produce heat and consume electricity.[73]


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Occupancy Profiles

Plug Load Profiles

a) Monday-Friday b) Saturday c) Sunday

Graph 8-2 An open plan office profiles for occupancy and plug load from [49]

Some of the BPS software has predefined occupancy and plug load schedules readily available

for the user. D. Ioannidis mentions EnergyPlus and OpenStudio offering templates of internal

loads’ profiles to be used during the first stages of a building design [50].

8.1.3 Discrepancies Between Actual and Predefined Schedules

Several studies have examined the difference between the predefined internal loads' profiles to

the actual metered data. Mostly, the findings where that there are not enough loads accounted

for in the standard schedules. The lack of occupancy information during the preconstruction

phase is one of the most common issues when selecting design assumptions [31].

This article [51] found that the presence of the occupants is highly influential towards energy

consumption. It was concluded that the increase of one-third could be added to design energy

usage, if the building users are the wasteful energy type, and save one-third for energy

conservation behaviour.

A study was conducted by [28] examining the correlation between the occupancy levels for

compliance predictions and electricity consumption. An office building in central London was

in-depth monitored for electricity consumption for lighting, small power and catering

equipment, and the occupancy numbers were recorded manually in half-hour intervals. The

plug load demand followed the monitored users’ level accordingly. However, the profiles had

only little correlation to the predefined schedules. Menezes et al. pointed out the occupancy

hours to have the highest impact on accurate predictions.

[50] had examined how actual occupancy measurements fit with the predefined profiles.

Comparison outcome indicates that the templates currently used by some building simulation

tools (EnergyPlus, OpenStudio) may be inaccurate with regard to the ground truth in certain

cases, thus introducing errors in simulation results. The analysed meeting room had up to 59%

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difference for the workday, and 67% for the weekend. The other space analysed, a corridor, had

one of the programs underestimating the workday by 52% and overestimating the weekend by

75%.

Few studies have been conducted regarding ASHRAE recommended profiles. [52] appreciates

the availability of the designed occupancy and plug load schedules when the actual data cannot

be known but the possibility has been identified of up to 40% variation comparing to real

monitored results. A study conducted by [46] investigated a large commercial office building,

and the reduction of up to 46% for private offices and 12% for open-plan offices in occupancy

patterns have been found when compared with the standardised occupancy schedules in

ASHRAE. The suggestion is to conduct two simulations with typical low and high occupancy

profiles to obtain the expected range of energy usage over time.

It seems that the incorporation of real accurate occupancy information in the templates to be

used in BPS software can enhance their effectiveness and performance. [50]

8.2 Occupants’ Behaviour Influences (Including the Use of

Appliances)

In general, occupants’ behaviour influences the thermal condition indoors in two ways: passive

and active behaviour. By being present in the space, occupants produce heat and carbon dioxide,

and the active interactions with the building can be such as the use of window-opening, light-

switching, blind-adjusting, system and set-points for HVAC, and the use of electricity via plug

loads. [29] [53] [30] [54]

Figure 8-1 Occupants’ interactions influencing building energy consumption, adapted from [29]

As can be seen in Figure 8-1, occupants are affecting building energy consumption by being in

the space, same as electrical equipment. For this paper, equipment that is plugged into the

electrical outlet is referred to as plug loads. For building simulations, the internal load profiles

are used for occupancy and receptacle load schedules, and some simulation software also needs

hourly inputs for lighting usage. The values are entered as 24-hour load factors for the different

day times as weekday, weekend and holiday or such [47]. To ensure that the simulation outcome


January 2019 103

is realistic, the predefined industry standards might need to be modified to fit the specific case

[50]. This study is focused on occupants and plug load factors and investigating if there are any

discrepancies between the predefined standard schedules to the field observations.

8.2.1 Methods for Detecting Occupancy Presence and Count

Figure 8-2 Spatial-temporal properties of occupancy measurement adapted from [55]

To identify the optimal occupancy measurement system, the granularity of extracted

information has to be specified. As seen in Figure 8-2, there are different levels of human

sensing resolution:

• Presence – at least one occupant in the zone;

• Count – the ‘numbers’ of occupants in a specific zone;

• Identity – identification of occupants.

For this study of actual internal occupancy loads the resolution is limited to the ‘Count’

approach, thus without identifying users, there are no privacy issues. In connection with spatial-

temporal properties, as the data to be gathered in the determined space with certain time

intervals, the successful occupancy sensing can be achieved [55]. As noted by Melfi et al.

(2011), to indicate the accuracy or error of the sensing methods, they can be compared against

the ground truth [56].

It is decided not to focus on how the different human-sensing techniques work, but shortly

review them for basic understanding. Most commonly used sensing systems in the office

buildings are:

• Manual observations;

• CO2 based detection system;

• PIR (Passive)/ IR (Active) Infrared Tracking;

• Radio-frequency identification (RFID);

• Ultrasonic sensors;

• Wireless communication;

• Image recording – RGB, thermal and depth imaging.



8.2.1.1 Manual observations

The simplest and most direct occupancy metering can be done through manual observations.

Depending on the size of the building or space, it might be a complicated task, and the number

of workers needed to perform the observation could increase significantly. In [28] study, the

monitoring of occupants was conducted via half-hourly walkthrough inspections. The study

done by [57] had focused on single offices, and the occupants manually recorded their presence.

The method is time-consuming for the test makers, as they must be present in the monitored

space constantly.

8.2.1.2 CO2 Based Detection System

While using the CO2 measurements it is possible to detect and calculate the occupant number,

the results are with delay and can be influenced by variables such as ventilation rate or

window/door positions, and the carbon dioxide concentration in the ambient air next to the

building must be known [58]. The CO2 sensors are strongly influenced by the local presence of

occupants, such as proximity and activity level, leading to uncertainties and estimation errors

as reviewed by [51]. Yan et al. study also observed the delay between the actual occupancy

and the increase of carbon dioxide and mentioned that for open-plan offices the method might

not be suitable [30]. Several types of research [59],[58] and [55] support the CO2 metering

having up to 20 min delay, indicating that occupants could already have changed their presence

state in the room.

8.2.1.3 Active/Passive Infrared Tracking

The passive infrared (PIR) sensors can be used for detecting people by the infrared light

radiating from objects in its field of view. However, the binary output allows only to determine

the if space is occupied or not, and the relatively stationary people might not be detected.

Differently, from PIR, the active infrared IR sensors work by the emitter releasing the infrared

light signal to the detector (or reflector), and when the signal is interrupted, the count is made.

The active IR sensors, otherwise called light beams, can count the users passing by. [51], [60]

8.2.1.4 Radio-Frequency Identification (RFID)

RFID is an object detection technology based on signal detection and data transmittance

through a radio frequency. An RFID system consists of an antenna, a reader, and tags. The

system can detect occupants within the reader’s detection range. The RFID can be used for both

detection and counting of users, despite the being stationary or moving. However, previous

studies have reported that the users must wear tags all the time to be identified which cannot be

guaranteed. [51], [60], [61]


January 2019 105

8.2.1.5 Ultrasonic Sensors

These types of sensors can detect human movements through the use of a single transducer to

send the ultrasonic signals and to receive the echo. Not suitable for people counting, as the

output is binary and it can be sensitive to motion of inanimate objects leading to errors and false

readings. [62], [55], [60]

8.2.1.6 Wireless Communication

Another detection technique relates to the position of the user by the use of wireless

communications: WiFi, Bluetooth or even global position systems (GPS) [51]. The main

advantage of using wireless communication as a proxy for human occupancy presence

identification is its availability [59].

Commonly in an office building, the WiFi networks are protected, where employees have to

connect using their username and password. The user count can be achieved if the occupants

use the secured networks every time while at work. As more devices can be connected to WiFi,

it is important for the system to know the laptops’ addresses, or if the user has more than one

phone, which device is his representative one [63]. The accuracy of utilising the existing Wi-Fi

signal networks inside buildings to profile the number of occupants is about 80% [52]. [59]

states that this method predicts actual occupancy with higher certainty compared to CO2

detection.

The mobile phone tracking can be performed by the Bluetooth Low Energy (BLE) technology.

The special app must be installed, and the signal must be kept on all the time. If occupants are

not always using equipment, BLE signal might not always be discovered and matched, same as

WiFi.[61]

Even though the methods are low intrusive, the privacy concerns might not satisfy the users.

8.2.1.7 Image Recording

Human detection by images through devices such as cameras can be used for identifying the

occupants’ location, number and activity as well as identifying the users. The different imaging

methods are used for various purposes already, such as CCTV cameras for surveillance. An

example of RGB, depth, and thermal images for an indoor space is shown in Figure 8-3.



(a) RGB (b) Thermal (c) Depth

Figure 8-3 Example of indoor scenes with RGB, Thermal and Depth cameras [64]

RGB Imaging

The study done by [62] used the fisheye lens camera for taking low-resolution photos every 1

min. The pictures conversion to the pixelated images utilises the Matlab’s Image Recognition

Toolbox, where they are interpreted and can detect users present. However, only the presence

detection was used, and the count was not observed. [52] and [65] used the image captures

obtained using conventional cameras to identify the actual occupancy in the space.

Thermographic Imaging

The infrared thermographic camera works in similar ways to a regular imaging camera, but

instead of dealing with visible light, it processes the infrared light mostly emitted by the warm

human bodies [60]. If using low-resolution images, only blob detection is possible (not possible

to extract identity of users) [66]. If compared to RGB cameras, the advantages can be seen such

as the night scene imaging, the separation between the background and people [67].


January 2019 107

Depth Imaging

Recently, several authors [34], [35], [60], [68] have studied the occupancy detection and

counting by utilising a RGB-D (Depth) sensor. The anonymity is preserved by extracting

images showing monochrome ‘blobs’ instead of an actual RGB picture, if certain algorithms

are applied during processing. If the camera is installed vertically as mounted on a ceiling

(Figure 8-4), better detection of human heads can be achieved by reducing the occurrence of

occlusions.

Figure 8-4 Sketch of depth camera placement in the zenithal position [34]

8.2.1.8 Summary of the Occupancy Detection Systems

The occupancy detection systems are summarized in Table 8-1, where the characteristics such

as presence, number of occupants and privacy matters are pinned down. The thesis by

Ekwevugbe summarized various occupancy detection systems found in most office buildings

that were already implemented. He concludes the CO2 as a measure to have limitations, as being

influenced by factors such as window openings and the delayed result. The video sensing would

almost always create privacy concerns, and the IT infrastructure detection would not account

for users not using computers or phones or having multiple devices on at the same time. [69]

Methods Presence Count Privacy

Manual Observations ✓ ✓ ✗

CO2 ✓ ✗ ✓

PIR (Passive Infrared) ✓ ✗ ✓

IR (Active Infrared) ✓ ✓ ✓

RFID ✓ ✓ ✗

Ultrasonic ✓ ✗ ✓

Wireless Communication ✓ ✓ ✗

Image Recording ✓ ✓ ?

(✓ – yes, ✗ - no, ? - yes/no)

Table 8-1 Summary of the occupancy detection systems



From the literature reviewed, the accuracies observed using the previously mentioned

techniques depend on space (being closed or open office), occupants’ active behaviour as

window opening and such. The CO2 method might have accuracy between 69% and 81% ([57],

[58], [59]. The WiFi can be attributed to have up to 80% accuracy ([52]), while Bluetooth

beaconing together with plug metering (fusion of two sensing methods) suggests 87–90%

accuracy [61]. Most of the analysed studies have chosen to use the imaging sensing for a ground

truth measurement as the observed accuracies are satisfactory high - 95% accuracy [50].

The case study must be analysed to choose the most suitable detection system. The properties

of analysed space, such as the size, single or open-plan offices, possibilities of entering and

exiting the room, must be taken into consideration.

8.2.2 Plug Load Metering

To talk about metering the plug loads, there is a need for a clear definition of what it is. Any

device that can be plugged into an electrical socket is a plug or receptacle load. These loads do

not include heating, ventilation and air conditioning (HVAC) or lighting systems. Nowadays,

in the office buildings, the plug loads account for between 9% to 28% of the total electricity

consumption [70]. If the plug loads are overestimated, it could lead to oversized electrical

infrastructure, decreased heating needs and increased cooling loads. [70] [71]

The paper by Sheppy et al. shows measured evidence that actual peak PPL (Plug and process

loads) densities are significantly lower (by a factor of 5 to 10) than what is typically requested

in leases by the design phase documents. To add, the metered data revealed the case buildings

using electricity during unoccupied hours, suggesting to control plug load with power strips.

[71]

Additionally, in 2015, Lamano reviewed the current plug load standards from various sources

and summarised that the power use could be as low as 2,7 W/m² with advanced energy efficient

equipment and as high as 21,5 W/m² for intensive office areas [72]. He also mentions that

ASHRAE suggests the peak load consumption be set at 10,8 W/m², that would equate to

approximately two monitors per workstation.

Harris and Higgins (2013) introduced the methodology of how the energy use of the office plug

load should be reported. It was emphasized that when performing the building energy and

performance simulations, it is important to include accurate assumptions related to plug

maximum load power and its schedule. Same as in Danish guidelines, this guide includes


January 2019 109

preliminary ranges for High, Medium and Low. The day types are a weekday, Saturday and

Sunday/Holiday. [26]

8.3 Summary of The Findings of The Literature Review

The research between the published predefined internal load schedules and the project-specific

profiles can lead to the improvement of the standard profiles resulting in higher accuracy of the

building simulation outputs. [50] The publicly available Danish industry guidelines BI-SBi

suggest three levels profiling for High, Regular and Low users. As for open offices, they

provided profiles for occupancy and plug load with no consideration for weekend and night

use.

Previous studies had examined the modelling of people and plug load profiles effect on energy

consumption. The influence was observed in all the cases, due to overestimation or

underestimation. By creating the design phase BPS models, the assumptions regarding internal

gains and plug loads led to inconsistency between the modelled and built project [32]. By

understanding occupants’ passive and active behaviour, higher accuracy of simulation inputs

regarding their presence and use of appliances can be modelled.



9 APPENDIX: MEASUREMENT

CAMPAIGN: OPEN-PLAN

OFFICE INTERNAL LOADS’

PROFILES

9 APPENDIX: MEASUREMENT CAMPAIGN: OPEN-PLAN OFFICE INTERNAL

LOADS’ PROFILES ............................................................................................................... 110

9.1 DATA ACQUISITION AND ANALYSIS PROCEDURE .................................................... 111

9.1.1 Case Study - Assessment and Selection ......................................................................... 111

9.1.2 Data Collection Strategy ............................................................................................... 112

9.2 DATA PROCESSING AND VISUALIZATION – OCCUPANCY COUNTING ...................... 115

9.2.1 Data Extraction ............................................................................................................. 116

9.2.2 Data Conversion ............................................................................................................ 116

9.2.3 Data Processing ............................................................................................................. 116

9.2.4 Peak Load Data ............................................................................................................. 119

9.2.5 Hourly People Load Profiles ......................................................................................... 119

9.2.6 Higher Resolution People Load Profiles ....................................................................... 120

9.3 DATA PROCESSING AND VISUALIZATION - PLUG LOAD METERING ........................ 122

9.3.1 Data Extraction ............................................................................................................. 122

9.3.2 Data Conversion ............................................................................................................ 122

9.3.3 Data Processing ............................................................................................................. 122

9.3.4 Data Processing by Energy User Type during the Metering Campaign ....................... 125

9.3.5 Low, Medium and High Profiles versus the Workday Profile ....................................... 127

9.3.6 Final Workday and Weekend Consumption Profiles ..................................................... 129

9.3.7 Peak Load Data ............................................................................................................. 129

9.3.8 Hourly Equipment Load Profiles ................................................................................... 130

9.3.9 Higher Resolution Equipment Load Profiles ................................................................. 132

9.3.10 Data from the Electric Utility Company.................................................................... 136

Chapter 9: Appendix: Measurement Campaign: Open-Plan Office Internal Loads’ Profiles

January 2019 111

9.1 Data Acquisition and Analysis Procedure

9.1.1 Case Study - Assessment and Selection

As for office buildings, Danish industry guidelines propose profiles for three room types. In the

‘Pakhusene’ building, the room type areas were analysed (Beta case building in the previous 2

Thermal Zoning Impact on BPS Model Results).

Office Room Type Multiple Person Offices Single Offices Meeting Rooms

% of the total building area 30 13 5

Table 9-1 Three office room types and their respective % of the total ‘Pakhusene’ building area

As can be seen from Table 9-1, landscape - or open - space offices are the main office room

type in the case study office building. For this study, one open plan office was chosen as the

representative type of room (MOE office) on the first floor, shown in Figure 9-1. The

measurement campaign took place in autumn 2018. The occupancy metering started on 23rd

October, the plug load metering began on 31st October, and both ended on the 2nd of December.

Figure 9-1 1st-floor plan of the ‘Pakhusene’ building, indicating the chosen open plan office as the

case study MOE

There are 18 designed and available workstations in the corner office. Though, at the time of

measurements, a maximum of 16 places were used. The area of the space is approximately 142

m², resulting in 7,9 m²/occupant for 18 users and 8,9 m²/occupant for 16 users. As each

workstation can accommodate one user, the workplace density is the same for occupants and

workstations.

The users of this specific office space are employees of the consultancy company within

Buildings, Energy & Industry and Infrastructure. The users could be described mostly as

sedentary type, working at their workstations, with some meetings or ‘out of office’ tasks. Few

employees are having the second working space outside the MOE office in Aarhus. This space



was selected due to the representative behaviour for open space office, according to the

employee statements.

Walkthrough survey was conducted to gather information about present electrical office

inventory. Specific appliances for the workstations were investigated and no commonly used

items, such as commercial printers, were found in the office space. Architects, engineers and

consultants tend to work using desktop screens next to their laptop. Table 9-2 below shows the

equipment type and quantity for each workstation in the office. The items such as desk lamps

and phone chargers are not constant, and their count differs from day to day.

Equipment Type Workstations

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Total

Desktop Computer 1 1 1 3

Laptop + Dock 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15

LCD Screen 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2 27

Desk Lamp 1 1 2

Phone Charger 1 1 1 1 1 5

Table 9-2 Summary of the case study MOE office equipment

9.1.2 Data Collection Strategy

9.1.2.1 Occupancy Counting

The method used for metering the occupancy in the landscape office was chosen to be the image

recording technique. The main reasons were:

• The available sensors at the case study office were CO2 meters and PIR sensors at the

time of measurement campaign. For the intended study purpose data with the delay

or binary output was not acceptable. Therefore none of the existing systems were fit

to be chosen;

• Literature research revealed that imaging sensors were used as a ground truth due to

its high reliability.

Equipment accessibility from the AAU University was limited, and the obtainable image

recordings at the time had been the web-cameras. The decision was made to use the RGB

imaging with blurred pictures to minimise the concern of privacy. No processing software was

used, and the images were kept at JPEG format, to be later analysed manually.

Logitech C920 PRO HD Webcams were used in the field measurements. Figure 9-2 is a sketch

of the camera’s position towards the workstations.


January 2019 113

Figure 9-2 Vertical placement of the camera

Figure 9-3 The camera set up on site at the MOE office. Red circles indicate the cameras

Figure 9-3 demonstrates the actual camera placement on site. To be able to capture images with

time interval automatically, each camera was connected to the separate laptop, which was

placed at the ceiling level as well. The hidden set-up guaranteed no unwanted interaction by the

occupants. The script for Logitech Webcam Software was used for setting up the capture time

interval every 5 min. After, the manual processing of occupant count from the pictures took

place. All the three laptops had a connection to WiFi, and remote access to the computers and

the pictures was established via TeamViewer. Test of the device was a crucial step in order not

to lose the data and to ensure accurate set-up performance.



Figure 9-4 An example of the taken office picture during the measurement campaign

The example of the pictures taken during the measurement campaign can be seen in Figure 9-4.

The occupants can be seen in the images, but their features are not apparent.

9.1.2.2 Plug Load Metering

Plug load meters or energy meters were used to collect the information about actual usage for

each analysed workstation. Due to the limited availability of the plug meters, it was chosen not

to perform device level metering for disaggregated power consumption, only the aggregated

load from each metered station.

The METZ CONNECT (previously called BTR NETCOM) energy metering system was

selected for power measurements. To obtain the data for the plug load profiles creation, 8

available individual energy meters were used, one per workspace. They were connected to the

transmitters and data was received every 1 minute. The set-up was made to log the

measurements every 5 minutes, and to report to the computer where the software was installed.

The data was saved as a text file, later to be converted to an Excel file.

The equipment used for plug metering can be seen in Figure 9-5. In total there were 4

transmitters (white boxes on the left side), with 4 sockets attached. Each measured workstation

was connected to one of these sockets. The transmitters were connected to the logger (white

box on the right side), which was linked to the laptop.


January 2019 115

Figure 9-5 The plug metering system

The energy metering equipment was kept discrete. There was a plastic box located in between

two middle workstations containing the meters, see Figure 9-6. Extension cords were running

from the plug metering system to the ceiling, where they were distributed to the rest of the

office. Each workstation’s electrical equipment was connected into one plug outlet multiplier.

Figure 9-6 The setup of plug metering system. Left – the box containing the transmitters and logger,

right – extension cords layout from the ceiling to the box.

9.2 Data Processing and Visualization – Occupancy Counting

After the data was gathered, it had to be treated to get the wanted output for analysis. The data

processing steps were defined for both occupancy counting and plug load metering:



STEP 1. Data Extraction

STEP 2. Data Conversion

STEP 3. Data Processing

STEP 4. Peak Load Data

STEP 5. Internal Loads Factors

9.2.1 Data Extraction

For the occupancy measurements, the data extracted were JPG format file pictures. The

software was set to name the pictures in the order they were taken.

The check for errors was performed. During the whole metering period, all three cameras

performed with no issues and the data received was complete (the pictures were taken every 5

minutes 24/7 with no breaks).

As the Daylight Savings occurred on Sunday, the 28th October, the one hour was deleted from

the Sunday night to keep the 24 daily hours.

9.2.2 Data Conversion

The occupancy count was obtained by reviewing the images taken by all three cameras

separately and manually inserting the number into the Excel sheet.

9.2.3 Data Processing

The measured data was sorted in different day types as workdays and weekends to calculate the

average values. For occupancy count, the number of people was added from all three cameras

for each time step. All workdays values from the same time step were summed up and divided

by the total count of values. Table 9-3 shows an example of occupancy data, and how the data

from same day type were averaged.

23.10 24.10 25.10 26.10 29.10 30.10 31.10 Averaged

value: Time Tuesday Wednesday Thursday Friday Monday Tuesday Wednesday

13:00 10 6 8 5 12 8 7 8

13:05 7 6 8 2 10 10 8 7,29

Table 9-3 An example of occupancy averaging between same day type

Graph 9-1 contains the 5-min interval average occupancy count (red line) plotted together with

separate days measurements. The individual day profiles have higher fluctuations, as the

occupancy patterns daily are very diverse.


January 2019 117

Graph 9-1 Averaged versus measured data for 7 randomly selected workdays with a 5-min interval

As the data gathered was with a predefined interval of 5 min, the averaging of data was done to

convert it to the hourly profiles. From 288 daily data points only 24 were left, as the 12 values

every 5 min were averaged into a single data point. Graph 9-2 shows the averaging performed

for the measured occupancy data for two random workdays. The averaged patterns follow the

actual data patterns, while the fluctuations are smoothed out.

Graph 9-2 Averaged hourly (grey columns) versus 5-min (black dashed line) measured data for two

randomly selected workdays

The daily metered occupancy was plotted for the metering period for the workdays with 1-hour

interval, and no occupants were detected during the weekends. Table 9-4 contains all the graphs

for each measured workday. All Mondays were plotted together with the Mondays’ averaged

count, and the same was performed for all other weekdays. The last row “Workday Averages”

shows each workday average with the total workdays averaged count. A similar pattern was

observed throughout the whole metering period.

0

5

10

15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0

Peo

ple

Lo

ad

[N

o]

HourMeasured Data Average

24

0

5

10

15

1 5 9 13 17 21

Peo

ple

Lo

ad

[N

o]

Hour

0

5

10

15

1 5 9 13 17 21

Peo

ple

Lo

ad

[N

o]

Hour



Monday Tuesday

Wednesday Thursday

Friday Saturday / Sunday

Workday Averages

Table 9-4 Hourly occupancy graphs for each weekday and their averages

0

3

6

9

12

1 3 5 7 9 11 13 15 17 19 21 23

Peo

ple

Lo

ad [

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]

Hour

0

3

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ple

Lo

ad [

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]

Hour

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12

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Peo

ple

Lo

ad [

No

]

Hour

0

3

6

9

12

1 3 5 7 9 11 13 15 17 19 21 23P

eo

ple

Lo

ad [

No

]

Hour

0

3

6

9

12

1 3 5 7 9 11 13 15 17 19 21 23

Peo

ple

Lo

ad [

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ple

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ad [

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Peo

ple

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January 2019 119

9.2.4 Peak Load Data

To determine the factors as 1 or 100% for occupancy profiles, the maximum intensity of internal

loads was found.

Peak value

Occupancy [No.] 16

Table 9-5. The peak loads for occupancy of the examined open plan office

The peak load presented in Table 9-5 was determined by the number of employees in the

measured office, later used for the people profiles as a peak load (100%).

9.2.5 Hourly People Load Profiles

After the peak load was determined, the percentage values were calculated as the fractions of

the peak load. Figure 9-7 displays the final profile for the workday. No occupants were detected

during the weekend.

Time Occupant

count Percentage

01:00 0,0 0%

02:00 0,0 0%

03:00 0,0 0%

04:00 0,0 0%

05:00 0,0 0%

06:00 0,0 0%

07:00 0,0 0%

08:00 1,8 12%

09:00 6,4 40%

10:00 7,2 45%

11:00 7,6 48%

12:00 6,1 38%

13:00 6,3 39%

14:00 7,8 49%

15:00 7,0 44%

16:00 5,2 33%

17:00 1,8 11%

18:00 0,3 2%

19:00 0,0 0%

20:00 0,0 0%

21:00 0,0 0%

22:00 0,0 0%

23:00 0,0 0%

24:00 0,0 0%

Peak

Load 16

Figure 9-7 Final hourly occupancy profile for the workday

The created profile schedule is plotted on the graph using the calculated percentages from the

peak load of 16 occupants.

0%

20%

40%

60%

80%

100%

1 3 5 7 9 11 13 15 17 19 21 23Hour

Workday

Weekend



9.2.6 Higher Resolution People Load Profiles

Below, the profiles for the higher resolution were created with 5-min, 15-min and 30-min

intervals. In all the tables, the top row indicates the time as the hour of the day, the second row

shows the occupancy profile expressed in 1-hour intervals, followed by more detailed

occupancy profiles.

End Hour 1-6 7 8 9 10 11 12

1-hour 0,0% 0,1% 11,5% 40,0% 45,3% 47,7% 37,9%

5-m

in

--:05 0,0% 0,0% 2,8% 28,0% 42,2% 45,9% 48,3%

--:10 0,0% 0,0% 4,3% 32,1% 39,7% 46,6% 50,6%

--:15 0,0% 0,0% 5,8% 34,3% 39,2% 45,9% 50,0%

--:20 0,0% 0,0% 6,7% 36,0% 42,9% 47,2% 50,0%

--:25 0,0% 0,0% 6,5% 35,6% 46,8% 46,3% 51,7%

--:30 0,0% 0,0% 8,8% 40,5% 45,5% 45,9% 47,4%

--:35 0,0% 0,0% 11,0% 43,8% 47,8% 50,2% 39,7%

--:40 0,0% 0,0% 13,6% 45,7% 46,1% 49,4% 32,3%

--:45 0,0% 0,0% 16,4% 45,7% 48,9% 47,6% 27,8%

--:50 0,0% 0,0% 16,8% 45,9% 48,3% 49,8% 20,3%

--:55 0,0% 0,4% 21,1% 45,3% 49,4% 51,3% 18,5%

--:00 0,0% 0,9% 24,8% 47,0% 46,3% 45,9% 18,3%

End Hour 13 14 15 16 17 18 19 20-24

1-hour 39,1% 48,7% 43,8% 32,6% 11,2% 1,8% 0,2% 0,0%

5-m

in

--:05 18,8% 48,7% 44,2% 39,7% 21,1% 4,7% 0,2% 0,2%

--:10 25,0% 49,8% 43,5% 38,4% 19,4% 4,5% 0,2% 0,2%

--:15 30,6% 50,0% 44,2% 36,6% 15,9% 2,6% 0,0% 0,0%

--:20 34,1% 48,1% 45,5% 35,6% 12,1% 2,2% 0,2% 0,0%

--:25 34,3% 47,8% 42,9% 33,2% 9,5% 2,2% 0,0% 0,0%

--:30 38,8% 50,0% 41,4% 33,4% 10,1% 1,9% 0,0% 0,0%

--:35 44,8% 49,1% 46,1% 33,8% 8,8% 1,1% 0,2% 0,0%

--:40 44,8% 48,7% 42,9% 32,8% 8,6% 0,9% 0,2% 0,0%

--:45 48,3% 49,4% 45,3% 29,7% 8,4% 0,4% 0,2% 0,0%

--:50 49,1% 47,8% 45,3% 28,2% 7,5% 0,4% 0,2% 0,0%

--:55 50,9% 48,1% 43,1% 25,4% 7,1% 0,4% 0,2% 0,0%

--:00 49,4% 46,8% 41,4% 24,4% 5,6% 0,0% 0,2% 0,0%

Table 9-6 Final occupancy profile for the workday, resolution - 5-min

Graph 9-3 Final occupancy profile comparison for the workday between two resolutions – 1-hour

(grey columns) and 5-min (blue dashed line)

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0%

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100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Hour


January 2019 121

End Hour 1-6 7 8 9 10 11 12

1-hour 0,0% 0,1% 11,5% 40,0% 45,3% 47,7% 37,9%

15

-min

--:15 0,0% 0,0% 4,3% 31,5% 40,4% 46,1% 49,6%

--:30 0,0% 0,0% 7,3% 37,4% 45,0% 46,5% 49,7%

--:45 0,0% 0,0% 13,6% 45,0% 47,6% 49,1% 33,3%

--:00 0,0% 0,4% 20,9% 46,0% 48,0% 49,0% 19,0%

End Hour 13 14 15 16 17 18 19 20-24

1-hour 39,1% 48,7% 43,8% 32,6% 11,2% 1,8% 0,2% 0,0%

15

-min

--:15 24,8% 49,5% 44,0% 38,2% 18,8% 4,0% 0,1% 0,0%

--:30 35,7% 48,6% 43,2% 34,1% 10,6% 2,1% 0,1% 0,0%

--:45 46,0% 49,1% 44,8% 32,1% 8,6% 0,8% 0,2% 0,0%

--:00 49,8% 47,6% 43,2% 26,0% 6,8% 0,3% 0,2% 0,0%

Table 9-7. Final occupancy profile for the workday, resolution - 15-min



End Hour 1-6 7 8 9 10 11 12

1-hour 0,0% 0,1% 11,5% 40,0% 45,3% 47,7% 37,9%

30

-

min

--:30 0,0% 0,0% 5,8% 34,4% 42,7% 46,3% 49,7%

--:00 0,0% 0,2% 17,3% 45,5% 47,8% 49,0% 26,1%

End Hour 13 14 15 16 17 18 19 20-24

1-hour 39,1% 48,7% 43,8% 32,6% 11,2% 1,8% 0,2% 0,0%

30

-

min

--:30 30,2% 49,1% 43,6% 36,1% 14,7% 3,0% 0,1% 0,0%

--:00 47,9% 48,3% 44,0% 29,1% 7,7% 0,5% 0,2% 0,0%

Table 9-8. Final occupancy profile for the workday, resolution - 30-min



00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour



9.3 Data Processing and Visualization - Plug Load Metering

Same 5 steps were used for the data processing as for the occupancy counting.

9.3.1 Data Extraction

The BTR plug metering system was configured to save the logged data into a Text Document

file type. The data were registered every 1 minute, with the corresponding time stamp. Each

channel measured was saved as a new text file.

9.3.2 Data Conversion

For the plug load, the impulses were logged from the metering system and the conversion to

energy units as Wh was necessary. The lowest available resolution of energy meter was 0,5 Wh

per 1 impulse per 36 seconds. The example of conversion from impulses to the energy use is

shown in Table 9-9.

Registered Impulse No. Change in Impulses Energy Consumption [Wh]

Time Channel 1 Channel 2 Channel 1 Channel 2 Channel 1 Channel 2

2018.10.31 08:05:00 1338039 7357 8 5 4 2,5

2018.10.31 08:06:00 1338048 7362 9 5 4,5 2,5

2018.10.31 08:07:00 1338057 7367 9 5 4,5 2,5

2018.10.31 08:08:00 1338065 7372 8 5 4 2,5

2018.10.31 08:09:00 1338074 7377 9 5 4,5 2,5

2018.10.31 08:10:00 1338082 7381 8 4 4 2

Table 9-9 Example of the data from the plug metering system conversion to energy consumption

Each channel was converted separately, while the example above is showing two random

stations at a random time. The impulses were logged every 1 minute, with a starting number

being fixed while setting up the system. The change in impulses was calculated as a difference

between the actual and the previous time step. The last step was to multiply the impulse

difference by 0,5 Wh. The last two columns’ numbers were used in later calculations.

9.3.3 Data Processing

The metered data was sorted by day type, workday and weekend. Each measured station was

converted to the hourly profiles for each measured day by adding sixty 1-min measurement

values. From 1440 daily data points, 24 remained.

Table 9-10 is showing the workdays’, and Table 9-11 – the weekend’s hourly data. All graphs

contain data with 1-hour interval, while the data is expressed in watt (symbol: W). The

horizontal axis indicates the hour of the day.


January 2019 123

The daily graphs from ‘Monday’ to ‘Sunday’ contain the daily consumption of all 8 metered

channels, with the ‘red’ line indicating the daily average. The last columns ‘Workday Average’

and ‘Weekend Average’ show the daily averages for each measured week, and the weekly

averages are shown with ‘red’ lines. The bottom rows as ‘Average’ contain the specific

workday averages (as all averages from separate Mondays), with their average plotted with

‘red’ line.



Week Monday Tuesday Wednesday Thursday Friday Workday Average

29.10 30.10 31.10 01.11 02.11

44

05.11 06.11 07.11 08.11 09.11

45

12.11 13.11 14.11 15.11 16.11

46

19.11 20.11 21.11 22.11 23.11

47

26.11 27.11 28.11 29.11 30.11

48

Monday Tuesday Wednesday Thursday Friday

Av

erag

e

Table 9-10 Hourly plug load usage graphs for measured workdays and their averages. X-axis - Hour, Y-axis – Power [W]

0

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1 5 9 13 17 210

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1 5 9 13 17 21


January 2019 125

Week Saturday Sunday Weekend Average

03.11 04.11

44

10.11 11.11

45

17.11 18.11

46

24.11 25.11

47

01.12 02.12

48

Saturday Sunday

Av

erag

e

Table 9-11 Hourly plug load usage graphs for measured weekends and their averages. X-axis - Hour,

Y-axis – Power [W]

9.3.4 Data Processing by Energy User Type during the Metering Campaign

For the workdays, closer examination of the 8 metered workstations was performed to identify

the different users by their energy usage. The averages of 1-min data were calculated for all

stations. Graph 9-6 summarizes the averaged metered plug load consumption of 1-min interval

for a month, with the box and whisker diagram graphically sorting the data through their

quartiles. The ‘x’ marks the mean value.

0

25

50

1 5 9 13 17 21

0

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50

1 5 9 13 17 21

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0

5

10

15

1 5 9 13 17 21

0

5

10

15

1 5 9 13 17 21



Graph 9-6 Measured plug load overview of the 1-min interval energy consumption data of all 8

stations

The hourly usage was counted by summing up every minute’s usage of the same hour. The final

data averaging was also performed in between the measured channels. Graph 9-7 shows the 8

stations average energy use per day, as well as the final averaging.

Graph 9-7 Average energy use for all 8 metered workstations, and their total average, 1-hour interval

(8 channels data – black dashed lines, average – red solid line)

0

20

40

60

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Po

wer

[W]

Hour


January 2019 127

Table 9-12 shows the total energy use per station separately over the whole measurement

period.

Energy Use Average Usage

Station Workday Weekend Total Workday (23 days) Weekend (10 days)

1 11 559 2 419 13 978 503 242

2 2 612 234 2 845 114 23

3 8 714 663 9 377 379 66

4 23 690 5 209 28 899 1030 521

5 2 439 10 2 449 106 1

6 9 780 826 10 606 425 83

7 3 470 13 3 483 151 1

8 7 375 781 8 155 321 78

Table 9-12 Total energy use of the 8 workstations during the metering campaign, expressed in Wh

After comparing the total and average usages, the 8 channels data was sorted into the ‘Low’,

‘Medium’ and ‘High’ profiles. The different load stations were as follows:

• ‘LOW’: 2, 5, 7;

• ‘MEDIUM: 1, 3, 6, 8;

• ‘HIGH’: 4.

The three profiles were checked versus the user type of the workstations to see the correlation

in between if any. User profiling was performed for the metered workstations:

• ‘Low’: users such as a department manager, or an engineer on a reduced work-time;

• ‘Medium’: typical workers such as engineers, with or without extra title (as an engineer/

technical director);

• ‘High’: engineers running simulations, or another type of users performing power

consuming tasks.

9.3.5 Low, Medium and High Profiles versus the Workday Profile

The next step included the comparison of the energy usage between the final averaged workday

to the created ‘Low’, ’Medium’ and ‘High’ load usage profiles.



Different Load Stations Final Workday

Hour High Medium Low

1 26,7 6,4 0,8 6,8

2 26,6 6,5 0,8 6,8

3 27,1 6,5 0,9 6,9

4 27,1 6,3 0,8 6,8

5 26,7 6,5 0,8 6,8

6 26,8 6,3 0,8 6,8

7 27,3 6,4 1,0 6,9

8 68,6 7,6 1,4 12,8

9 78,5 33,7 6,6 29,0

10 70,1 40,9 14,8 34,6

11 67,6 38,8 15,8 33,6

12 75,0 36,2 16,1 33,3

13 72,7 39,7 16,4 34,9

14 70,5 36,2 15,6 32,6

15 65,0 31,9 13,6 29,5

16 51,4 31,8 7,3 24,9

17 31,6 18,6 3,2 14,4

18 28,8 8,3 1,6 8,3

19 27,5 6,4 1,2 7,0

20 27,4 6,4 0,9 6,9

21 27,2 6,4 0,8 6,8

22 26,9 6,4 0,8 6,8

23 26,4 6,4 0,8 6,7

24 26,6 6,3 0,8 6,7

Peak Load: 78,5 40,9 16,4 34,9

Table 9-13 Energy consumption averages for High, Medium, Low energy user and Workday,

expressed in W

Graph 9-8 shows these three profiles together with the averaged workday profile. The averaged

usages between different profiles have similar patterns, and the ‘Medium’ profile resembles the

’Workday’s’ pattern. Even though the energy usage between the lowest and the highest peaks

differs by almost 4 times, the overall daily patterns are the same.

Graph 9-8 Energy consumption averages for High, Medium, Low energy user and Workday

78,5

40,9

16,4

34,9

0

20

40

60

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Po

wer

[W]

Hour

High

Medium

Low

Workday


January 2019 129

9.3.6 Final Workday and Weekend Consumption Profiles

The final averaged energy consumption is demonstrated in Graph 9-9 for both, a workday and

weekend with the indicated peak load.

Graph 9-9 The averages of energy consumption for workday and weekend

9.3.7 Peak Load Data

One main plug load profile for all averaged workdays was created. The peak loads were

determined for the averaged workday, ‘High’, ‘Medium’ and ‘Low’ profiles. The peak loads

are presented in Table 9-14:

Peak value [W]

Plug metering (averaged workday) 34,9

Plug metering (‘High’ profile) 74,4

Plug metering (‘Medium’ profile) 41,6

Plug metering (‘Low’ profile) 17,1

Table 9-14 The peak loads for plug load of the examined open plan office

34,9

0

10

20

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

En

erg

y U

se [

W]

Hour

Workday

Weekend



9.3.8 Hourly Equipment Load Profiles

For the plug load metering, the averaged workday data was used to create the main profile for

the office energy use. The weekend profile was calculated by averaging all Saturdays and

Sundays between all 8 measured workstations. Figure 9-8 displays the final profile for the

workday and the weekend.

Time Workday Weekend

01:00 19% 15%

02:00 20% 15%

03:00 20% 15%

04:00 20% 15%

05:00 20% 15%

06:00 19% 15%

07:00 20% 15%

08:00 37% 15%

09:00 83% 15%

10:00 99% 15%

11:00 96% 15%

12:00 96% 15%

13:00 100% 15%

14:00 93% 15%

15:00 83% 15%

16:00 71% 15%

17:00 41% 15%

18:00 24% 15%

19:00 20% 15%

20:00 20% 15%

21:00 20% 15%

22:00 20% 15%

23:00 19% 15%

24:00 19% 15%

Peak

Load 34,9 W

Figure 9-8 Final hourly plug load profile for the workday and weekend

The peak loads determined for the ‘High’, ‘Medium’ and ‘Low’ profiles were averaged from

the metered workstations. These profiles and the created averaged workday profile were

expressed in %, and the daily energy consumption was calculated for these three profiles.

Graph 9-10 shows the comparison between the ‘High’, ‘Medium’ and ‘Low’ actual averaged

stations’ profiles (grey dashed lines) and the final averaged workday profiles (solid lines in

colour). The energy use is expressed in watts [W].

0%

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100%

1 3 5 7 9 11 13 15 17 19 21 23Hour

Workday

Weekend


January 2019 131

a) ‘High’ profile b) ‘Medium’ Profile c) ‘Low’ Profile

Graph 9-10 Comparison between the measured stations' averages by the different User Profile

(colourful dashed lines) to the created workday profile (solid black lines)

The created ‘Average workday’ profile can be used for the different types of plug load users by

identifying the peak loads.

To sum up, the averaged workday profile with a peak load of 34,9 W was made by analysing

the whole office level. The specific profiles as ‘High’, ‘Medium’ and ‘Low’ were created for

the workstation level.

78,5 78,5

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9.3.9 Higher Resolution Equipment Load Profiles

Same as for occupancy, the higher resolution profiles were created with 5-min, 15-min and 30-

min time steps.

End Hour 1 2 3 4 5 6 7 8 9 10 11 12

1-hour 19% 20% 20% 20% 20% 19% 20% 37% 83% 99% 96% 96%

5-m

in

--:05 1,6% 1,6% 1,7% 1,6% 1,7% 1,5% 1,6% 1,9% 4,0% 8,7% 8,1% 7,9%

--:10 1,6% 1,7% 1,6% 1,6% 1,7% 1,5% 1,6% 2,4% 4,5% 8,6% 8,1% 7,7%

--:15 1,6% 1,6% 1,6% 1,8% 1,5% 1,7% 1,6% 2,6% 4,8% 8,3% 8,0% 8,0%

--:20 1,6% 1,6% 1,7% 1,6% 1,7% 1,6% 1,6% 2,7% 5,5% 8,2% 8,1% 7,8%

--:25 1,7% 1,6% 1,7% 1,6% 1,6% 1,5% 1,8% 3,0% 6,3% 8,1% 7,8% 8,1%

--:30 1,5% 1,7% 1,6% 1,7% 1,6% 1,6% 1,6% 3,0% 7,3% 8,3% 8,2% 8,0%

--:35 1,7% 1,5% 1,6% 1,7% 1,5% 1,7% 1,6% 3,5% 8,1% 8,5% 8,2% 7,8%

--:40 1,6% 1,6% 1,8% 1,6% 1,7% 1,5% 1,7% 3,4% 8,4% 8,2% 8,1% 8,1%

--:45 1,6% 1,7% 1,6% 1,7% 1,7% 1,6% 1,6% 3,5% 8,6% 8,1% 8,0% 8,0%

--:50 1,6% 1,7% 1,6% 1,6% 1,6% 1,7% 1,6% 3,6% 8,6% 8,0% 7,9% 7,9%

--:55 1,7% 1,5% 1,7% 1,6% 1,6% 1,6% 1,5% 3,6% 8,5% 8,1% 7,8% 8,1%

--:00 1,6% 1,7% 1,7% 1,6% 1,7% 1,6% 1,9% 3,6% 8,5% 8,0% 7,9% 8,0%

End Hour 13 14 15 16 17 18 19 20 21 22 23 24

1-hour 100% 93% 83% 71% 41% 24% 20% 20% 20% 20% 19% 19%

5-m

in

--:05 8,0% 7,6% 7,3% 6,8% 4,6% 2,4% 1,7% 1,7% 1,7% 1,7% 1,6% 1,6%

--:10 8,2% 7,9% 7,2% 6,9% 4,4% 2,3% 1,7% 1,7% 1,6% 1,5% 1,7% 1,6%

--:15 8,3% 7,5% 7,1% 6,5% 4,1% 2,1% 1,7% 1,7% 1,6% 1,8% 1,5% 1,7%

--:20 8,3% 7,7% 6,9% 6,2% 4,0% 2,0% 1,7% 1,6% 1,7% 1,5% 1,7% 1,5%

--:25 8,4% 7,8% 6,9% 6,1% 3,6% 2,0% 1,6% 1,7% 1,6% 1,6% 1,6% 1,7%

--:30 8,3% 7,9% 6,8% 6,1% 3,3% 2,0% 1,7% 1,6% 1,7% 1,7% 1,6% 1,6%

--:35 8,3% 7,9% 6,8% 5,9% 3,3% 2,0% 1,8% 1,6% 1,7% 1,6% 1,5% 1,6%

--:40 8,6% 8,0% 6,9% 5,8% 3,1% 1,9% 1,7% 1,7% 1,6% 1,6% 1,7% 1,6%

--:45 8,5% 7,9% 6,9% 5,6% 2,9% 1,9% 1,6% 1,7% 1,5% 1,6% 1,6% 1,6%

--:50 8,4% 7,8% 6,8% 5,4% 2,8% 1,8% 1,7% 1,7% 1,6% 1,7% 1,6% 1,6%

--:55 8,4% 7,7% 6,8% 5,2% 2,6% 1,7% 1,7% 1,6% 1,7% 1,6% 1,6% 1,5%

--:00 8,3% 7,6% 6,7% 4,9% 2,5% 1,8% 1,6% 1,7% 1,5% 1,6% 1,6% 1,6%

Table 9-15 Final plug load profile for the workday, resolution - 5-min

Graph 9-11 Final plug load profile comparison for the workday between two resolutions – 1-hour

(grey columns on the left vertical axis) and 5-min (blue dashed line on the right vertical axis)

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

5-m

in P

rofi

le %

1-h

ou

r P

rofi

le %

Hour


January 2019 133

End Hour 1 2 3 4 5 6 7 8 9 10 11 12

1-hour 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

5-m

in

--:05 1,2% 1,4% 1,2% 1,3% 1,2% 1,3% 1,3% 1,3% 1,3% 1,2% 1,3% 1,2%

--:10 1,3% 1,1% 1,3% 1,4% 1,3% 1,3% 1,2% 1,3% 1,1% 1,3% 1,1% 1,2%

--:15 1,2% 1,2% 1,3% 1,4% 1,3% 1,3% 1,2% 1,2% 1,3% 1,2% 1,3% 1,4%

--:20 1,4% 1,4% 1,2% 1,3% 1,2% 1,3% 1,3% 1,4% 1,2% 1,3% 1,3% 1,1%

--:25 1,2% 1,3% 1,4% 1,3% 1,3% 1,3% 1,3% 1,2% 1,2% 1,3% 1,1% 1,3%

--:30 1,3% 1,3% 1,2% 1,1% 1,4% 1,2% 1,3% 1,3% 1,2% 1,2% 1,2% 1,2%

--:35 1,3% 1,2% 1,4% 1,3% 1,3% 1,3% 1,3% 1,2% 1,3% 1,2% 1,2% 1,2%

--:40 1,2% 1,4% 1,2% 1,2% 1,3% 1,1% 1,2% 1,3% 1,2% 1,2% 1,4% 1,2%

--:45 1,3% 1,1% 1,3% 1,3% 1,2% 1,4% 1,4% 1,3% 1,2% 1,3% 1,0% 1,3%

--:50 1,2% 1,3% 1,4% 1,2% 1,4% 1,1% 1,3% 1,2% 1,2% 1,2% 1,3% 1,2%

--:55 1,2% 1,3% 1,2% 1,4% 1,1% 1,4% 1,3% 1,3% 1,3% 1,2% 1,3% 1,2%

--:00 1,2% 1,3% 1,3% 1,3% 1,3% 1,3% 1,2% 1,2% 1,3% 1,2% 1,2% 1,3%

End Hour 13 14 15 16 17 18 19 20 21 22 23 24

1-hour 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

5-m

in

--:05 1,3% 1,2% 1,3% 1,2% 1,2% 1,1% 1,2% 1,2% 1,2% 1,2% 1,2% 1,3%

--:10 1,1% 1,3% 1,3% 1,2% 1,4% 1,1% 1,3% 1,2% 1,5% 1,2% 1,4% 1,2%

--:15 1,3% 1,3% 1,2% 1,4% 1,1% 1,5% 1,2% 1,4% 1,1% 1,3% 1,2% 1,3%

--:20 1,3% 1,2% 1,3% 1,2% 1,4% 1,2% 1,3% 1,2% 1,2% 1,3% 1,2% 1,3%

--:25 1,2% 1,2% 1,3% 1,3% 1,3% 1,3% 1,1% 1,3% 1,4% 1,2% 1,3% 1,2%

--:30 1,2% 1,3% 1,1% 1,3% 1,2% 1,2% 1,3% 1,2% 1,3% 1,3% 1,3% 1,3%

--:35 1,4% 1,2% 1,3% 1,3% 1,3% 1,4% 1,2% 1,4% 1,1% 1,3% 1,2% 1,2%

--:40 1,2% 1,2% 1,4% 1,2% 1,4% 1,1% 1,2% 1,1% 1,3% 1,2% 1,2% 1,3%

--:45 1,2% 1,3% 1,1% 1,4% 1,2% 1,3% 1,3% 1,2% 1,4% 1,1% 1,4% 1,1%

--:50 1,3% 1,3% 1,1% 1,3% 1,2% 1,1% 1,3% 1,3% 1,2% 1,4% 1,2% 1,4%

--:55 1,2% 1,2% 1,4% 1,2% 1,4% 1,3% 1,3% 1,3% 1,2% 1,2% 1,2% 1,1%

--:00 1,2% 1,2% 1,3% 1,3% 1,3% 1,2% 1,2% 1,2% 1,2% 1,2% 1,3% 1,0%

Table 9-16 Final plug load profile for the weekend, resolution - 5-min

Graph 9-12 Final plug load profile comparison for the weekend between two resolutions – 1-hour


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

5-m

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Hour



End Hour 1 2 3 4 5 6 7 8 9 10 11 12

1-hour 19% 20% 20% 20% 20% 19% 20% 37% 83% 99% 96% 96%

15

-min

--:15 4,8% 4,9% 4,9% 5,0% 4,9% 4,8% 4,8% 6,8% 13,3% 25,6% 24,2% 23,6%

--:30 4,9% 4,9% 4,9% 4,9% 4,9% 4,8% 5,0% 8,7% 19,1% 24,6% 24,1% 24,0%

--:45 4,9% 4,8% 5,0% 5,0% 4,9% 4,9% 4,9% 10,3% 25,0% 24,7% 24,3% 24,0%

--:00 4,9% 4,9% 5,0% 4,7% 4,9% 4,9% 5,1% 10,9% 25,5% 24,2% 23,6% 24,0%

End Hour 13 14 15 16 17 18 19 20 21 22 23 24

1-hour 100% 93% 83% 71% 41% 24% 20% 20% 20% 20% 19% 19%

15

-min

--:15 24,4% 23,0% 21,6% 20,2% 13,1% 6,9% 5,0% 5,2% 4,9% 5,0% 4,8% 4,9%

--:30 25,0% 23,3% 20,6% 18,4% 10,9% 6,0% 5,0% 4,9% 5,0% 4,8% 4,9% 4,8%

--:45 25,5% 23,9% 20,6% 17,3% 9,2% 5,7% 5,1% 4,9% 4,9% 4,8% 4,8% 4,8%

--:00 25,1% 23,1% 20,3% 15,5% 7,9% 5,2% 5,1% 4,9% 4,9% 4,9% 4,8% 4,7%




End Hour 1 2 3 4 5 6 7 8 9 10 11 12

1-hour 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

15

-min

--:15 3,8% 3,7% 3,9% 4,0% 3,9% 3,9% 3,7% 3,8% 3,8% 3,6% 3,7% 3,8%

--:30 3,9% 3,9% 3,7% 3,7% 3,9% 3,7% 3,9% 3,9% 3,7% 3,9% 3,6% 3,7%

--:45 3,8% 3,7% 3,9% 3,9% 3,8% 3,9% 3,9% 3,7% 3,8% 3,7% 3,6% 3,7%

--:00 3,7% 3,9% 3,9% 3,9% 3,9% 3,8% 3,7% 3,7% 3,8% 3,6% 3,7% 3,7%

End Hour 13 14 15 16 17 18 19 20 21 22 23 24

1-hour 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

15

-min

--:15 3,7% 3,8% 3,8% 3,7% 3,7% 3,7% 3,8% 3,8% 3,8% 3,7% 3,8% 3,8%

--:30 3,7% 3,7% 3,7% 3,7% 3,8% 3,7% 3,7% 3,7% 3,9% 3,7% 3,8% 3,8%

--:45 3,8% 3,7% 3,8% 3,8% 3,9% 3,8% 3,8% 3,7% 3,8% 3,6% 3,7% 3,7%

--:00 3,7% 3,7% 3,8% 3,8% 3,9% 3,7% 3,7% 3,8% 3,6% 3,9% 3,7% 3,5%


00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

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Hour


January 2019 135



End Hour 1 2 3 4 5 6 7 8 9 10 11 12

1-hour 19% 20% 20% 20% 20% 19% 20% 37% 83% 99% 96% 96%

30

-min

--:30 9,7% 9,9% 9,8% 9,8% 9,8% 9,6% 9,8% 15,6% 32,5% 50,2% 48,4% 47,6%

--:00 9,8% 9,7% 10,1% 9,7% 9,7% 9,7% 10,0% 21,2% 50,5% 48,9% 48,0% 48,0%

End Hour 13 14 15 16 17 18 19 20 21 22 23 24

1-hour 100% 93% 83% 71% 41% 24% 20% 20% 20% 20% 19% 19%

30

-min

--:30 49,4% 46,3% 42,2% 38,6% 24,0% 12,9% 10,0% 10,0% 9,9% 9,8% 9,7% 9,7%

--:00 50,6% 47,0% 40,9% 32,8% 17,1% 11,0% 10,1% 9,8% 9,7% 9,7% 9,6% 9,5%




End Hour 1 2 3 4 5 6 7 8 9 10 11 12

1-hour 0% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

30

-

min

--:30 7,6% 7,7% 7,7% 7,7% 7,7% 7,6% 7,6% 7,7% 7,5% 7,5% 7,3% 7,6%

--:00 7,5% 7,7% 7,7% 7,8% 7,7% 7,7% 7,6% 7,4% 7,5% 7,3% 7,3% 7,4%

End Hour 13 14 15 16 17 18 19 20 21 22 23 24

1-hour 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15%

30

-

min

--:30 7,4% 7,5% 7,5% 7,5% 7,5% 7,4% 7,5% 7,5% 7,6% 7,5% 7,6% 7,6%

--:00 7,5% 7,4% 7,6% 7,6% 7,7% 7,5% 7,5% 7,5% 7,4% 7,5% 7,5% 7,2%


00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00

0%

5%

10%

15%

20%

25%

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

15-m

in

Pro

file

%

1-h

ou

r P

rofi

le %

Hour

00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00

0%

10%

20%

30%

40%

50%

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

30-m

in

Pro

file

%

1-h

ou

r P

rofi

le %

Hour





9.3.10 Data from the Electric Utility Company

NRGi is the electric utility company metering the electricity consumed in the “Pakhusene”

building. The data received included the total consumption of plug load and lighting for two

building levels, both divided into half floor sections. The four data sets were named as PAK1,

PAK2, PAK3 and PAK4. Graph 9-17 shows the four sets yearly consumption together with

their average consumption.

Graph 9-17 Yearly consumption of the "Pakhusene" building four data sets

00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00 00: 00

0%

10%

20%

30%

40%

50%

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

30-m

in

Pro

file

%

1-h

ou

r P

rofi

le %

Hour

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0

20

40

60

80

02/18 03/18 04/18 05/18 06/18 07/18 08/18 09/18 10/18 11/18

En

ergy

Use

[k

Wh]

PAK 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0

20

40

60

80

02/18 03/18 04/18 05/18 06/18 07/18 08/18 09/18 10/18 11/18

En

ergy

Use

[k

Wh] PAK 2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0

20

40

60

80

02/18 03/18 04/18 05/18 06/18 07/18 08/18 09/18 10/18 11/18

En

ergy

Use

[k

Wh] PAK 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0

20

40

60

80

02/18 03/18 04/18 05/18 06/18 07/18 08/18 09/18 10/18 11/18

En

ergy

Use

[k

Wh] PAK 4


January 2019 137

9.3.10.1 Daily Consumption Comparison for the Metered Period

Graph 9-18 displays the comparison of the two profiles; the vacation period versus the

weekends. The PAK 1 and PAK 2 data sets have a similar pattern, indicating no occupancy was

present in the July vacation period. The PAK 3 reveals slight occupancy in the office. The PAK

4 data set had higher usage during weekends, revealing that occupants turned off their

workstation’s equipment, minimising the standby loads.

PAK 1 PAK 2

PAK 3 PAK 4

Graph 9-18 Hourly vacation period (July, weeks 29 and 30, 2018) profile of the "Pakhusene" building

four data sets versus their weekend profiles (solid black line – weekend profile, dotted green line –

vacation period profile)

0%

20%

40%

60%

1 3 5 7 9 11 13 15 17 19 21 23

Hour

0%

20%

40%

60%

1 3 5 7 9 11 13 15 17 19 21 23

Hour

0%

20%

40%

60%

1 3 5 7 9 11 13 15 17 19 21 23

Hour

0%

20%

40%

60%

1 3 5 7 9 11 13 15 17 19 21 23

Hour



10 APPENDIX: STUDY OF

INTERNAL LOADS AND

THERMAL MASS VARIATIONS

10 APPENDIX: STUDY OF INTERNAL LOADS AND THERMAL MASS VARIATIONS

138

10.1 BUILDING LEVEL ...................................................................................................... 138

10.1.1 Internal Wall Construction ........................................................................................ 138

10.1.2 Energy Demand ......................................................................................................... 139

10.2 ROOM LEVEL ............................................................................................................ 141

10.2.1 Energy Demand ......................................................................................................... 141

10.2.2 Overheating Hours .................................................................................................... 143

10.2.3 Peak Heating and Cooling Loads .............................................................................. 144

10.1 Building Level

10.1.1 Internal Wall Construction

Two types of concrete were used for internal wall constructions. They represent reinforced, cast

in situ scenario for Alfa case and lightweight, prefabricated panels for Beta case. The drywall

construction consists of two 0.0125m thick layers of plasterboard and air cavity, the density

stated in Table 10-1Table 10-1 corresponds to standard gypsum board. BSim inputs for

constructions can be found in Appendix 7.4.2.1, Table 7-3 and Appendix 7.4.3, Table 7-18.

Alfa Beta

Light Heavy Light Heavy

Thickness [m] 0,12 0,18 0,12 0,25

Density [kg/m3] 900 2385 900 800

[kg/m2] 6,43 429,3 6,43 326,8

U- value [W/mK] 4,16 8,88 4,16 1,03

Table 10-1 Internal construction properties for internal gain variations

Chapter 10: Appendix: Study of Internal Loads and Thermal Mass Variations

January 2019 139

10.1.2 Energy Demand

The alterations in equipment loads resulted in a total annual load of 88,0 kWh/m2/year for

standby and 65,7 kWh/m2/year for non-standby BPS models in Alfa case. Beta case resulted in

49,7 kWh/m2 and 37,6 kWh/m2 respectively. The impact of internal gain variations on total

heating, cooling and equipment energy demand for both case buildings is shown in Graph 10-1.

a) Alfa

b) Beta

Graph 10-1 Annual heating, cooling and equipment energy demand in kWh/year for Alfa (top) and

Beta (bottom) internal gain alteration BPS models

3730

2890

2083

1037

2243

6171

4415

8294

6142

8096

61944 61944

82946 82946 81517

0

20000

40000

60000

80000

100000

0

1500

3000

4500

6000

7500

9000

No Standby, Light

(3)

No Standby,

Heavy (4)

Standby, Light (1) Standby, Heavy

(2)

Baseline

Ann

ual

equ

ipm

ent

load

[kW

h/y

ear]

An

nu

al e

ner

gy u

se

[kW

h/y

ear]

qHeating qCooling qEquipment

6826

5720

4347

3543

5720

3473 36904287

4825

369031853 31908

42168 4216831908

0

20000

40000

60000

80000

100000

0

1500

3000

4500

6000

7500

9000

No Standby,

Light (3)

No Standby,

Heavy (4)

Standby, Light (1) Standby, Heavy

(2)

Baseline

An

nu

al e

qu

ipm

ent

load

[kW

h/y

ear]

An

nu

al e

ner

gy u

se

[kW

h/y

ear]

qHeating qCooling qEquipment



SET1 SET2 SET3 SET4 Baseline

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

[m2] [kWh/year] [kWh/year] [kWh/year] [kWh/year] [kWh/year]

1 Offices SW 134,2 97 1160 24 809 310 617 214 388 108 1128

2 Offices SE 126,7 122 720 40 437 334 340 240 179 132 697

3 Offices NW 154,0 188 63 71 24 488 0 369 0 199 57

4 Offices NE 143,8 231 8 88 3 522 0 390 0 246 6

5 Meeting rooms C. 36,7 151 51 11 5 180 5 84 0 162 35

6 Small meeting rooms S 36,7 420 753 186 368 559 673 307 251 463 731

7 Other N 16,5 128 14 106 4 195 1 230 0 146 9

8 Other W 20,6 129 193 100 155 189 137 191 98 154 169

9 Other W 36,7 22 1514 5 1309 45 1294 21 1068 25 1487

10 Other E 39,6 74 1611 37 1442 106 1444 78 1257 77 1598

11 Other C. 53,7 0 585 0 176 0 309 0 13 0 563

12 Lounge/Kitch. N 25,2 251 513 203 372 399 405 404 290 257 508

13 Lounge/Kitch. E 25,2 113 1110 94 1040 180 947 177 872 114 1107

14 Toilets C. 36,7 159 0 73 0 225 0 184 0 161 0

Table 10-2 Alfa: Variation sets results for 14-zone model: annual heating and cooling demand, kWh/

year

SET1 SET2 SET3 SET4 Baseline

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

Hea

tin

g

Co

oli

ng

[m2] [kWh/year] [kWh/year] [kWh/year] [kWh/year] [kWh/year]

1 Offices W 137,5 82 1160 45 11 300 12 200 8 200 8

2 Offices N 104,1 95 720 59 0 320 0 232 0 232 0

3 Offices E 139,7 64 63 47 0 297 0 236 0 236 0

4 Offices SE 48,2 13 8 7 132 50 60 30 74 30 74

5 Meeting rooms S 23,7 782 51 809 11 948 6 988 5 988 5

6 Small meeting r. SW 15,2 389 753 371 1720 470 1656 436 1643 436 1643

7 Meeting rooms W 17,1 103 14 55 0 204 0 158 0 158 0

8 Small meeting r. C. 10,8 99 193 8 10 251 6 89 2 89 2

9 Lounge/Kitch. S 60,7 20 1514 4 1668 209 940 192 974 192 974

10 Toilets C. 42,0 2018 1611 1386 0 2771 0 2118 0 2118 0

11 Other S 76,7 274 585 249 450 478 273 449 277 449 277

12 Other N 42,2 410 513 504 644 527 494 593 604 593 604

13 Other C. 111,8 0 1110 0 1 0 0 0 0 0 0

14 Other C. 20,0 0 0 0 178 0 26 0 102 0 102

Table 10-3 Beta: Variation sets results for 14-zone model: annual heating and cooling demand,

kWh/year


January 2019 141

10.2 Room Level

10.2.1 Energy Demand

Heating and cooling demand after MOE’s profiles have been applied to all offices in Alfa BSim

12- and 14-zone models (Graph 4-3, Graph 10-3).

Graph 10-2 Alfa: annual heating and cooling loads per m² in relation to the change of the people and

equipment load profiles

Graph 10-3 Beta: heating and cooling loads in relation to the change of the people and equipment

load profiles

qPeople qEquipment qHeating

[kWh] MOE [%] [kWh] MOE [%] [kWh] MOE [%]

Alfa 12 41609

↓ 41%

81517

↓ 49%

2128 ↑ 358%

Moe 12 24722 41332 7620

Alfa 14 41609 81517 2147 ↑ 364%

Moe 14 24722 41332 7807

Models with mechanical cooling

qPeople qEquipment qHeating qCooling

[kWh] MOE [%] [kWh] MOE [%] [kWh] MOE [%] [kWh] MOE [%]

Alfa 12 41609

↓ 41%

81517

↓ 49%

2221 ↑ 349%

9180 ↓ 52%

Moe 12 24722 41332 7759 4803

Alfa 14 41609 81517 2243 ↑ 355%

8096 ↓ 61%

Moe 14 24722 41332 7955 4900

Table 10-4 Alfa: annual change in internal loads and heating, cooling demand

2,4 2,4

↑ 72% ↑ 72%

9,78,6

↓ 48% ↓ 39%

qPeople↓ 41%

qEquipment

↓ 49%

0

20

40

60

80

100

0

2

4

6

8

10

Alfa 12 Alfa 14 MOE 12 MOE 14

Peo

ple

-equip

men

t lo

ad

[kW

h/m

²]

En

ergy

dem

and

[kW

h/m

²]

qHeating qCooling qPeople qEquipment

7,26,7

↑ 24%↑ 22%

3,94,3 ↑ 7%

↑ 8%

qPeople↓ 42%

qEquipment ↓ 7%

0

10

20

30

40

0

2

4

6

8

10

Beta 12 Beta 14 MOE 12 MOE 14

Peo

ple

-equ

ipm

ent

load

[kW

h/m

²]

En

ergy

dem

and

[kW

h/m

²]

qHeating qCooling qPeople qEquipment



qPeople qEquipment qHeating

[kWh] MOE [%] [kWh] MOE [%] [kWh] MOE [%]

Beta 12 14559

↓ 42%

31908

↓ 7%

5972 ↑ 24%

Moe 12 8389 29578 7417

Beta 14 14559 31908 5573 ↑ 22%

Moe 14 8389 29578 6810

Models with mechanical cooling

qPeople qEquipment qHeating qCooling

[kWh] MOE [%] [kWh] MOE [%] [kWh] MOE [%] [kWh] MOE [%]

Beta 12 14559

↓ 42%

31908

↓ 7%

↑ 24%

9180 ↑ 7%

Moe 12 8389 29578 7759 4803

Beta 14 14559 31908 2243 ↑ 22%

8096 ↑ 8%

Moe 14 8389 29578 7955 4900

Table 10-5 Beta: annual change in internal loads and heating, cooling demand

The heating and cooling need per m² are expressed in Table 4-5 below.

a) Alfa With zoning chapter load profiles With MOE’s load profiles Part of the original demand*

qHeating qCooling qHeating qCooling qHeating qCooling

12-zone [kWh/m²/year]

Offices S1 0,81 10,44 8,03 0,54 ↑ 986% ↓ 5%

Offices N1 1,45 2,13 11,60 0,01 ↑ 800% ↓ 0,4%

14-zone

Offices S1 0,80 8,41 8,09 0,05 ↑ 1014% ↓ 1%

Offices S2 1,03 5,50 8,77 0,01 ↑ 851% ↓ 0,1%

Offices N1 1,29 0,37 11,19 0,00 ↑ 869% ↓ 0%

Offices N2 1,70 0,04 12,74 0,00 ↑ 751% ↓ 0%

b) Beta With zoning chapter load profiles With MOE’s load profiles Part of the original demand*

qHeating qCooling qHeating qCooling qHeating qCooling

12-zone [kWh/m²/year]

Offices SW 1,51 0,05 3,68 0,07 ↑ 244% ↑ 138%

Offices N 2,32 0,07 5,79 0,03 ↑ 250% ↓ 52%

Offices E 0,65 1,21 1,95 0,07 ↑ 301% ↓ 6%

14-zone

Offices SW 1,45 0,06 3,67 0,08 ↑ 253% ↑ 139%

Offices N1 2,23 0,00 4,83 0,00 ↑ 217% 0%

Offices N2 1,69 0,00 4,12 0,00 ↑ 244% 0%

Offices E 0,63 1,53 1,97 0,14 ↑ 313% ↓ 9%

*The change is calculated in percentage taking the designed load profiles as 100%.

Table 10-6 The impact on the heating and cooling loads in kWh/m²/year in all offices in 12- and 14-

zone models in both cases after applying MOE’s people and equipment loads


January 2019 143

10.2.2 Overheating Hours

The total overheating hours in both Alfa and Beta 12- and 14-zone models in comparison with

the modified models with MOE’s internal loads, see following graphs:

a) Alfa b) Beta

Graph 10-4 The total overheating hours in Alfa and Beta models before and after applying MOE’s

measured occupancy and equipment load profiles

a) 12-zone model b) 14-zone model

Graph 10-5 Alfa: overheating hours in the offices

a) 12-zone model b) 14-zone model

Graph 10-6 Beta: overheating hours in the offices

1713 1703

↓15% ↓17%1640

1469

↓27% ↓19%

75 71 ↓23% ↓17%39 39↓18% ↓18%

0

500

1000

1500

2000

Alfa 12 Alfa 14 Moe 12 Moe 14

Ov

erh

eati

ng

ho

urs

[-]

>26°C >27°C >26°C(Cool) >27°C(Cool)

919

1075

↓40%

↑18%

572

872

↓40%

↑17%

30113

↑3%↓18%

881

↑75%↓14%

0

400

800

1200

Beta 12 Beta 14 Moe 12 Moe 14

Over

hea

ting

hou

rs [

-]

>26°C >27°C >26°C(Cool) >27°C(Cool)

446

8941

0

113

105 0

0

100

200

300

400

500

Offices S1 Offices N1Over

hea

ting

hou

rs [

-]

12 Alfa >26°C 12 Moe >26°C12 Alfa >27°C 12 Moe >27°C

352

240

1 00 0 022

4729

0 00 0 0 2

0

100

200

300

400

Offices S1 Offices S2 Offices N1 Offices N2Ov

erh

eati

ng

ho

urs

[-]

14 Alfa >26°C 14 Moe >26°C14 Alfa >27°C 14 Moe >27°C

0 0

422

0 037

0 0 00 0 0

0

100

200

300

400

500

Offices SW Offices N Offices EOv

erh

eati

ng

ho

urs

[-]

12 Beta >26°C 12 Moe >26°C12 Beta >27°C 12 Moe >27°C

0 0 0

82

0 0

57

11

0 0 0 20 0

29

0

0

25

50

75

100

Offices SW Offices N1 Offices N2 Offices EOver

hea

ting

hou

rs [

-]

14 Beta >26°C 14 Moe >26°C14 Beta >27°C" 14 Moe >27°C



10.2.3 Peak Heating and Cooling Loads

The change in peak heating and cooling loads per m² are expressed in all offices in Alfa and

Beta 12- and 14-zone models, see the following tables.

With design load profiles from the zoning chapter

14-zone model 12-zone model

Thermal Zone Area Heating Cooling Thermal Zone Area Heating Cooling

[m²] [kW/m²/year] [m²] [kW/m²/year]

Offices S1 134,2 0,02 0,04 Offices S1 261,0 0,02 0,07

Offices S2 126,7 0,03 0,04 Offices N1 297,7 0,03 0,04

Offices N1 154,0 0,03 0,01 Offices N2 143,8 0,04 0,01

With MOE’s load profiles




Offices S1 134,2 ↑ 0,04 ↓ 0,01 Offices S1 261,0 ↑ 0,04 ↓ 0,04

Offices S2 126,7 ↑ 0,04 ↓ 0,00 Offices N1 297,7 ↑ 0,05 ↓ 0,00

Offices N1 154,0 ↑ 0,05 ↓ 0,00 Offices N2 143,8 ↑ 0,05 ↓ 0,00

Table 10-7 Alfa: change in peak heating and cooling loads in the offices in 12- and 14-zone models

With design load profiles from the zoning chapter




Offices SW 137,5 0,04 0,01 Offices SW 137,5 0,04 0,01

Offices N1 104,1 0,04 0,00 Offices N 243,9 0,04 0,01

Offices N2 139,7 0,04 0,00 Offices E 48,2 0,03 0,02

Offices E 48,2 0,03 0,03

With MOE’s load profiles




Offices SW 137,5 ↑ 0,05 0,01 Offices SW 137,5 ↑ 0,05 0,01

Offices N1 104,1 ↑ 0,05 0,00 Offices N 243,9 ↑ 0,06 ↑ 0,02

Offices N2 139,7 ↑ 0,05 0,00 Offices E 48,2 ↑ 0,05 ↓ 0,01

Offices E 48,2 ↑ 0,05 ↓ 0,02

Table 10-8 Beta: change in peak heating and cooling loads in the offices in 12- and 14-zone models

Chapter 11: References

January 2019 145

11 REFERENCES

[1] Malanda@overlyxclusive, “No Title,” twitter, 2015. [Online]. Available:

https://twitter.com/overlyxclusive/status/583440203857035266?lang=en. [Accessed: 02-Jan-

2019].

[2] K. Thullner, “Low-energy buildings in Europe -Standards, criter ia and consequences,” 2010.

[3] T. Østergård, R. L. Jensen, and S. E. Maagaard, “Building simulations supporting decision

making in early design - A review,” Renew. Sustain. Energy Rev., vol. 61, pp. 187–201, 2016.

[4] I. Gaetani, P. J. Hoes, and J. L. M. Hensen, “Occupant behavior in building energy simulation:

Towards a fit-for-purpose modeling strategy,” Energy Build., vol. 121, pp. 188–204, 2016.

[5] M. B. Kaplan, “Guidelines for Energy Simulati n of Commercial Buildings Organization of the

Guidelines Modeling From the Program manager’s Perspective.”

[6] “Thermal Zoning Determination - Bembook.” [Online]. Available:

https://bembook.ibpsa.us/index.php?title=Thermal_Zoning_Determination. [Accessed: 02-Jan-

2019].

[7] N. L. Mette Havgaard Vorre, Mads Hulmose Wagner, Steffen E. Maagaard, Peter Noyé and L.

M. Lyng, Branchevejledning for indeklimaberegninger. Statens Byggeforskningsinstitut,

Aalborg Universitet,InnoBYG, 2017.

[8] F. Foreningen af Rådgivende Ingeniører and D. ARKITEKTVIRKSOMHEDER,

Ydelsesbeskrivelse for Byggeri og Landskab, 2018. 2018.

[9] ISO13790, “Energy performance of buildings–Calculation of energy use for space heating and

cooling,” Eur. Comm. Stand. (CEN), Brussels, vol. 1, no. 1, p. 140, 2006.

[10] The Danish Transport and Construction Agency, “Danish Building Regulations 2015,” vol.

2015, no. 810, p. 125, 2015.

[11] C. Bleil de Souza and S. Alsaadani, “Thermal zoning in speculative office buildings: discussing

the connections between space layout and inside temperature control,” First Build. Simul. Optim.

Conf., no. September, pp. 417–424, 2012.

[12] L. Rivalin et al., “Influence Of Building Zoning On Annual Energy Demand,” no. 2012, pp. 1–

9, 2014.

[13] P. G. Ellis and P. A. Torcellini, “Simulating tall buildings using energyplus,” IBPSA 2005 - Int.



Build. Perform. Simul. Assoc. 2005, no. May, pp. 279–286, 2005.

[14] M. Kim, I. Yook, D. Kim, L. Kim, and J. Kim, “Study on Simulation Modelling Strategy for

Predicting Thermal Energy Demand Profiles of Residential Complex,” Elev. Int. IBPSA Conf.,

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