ENERGY-EFFICIENT APARTMENTS FOR ELDERLYOptimising daylight, shading, electric lighting and thermal comfort in an energy-efficient nursing for the elderly

Antoni Balcerzak

Master Thesis in Energy-efficient and Environmental BuildingsFaculty of Engineering | Lund University

Lund UniversityLund University, with eight faculties and a number of research centers and specialized institutes, is the largest establishment for research and higher education in Scandinavia. The main part of the University is situated in the small city of Lund which has about 112 000 inhabitants. A number of departments for research and education are, however, located in Malmö and Helsingborg. Lund University was founded in 1666 and has today a total staff of 6 000 employees and 47 000 students attending 280 degree programs and 2 300 subject courses offered by 63 departments.

Master Program in Energy-efficient and Environmental Building DesignThis international program provides knowledge, skills and competencies within the area of energy-efficient and environmental building design in cold climates. The goal is to train highly skilled professionals, who will significantly contribute to and influence the design, building or renovation of energy-efficient buildings, taking into consideration the architecture and environment, the inhabitants’ behavior and needs, their health and comfort as well as the overall economy.

The degree project is the final part of the master program leading to a Master of Science (120 credits) in Energy-efficient and Environmental Buildings.

Examiner: Maria Wall (Energy and Building Design)Supervisor: Åke Blomsterberg (Energy and Building Design), Ivana Kildsgaard (Link arkitektur)

Keywords: daylight, thermal comfort, shading, overheating, energy-efficiency, elderly people

Thesis: EEBD–16/06

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Abstract

The literature claims that elderly people need warmer indoor temperatures, short overheating

periods and more light indoors. The thesis analysed a case study building in order to propose

a solution of the façade design in the energy-efficient apartments for elderly people. Due to

the simplified approach to the analysis, only specific apartments were chosen and optimized

parts of design were shape of the façade, glazing area and shading type. The analysis

included heating energy demand, amount of overheating hours, daylight autonomy and view

out, and lighting energy demand. The results proved that due to the increased requirements

of light, the building will suffer overheating but this can be easily fixed with shading and

natural ventilation. It also proved that the initial electric light assumptions were too low due

to the increased lighting requirements. Finally it showed that even with increased indoor

temperature during heating season, the building fulfils the requirements of an energy

efficiency certificate.

Preface

This thesis is an original, intellectual work of Antoni Balcerzak. The case study building

information and general requirements were provided by architecture firm LINKarkitektur.

Nomenclature

DA – Daylight Autonomy: percentage of hours for a given annual schedule, when at the

certain point (group of points) in space the illuminance is higher than a given threshold.

DF – daylight factor: a unit that describes the ratio between illuminance level inside and

outside, measured for an overcast sky. It is calculated as follows: Ein/Eout*100%.

DGP – daylight glare probability, percentage probability, when the visual comfort of the

inhabitant looking in a specific way is affected by general brightness of the scene, visual

contrasts, and glare sources

EP – energy performance, heating energy demand

GWR – glazing to wall ratio, ratio of glazed areas of windows to the area of the external

envelope of the apartment

TC – thermal comfort, yearly amount of overheating hours over a certain threshold

Temporal map – a specific graph visualizing a period of days/months. Each day is positioned

on the x-axis and the hours of that day are on y-axis. The data is presented by different

colours or gradients

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Table of content

Abstract ................................................................................................................................. 3 Preface ................................................................................................................................... 3 Nomenclature ........................................................................................................................ 3 Table of content ..................................................................................................................... 4 1 Introduction ................................................................................................................... 1

1.1 Question and aim 1 1.2 Scope and objectives 1 1.3 Overall approach 1 1.4 Theoretical background 2

2 Case study building ....................................................................................................... 4 2.1 Case study building description 4 2.2 Energy performance, thermal comfort and daylight and electric light goal 5 2.3 General settings of energy and daylight model 5 2.4 Base case model 8

3 Methodology ................................................................................................................. 9 3.1 Software tools 10 3.2 Initial analysis 10

3.2.1 Climate analysis 10 3.2.2 Shading by surroundings 10 3.2.3 Building design 10 3.2.4 Limitations 11

3.3 Daylight, thermal comfort, energy performance 12 3.3.1 Energy and daylight model calibration 12 3.3.2 Oriel design 13

3.3.2.1 Initial choice of design 14 3.3.2.2 View out 14

3.3.3 Solar control 14 3.3.3.1 Initial optimization 15 3.3.3.2 Shading design 15 3.3.3.3 Natural ventilation 15

3.3.4 Visual control 16 3.3.5 Electric light design 16 3.3.6 Final energy demand and Daylight Factor 16

4 Results ......................................................................................................................... 21 4.1 Initial analysis 21

4.1.1 Climate analysis 21 4.1.2 Shading by surroundings 22 4.1.3 Building design 23 4.1.4 Limitations 24

4.2 Daylight, thermal comfort and energy performance 25 4.2.1 Oriel design 25

4.2.1.1 View out - sill height 25 4.2.1.2 Choice of oriel design 26 4.2.1.3 View out – horizontal angle 29

4.2.2 Solar control 29 4.2.2.1 Initial optimization 31

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4.2.2.2 Shading design 32 4.2.2.3 Natural ventilation 34

4.2.3 Visual control 35 4.2.4 Electric light design 36 4.2.5 Final energy demand and Daylight Factor 41

5 Discussion ................................................................................................................... 43 5.1 Initial analysis 43 5.2 Daylight, thermal comfort and energy performance 43

6 Conclusion ................................................................................................................... 46 7 Further studies ............................................................................................................. 47 8 Summary ..................................................................................................................... 48 References ........................................................................................................................... 49 Appendix 52

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1 Introduction

The population of people has been rapidly growing for past century. From 2,5 billion in

1950, the population almost tripled till 2015 (United Nations, 2016). Even though the

population is increasing, most of well-developed societies are getting older. In Sweden, in

1950 elderly (>65 yo) were 10% of the population, while in 2015 they are 20% (United

Nations, 2016). This trend together with the need to reduce energy use in buildings creates

a very specific demand, which is true for all buildings including homes designed for the

elderly.

This however is more difficult for nursing homes, where general research claims that elderly

people need warmer indoor conditions, shorter overheating periods and higher levels of light.

The needs of elderly people contradict with the aim of energy reduction i.e. keeping higher

indoor temperature in the winter is not energy-efficient, higher illuminance requirements

imply higher lighting power. In order to fulfil all the requirements, there need to be an

optimisation of glazed areas performed.

Higher lighting requirements demand more glazed areas, which implies higher solar gains

and more visual issues. Together with glazing size optimization, there is a need to design

solar and visual control devices, which would still maintain enhanced and not obstructed

view out from the apartments.

1.1 Question and aim

The aim of the thesis is to propose a design of the façade, solar and visual protection and

electric light system that will allow for better view out in an energy-efficient apartments for

the elderly. The design must comply with Miljobyggnad Silver certification regarding the

energy-efficiency and with some additional requirements of thermal comfort, daylight and

electric light design that will suit elderly people.

The research question of the thesis is what are the consequences of higher lighting

requirements of elderly people on energy performance and indoor thermal comfort?

1.2 Scope and objectives

The main goal of the thesis is to analyse daylighting possibilities and propose a shading and

glare control system, design and optimize artificial lighting system and perform thermal

comfort analysis with requirements set for the elderly people. The results of the analysis can

influence the size, form and orientation of the bay windows for patient rooms. A secondary

goal is to draw general conclusions regarding the optimization of daylighting, electric

lighting and thermal comfort in areas specifically designed for elderly people.

1.3 Overall approach

The thesis approaches problems in facade and daylight design and thermal comfort

evaluation. Below the general course of action undertaken in this study is presented:

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-literature review: understand the requirements and limitations of elderly people regarding

daylight and thermal comfort

-case study proposal: analyse the proposed shape of the building, propose an envelope

construction type, analyse the influence of the surrounding buildings.

-modelling and design analysis: propose different solutions for façade shape, analyse the

window-wall ratio in different proposals, design solar and visual protection, and adjust the

electric lighting to the requirements.

-final conclusions: compare the basic shape of the building with the proposed design,

compare different solar and visual protection systems, draw conclusions.

1.4 Theoretical background

The thesis is focused on thermal comfort, energy performance, access to daylight and view

out in the building for elderly people. There are many reasons due to which old people may

not be able to move or their perception of surrounding is different. Most of the research

regarding elderly without severe psychological, psychiatric or physical issues, claims that

old people have higher lighting demand, should have warmer indoor conditions and more

steady temperatures in summer.

An important factor of decreasing the heating and cooling demand is to allow for more

temperature slacks during both seasons. However it might not be possible due to the fact that

elderly people seem to be more sensitive to indoor conditions. Tsuzuki and Ohfuku (2002)

claim that elderly people are less sensitive to heat in winter and less sensitive to cold in

summer i.e. elderly don’t feel the heating in winter and therefore need higher indoor

temperatures. This would suggest that there is a need for more precise control of indoor

environment. Generally old people have decreased ability to regulate their body temperature

and it is strongly connected to higher mortality and morbidity. It was also confirmed that

increased temperature during heat waves in USA and Japan is a cause of higher mortality

(Havenith, 2001). According to Webb et al. (1999) old people with different disabilities tend

to rate their thermal comfort in very extreme ways even though the deviation from

comfortable conditions is small. Collins and Hoinville (1980) proposed a set of comfortable

temperatures for elderly people for a set of three different metabolic rates corresponding to

activities. These proposals are within the boundaries of ASHRAE (2013) recommendations.

Based on abovementioned reviews it is advised to control overheating periods in summer

and keep the comfortable temperature in the winter to reduce unsatisfactory indoor

conditions.

The human eye works in different environments, but highest performance is achieved when

proper amount of light with appropriate wavelength is reaching the retina. There are some

physical factors determining if the eye itself is able to process the image correctly. Ability

to change optical power, clarity of the eye components, ability to change the size of pupil

are some of the most important aspects. The amplitude of accommodation of the eye is

highest (D=15) around the age of 10-20 and rapidly decreases with age. Around 60 years old

person have accommodation of D=2 (Weale, 1990). Although the size of the pupil directly

influences the amount of light reaching the retina, absorptance of different parts of the eye

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play a crucial role as well. Pupil maximum and minimum size is also affected by age.

Maximum size decreases from 8 to 5 mm at the age of 80, while minimum size from 4 to 3

mm (Weale, 1982). According to Murata, most of the absorption happens in the lens

(Murata, 1987). Additionally the absorptance of the lens increases with age (Aliò & Anania,

2008) leading to even bigger issues. Another important factor is the scattering of the light in

different parts of the eye. Scattering of the light increases with age (Artal, 2008) and leads

to loss of contrast and decreased difference of luminance between objects.

The abovementioned changes apply to most of the normally aging people. However there is

a higher risk of eye diseases leading to partial or complete blindness, due to age. The diseases

like cataract, macular degeneration, glaucoma can lead to severe vision loss. Some of them

will affect the amount of light in the eye, which will then affect sleep patterns due to low

light levels reaching the rods in the eye (Cuthbertson, 2009). Others can increase the amount

of light scattering in the eye, which leads people to prefer darker environment.

These aspects only describe the possible reasons of the vision loss or blindness, but do not

actually address the consequences. Haegerstrom (1999) described a test based on 900 people,

using different methods, how the actual performance of vision worsen with age. One can

clearly see that the visual performance of the eye is drastically reducing with increased age

and there is a need for improvements.

Boyce (2014) in his book has summarized all crucial works and research in the field and

discussed different approaches. The aspects of vision loss are different and can overlap each

other in old age i.e. improving one, may worsen the others. The matter of improving the

vision of elderly people is very subjective and should be adjusted to each person. However

there are some general advices on how to improve the lighting conditions for elderly. Van

Hoof et al. (2009) adapted some previous research and standards and presented illuminance

levels in different parts of the apartment. For eating, reading and leisure at the level of dining

table, he proposed 500-1000 lx for elderly, same for hobby and work space, which can be

compared with 300-500 lx for average adults.

Apart from illuminance levels, another important factor is glare control. Some residents are

predicted not to be able to move from the bed for most of the time. According to de Waard

(1992) old people experience higher scattering of the light in the eye than young and it is

very important to adapt the design to their requirements. Glare may be caused by daylight

and artificial lighting. It is crucial to adapt the electric light design to both illuminance and

luminance requirements. The lighting system should illuminate the whole room to 500 lx,

same as daylight, and without causing disability or discomfort glare. This can be achieved

by ambient lighting such as “fixtures installed out of sight, a light valance, wall wash

fixtures, or a torchiere.” (Illuminating Engineering Society, 2009)

Last aspect of the lighting design for elderly people is the possibility of view out. According

to the results of the test performed by Ulrich (1984) patients recover faster when the view

out consists of greenery and especially water compared to view on the brick wall. Based on

research performed in Norway the advantage of window is daylight and view out and

greenery and water was rated as the most satisfactory outdoor scenery. It was also outlined

that the horizontal angle of the view out have an important role in improving the quality of

the view (Szybińska-Matusiak & Klöckner, 2015).

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2 Case study building

2.1 Case study building description

The thesis is based on a case study building that is being currently designed and developed.

The building have a general concept of the shape and interior design. Some of the crucial

parts of the design e.g. construction had to be assumed based on the future energy-efficiency

goals.

The building will be located in Staffanstorp near Lund. It is a recently designed and now

being developed region near Lund. The whole space is purely a living area with single and

multi-family houses, school, recreation area and park. Below is a figure showing the

surroundings of the case building.

Figure 2-1 Perspective view of the building with general measurements and proposed site plan

The building will be located next to one of the main streets and will be surrounded from the

north by couple of 7,5 meter high buildings and the street, from east by 7,5 and 10 m high

buildings and from west by one 7,5 meter high building. From the south there is an open

space park with planted trees. Figure 2-1 shows the 3D model of the building with main

measurements and proposed site plan with all the surrounding buildings. Each floor is 3

meters high with 30 cm of slab, which makes the floor 2,7m high internally.

Figure 2-2 Plan view with proposed, simplified interior design

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Figure 2-2 shows plan view of the building’s 3rd floor with proposed interior design. The

other two floors have similar design. On each floor there are 18 rooms, each with a floor

area of 30 m², facing east and west with their own separate bathrooms. In the central north

part there are treatment rooms and administration. In the central part of the building there

are common areas with kitchens and dining areas. The entrance to the building, located on

the ground floor, is facing north towards the street. Staff rooms are meant for washing and

drying machines and for cleaning equipment.

2.2 Energy performance, thermal comfort and daylight and

electric light goal

Due to the fact that the whole study is performed as a request from the client, there were

some requirements, which the building had to fulfil. The building is required to pass

Miljobyggnad Silver certificate. Among other, Miljobyggnad has rules regarding energy-

efficiency, daylight and thermal comfort. The general approach of the thesis does not include

simulation of the whole building and only deals with the matter of energy, light and thermal

comfort on the room level. The certificate requires to prove the achievement of all points on

the building level. In order to partially cover the required analysis, the final, simplified

simulation of the building was carried out to check the total energy demand. All chosen

rooms were checked if they fulfil the requirements of daylight. In order to fulfil the thermal

comfort requirements, a more precise simulation that would include the ventilation and

heating system was needed. Due to the scope of the thesis, this point was not checked in

accordance to the Miljobyggnad certificate but was rated based on the requirements of the

client.

Miljobyggnad Silver certification regarding the energy efficiency requires the energy use to

be less than 75% of specific energy demand according to BBR requirements (Sweden Green

Building Council, 2016) for specific climate zone. In case of Skåne, it is zone IV and the

BBR requirement is 80 kWh/(m2∙year) (Boverket, 2016). It also requires the energy peak

load to be less than 40 W/m2. The infiltration rate is assumed to be less than 0,0003

m3/(s∙m2envelope area) according to FEBY (FEBY, 2009). Specific energy demand includes

heating, cooling, domestic hot water and building electricity (Boverket, 2016). Thermal

comfort requirements were specified by the client. The indoor operative temperature has to

be between 22°C and 24°C. The temperature is allowed to rise up to 26°C for a maximum

period of 10 days – 240h.

According to Miljobyggnad Silver certificate, the building is supposed to have Daylight

Factor of 1,2% minimum, calculated in accordance to the provided instructions. Illuminance

requirements set by the client aimed only for working plane and were 750 lx. Based on the

literature review in section 1.4 the illuminance for general space was set to 500 lx and for

working plane 1000 lx. Electric light is supposed to be delivered by LED bulbs with 50000

h life expectancy and 3000K colour temperature. The colour rendering index should be at

least 80.

2.3 General settings of energy and daylight model

All further studies were based on the first model and all settings mentioned below were used

in all models and simulations. The design heating temperature was set to be 22°C. The

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heating setback temperature was not set, because the building is constantly occupied

throughout the year. Important factors in the energy modelling were the schedules of

occupancy, ventilation, heating and all specific internal gains i.e. lighting, equipment, and

human activity.

Occupancy schedule assumed that inhabitants leave their rooms for meals 3 times a day and

have some additional activities together, outside of their rooms. In winter they were assumed

to be out of their rooms for 5 hours during the day and in the summer for 7 hours (two

additional hours spent outside – in the garden etc.). The schedules for winter and summer

(see point 4.1.1) are presented in the figure below, together with lighting and daylight

autonomy schedule.

Figure 2-3 Occupancy schedules for winter and summer, Daylight Autonomy analysis hours, lighting schedule

On the left side of the figure 2-3 there is the occupancy schedule, different for winter and

summer. The dark upper columns are the schedule for winter, the light grey bottom columns

are the schedule for summer, both presented from 8:00 till 24:00. The filled column means

the room is occupied, the empty column means the room is empty. The thick dashed line

square is the indication, which hours were considered in the Daylight Autonomy analysis.

On the right side of the figure, there is the lighting schedule, which shows the fraction of the

lights that are turned on for the specific hour. According to Sustainability Workshop

(Autodesk, 2016) the lighting can be assumed to be 8 W/m2 for households, when using

traditional lights. The initial settings of lighting will be changed in the part, where electric

light is designed and it will be compared to the initial settings. The equipment gains were

neglected.

Due to the fact that the building is mainly occupied by elderly, the activities taken inside are

rather calm. The activity rate was set to be 60 W/person during the day and 40 W/person

during the night (Grondzik & Kwok, 2015). Night was assumed to be from 23 till 7. The

heating was scheduled to be constantly on from 1st October till 31st March.

Ventilation is supported by constant air volume system (due to the occupancy schedule) and

the flow was calculated based on the recommendations from Minimikrav på luftväxling

(Enberg, 2015). In households the exhaust ventilation in bathrooms is 15 l/s, additionally 1

l/(s∙m2) for each 1m2 over 5m2 of floor area. The supply ventilation is obligatory 0,35

l/(s∙m2). Based on Johansson (2008), energy-efficient buildings should have small under-

pressure. The supply-exhaust ratio was set to 0,9. The calculation and resulting supply air

flow is below:

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V = max {Vsup

Vex, Vex = Vb + Vadd, Vsup = qhyg ∙ Afloor [

l

s] (1)

Vadd = qadd ∙ (A − 5) if A > 5 [l

s] (2)

where

V – final airflow, [l/s]

Vsup – supply airflow . [l/s]

Vex – exhaust airflow, [l/s]

Vb – obligatory exhaust airflow in bathroom, 15, [l/s]

Vadd – additional airflow, if the area of the bathroom with no windows is bigger than 5m2,

[l/s]

qhyg – hygienic flow for all indoor spaces, 0,35, [l/(s∙m2)]

qadd – additional flow per area, 1, [l/(s∙m2)]

A – bathroom area, 6,32 [m2]

Afloor – total floor area of the room, 29,9 [m2]

Finally:

Vex = Vb + Vadd = 15 + 1 ∙ (6,32 − 5) = 16,32 [l

s]

Vsup = qhyg ∙ Afloor = 0,35 ∙ 29,9 = 10,47 ⌊l

s⌋

Vex > Vsup ⟹ Vsup = 0,9 ∙ Vex = 0,9 ∙ 16,32 = 14,69 ⌊l

s⌋ (3)

The infiltration was assumed to be 0,0003 m3/(s∙m2envelope area) according to FEBY (2009).

As it will be further explained in chapter 3.1.4, the analysis involved only separate rooms.

In each simulated room, the envelope’s construction was the same. Only external wall and

window was exchanging heat, the rest of the walls, floor and ceiling were assumed to be

adiabatic, however each surface had a construction type applied. U-values were calculated

in the software, based on material type, thickness and their heat conductivity. The

construction and resulting U-values (if relevant) were as follows:

Table 2-1 Construction types and U-values for modeled surfaces in energy simulations

Surface Construction U-value (with thermal bridge

influence) / (W/(m2∙K))

External wall External cladding, ventilated air gap, insulated wood

stud construction, vapour barrier, internal finishing

0,097 (0,115)

Internal walls Gypsum board, insulation on wood frame, gypsum

board

-

Floor Concrete -

Ceiling Concrete -

Window Triple glazed, with low-e coating, air gaps filled with

noble gas

0,78 (0,936)

The U-values for the window were taken from a manufacturer data sheets (Internorm, 2016).

According to the same source the window’s Solar Heat Gain Coefficient was 0,64 and visual

transmittance 0,7. Another important aspect of the simulations was reflectance of different

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materials used in daylight simulations. Based on the recommendations given by IESNA

(2009) and Grondzik and Kwok (2015), the reflectance values were applied. The values are

summarized in the table below.

Table 2-2 Reflectance values of modeled surfaces in daylight simulations

Surface type Internal

walls

Ceiling Floor External

walls

External

roof

Ground

Reflectance 0,5 0,8 0,35 0,35 0,35 0,2

Table 2-3 below shows all settings of the Radiance calculating engine used in the daylight

simulations. It include the grid size, distance of the grid from the floor, amount of ambient

bounces. The ambient settings of the simulation are the recommended settings from (Diva

for Rhino, 2016).

Table 2-3 Settings of the daylight simulation model

Grid

size, m

Distance

from floor, m

Ambient

bounces

Ambient

divisions

Ambient super-

samples

Ambient

resolution

Ambient

accuracy

Value 0,2 0,8 5 1000 20 300 0,1

2.4 Base case model

All models of the rooms (see 3.1.4) were simulated with the above-mentioned settings,

without any oriel or shading. The model was a comparison for daylighting, thermal comfort

and energy performance of later proposed designs of oriel, shading and visual control

devices. The models were simulated for GWR: 20-60% (increment 5%). They were later

referred as “base case”.

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3 Methodology

The scheme of work was divided into three main parts. 1) Initial analysis, 2) Daylight,

thermal comfort and energy simulations and 3) Electric lighting design. The figure below

describes graphically how the work load was divided and conducted.

Figure 3-1 Graphical overview of the methodology

First part consists of general analysis of the case building, weather conditions and states

main assumptions and limitations of the thesis. It also describes how the zones for the

analysis were chosen. Second part focuses on simulations of different oriel designs and

shading and visual control solutions. The aim of the oriel is to enhance the view out, however

the rooms facing the inner patio will not have the oriel. Shading and visual control devices

are supposed to adapt the building to the thermal and visual requirements. Third part is

strictly based on the solutions chosen in the previous part and shows the path to design

energy-efficient lighting system based on the actual need of light.

The general approach is that the study does not include analysis of the whole building and

will focus on presenting the relative results between rooms with different orientations. The

process of choosing the rooms is described in the chapter 3.1.4.

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3.1 Software tools

In the process of design there are multiple models with different geometry parameters

simulated. Most of the energy and daylight software tools does not offer the crucial geometry

flexibility. That is why the whole model was conducted in Rhino5 with a plug-in

Grasshopper, which gives vast amount of possibilities to quickly manage the model’s

geometry. Rhino 3D is a 3-D modeling tool used in multiple different area e.g. aero

engineering or jewellery design (RobertMcNeel & Associates, 2016). “Grasshopper® is a

graphical algorithm editor tightly integrated with Rhino’s 3-D modeling tools.”

(Grasshopper, 2016). The software was able to carry out all energy and daylight calculations

using an add-on Honeybee that utilizes broadly known energy simulation engine EnergyPlus

(EnergyPlus, 2016) and daylight engine Radiance (Regents of the University of California,

2016). Weather analysis was aided by Ladybug, which is add-on similar to Honeybee, used

mainly to interpret the EnergyPlus weather files (Roudsari, 2015).

3.2 Initial analysis

First part of the analysis is performed in order to generally describe the building and its

surroundings. It includes information about the building, annual weather conditions, solar

irradiation study, shading by surrounding buildings and basic analysis of different proposals

of the architectural design.

3.2.1 Climate analysis

Climate analysis was focused on weekly average temperature, wind speed and direction,

monthly sky conditions and luminance of the sky, and monthly global horizontal irradiation.

Temperature and wind study was divided into cold season (October – March) and warm

season (April – September) in order to better understand the conditions. Monthly sky

condition analysis described frequency of three different conditions of the sky: cloudy,

intermediate and sunny, based on the cloud index. The hours considered in the analysis were

from dusk till dawn in respect to the solar time. Additional data regarding the distribution of

the luminance in different parts of the sky confirmed the results of building analysis but were

not crucial for the study. The climate analysis was based on Denmark – Copenhagen

(EnergyPlus, 2016) weather file, apart from sky condition study, which was based on data

from Satel-Light (2016).

3.2.2 Shading by surroundings

Shading by adjacent buildings was analysed to visualize areas where daylight and solar gains

may be significantly lower. The analysis dates were 21 March, 21 June and 21 December.

As described in chapter 4.1.2 these areas were excluded from the analysis.

3.2.3 Building design

Due to the focus of the study, the envelope of the building was not thoroughly investigated

and interfered with. Other aspects against significant changes in the proposed design are the

limitations of the size of the site and restrictions given by local community regarding the

shape and height of the building.

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The study includes the influence of the angle between the core of the building (east – west)

and the wings (north – south). The first angle being checked was 90° and the increment was

5° towards open angle. Due to the size of the site, the angle was limited to 105°.

The study was based on the average Daylight Autonomy with threshold of 500lx for the

rooms facing inwards and outwards separately. The hourly schedule for the simulation was

8:00 to 18:00 with Daylight Saving Time included.

3.2.4 Limitations

The study did not cover any structural design and structure-related analysis. There were only

required U-values used in order to reach energy performance goals of BBR and

Miljobyggnad certification system.

For the sake of less complex simulations, the model for energy and daylight analysis was

divided into 6 rooms, one for each facade. The studied rooms and floor were chosen based

on DA500 and shading analysis of adjacent buildings. The chosen zones must have had a high

DA and should not be shaded by any buildings. If the rooms were shaded by other buildings,

it would decrease possible shading and visual issues and therefore would not be applicable

for the rest of the rooms.

Another limitation was excluding the corridor in the room as the part that is used only

temporarily and no activity is taken there for longer periods. The figure below shows which

areas were included in different simulation steps i.e. energy performance and thermal

comfort, daylight, electrical light.

Figure 3-2 Parts of the analysed rooms included in different simulation steps

Due to the simplistic approach to the model, depth of the window in the wall was not

considered and as a result walls have no thickness in the daylight model. However there was

a simulation performed to report relative changes in daylight due to the increased thickness

of the wall. Thermal bridges were simplified and additional 20% of U-value (Wall &

Berggren, 2013) was added on top of existing envelope properties.

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3.3 Daylight, thermal comfort, energy performance

Second part of the process was carried out on the level of the rooms only. The chosen rooms

from chapter 3.1.4 were analysed regarding the daylight, thermal comfort, energy

performance and glare probability.

3.3.1 Energy and daylight model calibration

A crucial part of the simulations was the evaluation of the models and simulation tools used

in the study. The energy model was evaluated by hand calculation based on simple physics

equations and rules of building physics. The daylight model was validated by another

simulation tool since there is no other optimal solution.

In the hand calculation for the energy model, the same settings are used as in the simulations.

The calculation was based on simplified calculation method. There was no occupancy, no

internal and solar gains, the indoor temperature was set to 19°C. The U-values were the same

as in table 2-1 (see chapter 2.3). There was only one external wall with window facing east,

the rest of the envelope was assumed to be adiabatic. The calculation was as follows:

𝑄𝐻,𝑛𝑑 = 𝑆𝑡ℎ ∙ (𝐻𝑡𝑟 + 𝐻𝑣𝑒) [𝑘𝑊ℎ

𝑦𝑒𝑎𝑟] (4)

where

𝐻𝑡𝑟 = ∑(𝐴𝑖 ∙ 𝑈𝑖) [𝑊

𝐾] (5)

𝑛

𝑖=1

𝐻𝑣𝑒 = 𝜌 ∙ 𝐶𝑃 ∙ 𝑞𝑣𝑒𝑛𝑡 ∙ (1 − 𝜂) + 𝜌 ∙ 𝐶𝑃 ∙ 𝑞𝑖𝑛𝑓 [𝑊

𝐾] (6)

where

QH,nd – energy demand needed for heating purposes of the building / space [kWh/year]

Sth – degree hours of heating season, defined by meteorological data, Sth=96360, [K∙h]

Htr – heat transmission losses [W/K]

Hve – heat ventilation losses [W/K]

Ai – area of the envelope surrounding the heated zone, external measurements, windows and

doors are measured by the opening dimensions, Awall=10,67, Awindow=2,68 [m2]

Ui – heat transfer coefficient of the envelope surrounding the heated zone, between inside

and outside, Uwall=0,115, Uwindow=1,187 [W/(m2∙K)]

ρ – density of the air, ρ =1,2, [kg/m3]

CP – specific heat of the air, CP = 1000, [W∙s/(kg∙K)]

η – heat recovery coefficient, η=0,75, [-]

qvent – total airflow of mechanical ventilation, based on equation 3, [m3/s]

qinf – total airflow of infiltration [m3/s]

𝑞𝑣𝑒𝑛𝑡 = 0,01469 [𝑚3

𝑠]

𝑞𝑖𝑛𝑓 = 𝑞50 ∙ 𝐴𝑡𝑜𝑡𝑎𝑙 [𝑚3

𝑠] (7)

13

where q50 – leakage at 50 Pa pressure difference, q50=0,0003, [m3/(m2∙s)]. The recommended value

of FEBY (FEBY, 2009).

Atotal – total area of the envelope facing outside, Atotal=13,35, [m2]

𝑞𝑖𝑛𝑓 = 0,0003 ∙ 13,35 = 0,004 [𝑚3

𝑠]

Finally:

𝐻𝑡𝑟 = 10,67 ∙ 0,115 + 2,68 ∙ 0,936 = 3,74 [𝑊

𝐾]

Hve = 1,2 ∙ 1000 ∙ 0,01469 ∙ (1 − 0,75) + 1,2 ∙ 1000 ∙ 0,004 = 9,2 [W

K]

QH,nd = 96360 ∙ (3,74 + 9,2) = 1246,8 [kWh

year]

Qh =QH,nd

Afloor=

1246,8

29,9= 41,7 [

kWh

m2 ∙ year]

The energy demand for heating according to hand calculations is 41,7 kWh/(m2∙year) and

according to energy simulation it is 40,7 kWh/(m2∙year). Looking at the number, the results

are fairly close, computational model demand is 97% of hand calculation demand, which

proves that the simulation gives the right order of magnitude for the energy use.

3.3.2 Oriel design

First step of the analysis was to choose a good oriel design in terms of daylighting, thermal

comfort and energy performance. Daylight was evaluated by DA500lx with the specific

schedule, which was based on occupancy (see figure 2-3). Thermal comfort was determined

by the hourly operative temperatures and amount of hours when temperature was over 24

°C. Energy performance was compared with the base case model. The additional floor area

of the oriel was not included in the daylight simulation grid, because it is rarely used

(similarly to the corridor part) and could needlessly increase the results.

Figure 3-3 Proposals of oriel designs and base case with indication of primary glazed areas and information about

floor area and envelope area

14

Figure 3-3 shows design proposals of the oriel. The areas considered in daylight simulations

were the same as in figure 3-2. Energy and thermal comfort simulations included additional

areas of oriel. All oriel were approximately 1 m deep in order not to obstruct the view from

adjacent rooms. Glazed areas were first added on the walls of the oriel and if necessary, new

windows were added on the other walls. This usually happened with GWR higher than

~40%. The oriel designs were applied only in the rooms facing outwards. In the rooms facing

the patio, the enhanced view out from oriel was not necessary due to the greenery planned

in the patio.

3.3.2.1 Initial choice of design

In the beginning there were five oriel proposals, which were checked for all factors. All

designs in all rooms were then simulated for different GWR varying from 20 to 60%. The

design was chosen based on the general results of all three factors, with DA being the most

important. According to Reinhart (2014) areas with DA of 50% and higher are considered

as daylit. All results were first compared based on DA. Only the results over 50% were

further considered, however additional glazing resulting in longer overheating period and

heating demand was not desirable. After the designs were chosen based on DA, two

remaining factors were evaluated. If possible, the case with shortest overheating period was

chosen.

3.3.2.2 View out

The aspect of the view out includes the horizontal view angle from the window and the

height of the window sill in case the inhabitant cannot move from the bed. The height of the

sill was estimated based on the geometry of person staying in bed. The head height was

estimated to be 100-120cm based on Neufert (2009) and the position of the bed was fixed

according to the initial plan. The lower the sill, the shorter the distance, after which the eye

sight reaches the ground and greenery. The horizontal view angle was measured based on

the same geometry restraints as sill height. For each different oriel proposal and for base

case, the angle was measured for the geometry with chosen GWR.

3.3.3 Solar control

After the oriel design and GWR were chosen, the shading device had to be designed. Only

external shading devices were simulated as being generally the most effective (Atzeri,

Cappelletti, & Gasparella, 2014). There were several proposals of external shading devices.

Due to the sun position during summer on the northern hemisphere, the shading period

should be symmetrical around the June solstice. However the specific climate and internal

loads may differ this period. In order to visualize when the shading was needed, temporal

maps of indoor operative temperature and solar gains through windows were created. Based

on the graph, period and specific hours when the shading should be applied, were chosen.

For that period all proposals were simulated and the best design was chosen. Additionally

there was a short analysis of solar gains through windows performed, which was supposed

to show the difference between diffuse and direct solar gains. The solar gains were calculated

for each window and direct solar gains were presented as the fraction of total solar gains

through windows.

15

3.3.3.1 Initial optimization

Due to the complexity of the task, in the beginning there were short optimizations performed

for each different type of shading device. The building has basically only east and west

facades and is almost perfectly symmetrical in the north-south axis so the optimization was

performed for the room number 1 (see figure 4-7) and then the designs were only adjusted

to other rooms. Each simulation was checking the best solution for one type of shading with

variable angles, depths and distances of the design. The general properties of the materials

used are described below for each type of the shading device. The properties were based on

data from the software ParaSol (2016) and were divided between energy and visual

properties due to different calculating engines. The settings are presented in the table below.

Table 3-1 Table with properties of shading devices, used in energy and daylight simulations

Shade type Energy Plus properties Radiance properties

Horizontal /

vertical blinds

Transmittance: 0 Transmittance: 0

Reflectance: 0.7 -

Emissivity: 0.9 -

Awning

Transmittance: 0.06 Transmittance: 0.06

Reflectance: 0.24 -

Emissivity: 0.9 -

Window screen

Transmittance: 0.14 Diffuse transmittance: 0.14

Reflectance: 0.57 Direct transmittance: 0

Emissivity: 0.9 -

All solutions were tested as fixed devices, but with possibility to be switched off after/before

certain hour. The differences in the shading devices’ schedules are result of the orientation

of each façade and symmetry of the whole building. For east facing facades the shading is

active before noon, for west facing facades the shading is active after noon.

3.3.3.2 Shading design

Based on the results of the initial optimization of shading devices, the best performing were

further developed. To make the design process simpler and more accurate, sun angles and

hourly solar gains through windows were analysed. For each window, the horizontal and

vertical angles were checked between the center of the window and the current sun position.

Due to the fact that 21st June is the solstice, all sun angles are symmetrical around this date.

The analysed period was from 15th April till 21st June. The hourly angles were cross

referenced with solar gains through windows, separately for each window. Based on these

information, the shading devices were adjusted to cover the strongest irradiation coming

from specific angles. The factors steering both the initial optimization and design were DA

and thermal comfort.

3.3.3.3 Natural ventilation

Once the suitable shading design was chosen, there were still possibly some overheated

hours. In order to prove that the building will not face overheating problems, there was a

study of natural ventilation through windows performed. The analysis was carried out for

the most overheated room for the warmest week in the weather file, which is 1st-8th August.

16

Based on Johansson (2008) and analysis of wind speed and direction, the maximum amount

of air changes was adjusted.

3.3.4 Visual control

Last step of daylight design was to design visual control device to protect the inhabitants

against glare. Unlike in the offices or work places, the position and direction of looking may

be changed even if elderly people are partially disabled. There might be some people unable

to move from the bed but still able to adjust their position to some extent. In this case visual

control is not of such importance however some improvements have been tested.

In order to reduce the amount of devices, the first simulation checked if turning the head the

other way would fix the problems. If not, the external shading device was checked for its

visual control properties and was adjusted. If the issues were still existing, other methods

and devices were further developed. Annual Daylight Glare Probability metric indicated

when the glare issues could have occurred, when looking at specific direction and was used

to quantify the problem.

3.3.5 Electric light design

The design of electric lighting was based on the results of visual control analysis. The

artificial light was supposed to deliver 500 lux as a general lighting, but the time when it is

turned on depends on the actual need. There was an illuminance sensor placed in the middle

of the room at the level of 80 cm and it reported the annual illuminance of the room in that

point. Based on the results the schedule, when the artificial lighting is needed was created.

The analysis was performed for the whole year between 8:00 and 22:00.

Three different lighting systems were analysed. General lighting provided by sets of three

T5 fluorescence lamps, LED lights redirected from ceiling and recessed LED lights. The

amount of luminaires was chosen based on the results of DIALux Evo software (DIAL,

2016), which is able to automatically adjust the number of lights and their position to reach

an illuminance threshold. The lights were chosen based on the similar luminous efficacy,

which was around 100 lm/W. The lighting systems were then compared based on the electric

energy use, which was calculated for different visual control solutions and for dimmable/not

dimmable switch.

3.3.6 Final energy demand and Daylight Factor

The final energy demand was calculated in order to check if the analysed areas fulfil the

requirements set for the building. The total energy demand included heating/cooling,

domestic hot water and building electricity. The heating demand calculated for separate

rooms cannot be taken as a representative value.

The Daylight Factor was checked in the rooms that were simulated in previous parts of the

analysis.

For that purpose a simplified model of the whole building was created. The zoning of the

model was the same as presented in figure 2-2, except the apartments adjacent to each other

were compiled into single zone instead of three separate zones. The building consisted of 3

floors. The windows in the apartments were simulated according to the results of the

17

analysis, the GWR in the kitchen from the south was 30% and in the administrative/treatment

rooms from the north GWR was 20%. The table below shows the U-values for the

construction in accordance to BBR requirements (2016). The values were calculated in the

energy modeling engine Honeybee, based on the materials’ properties.

Table 3-2 U-values used in the final model

Surface type U-value / (W/(m2*K))

External wall 0,115

Roof 0,08

Ground floor 0,103

Window 0,936

Each zone had its own schedule, internal equipment gains and lighting gains set. The

assumptions of internal gains were made based on the book of Grondzik&Kwok (2015) and

on the website Sustainable Workshops (Autodesk, 2016) and are presented in the table 3-3.

Table 3-3 Lighting and equipment power density and number of people for all simulated zones

Zone Lighting power density /

W/m2

Equipment power density /

W/m2

Number of people

Corridor 10 0 5

Kitchen 10 9 – average value 18

Administration /

treatment

10 4 – 4 laptops, one printer 4

Storage 10 80 – 2 washing machines, 1

dryer

1

The occupancy schedule for apartment rooms was presented in figure 2-3 and the lighting

schedule was based on the results of the optimization, described in the Section 3.3.5. The

schedules for other zones were estimated based on the experience of the author and

reasonable assumptions taken for the building and are presented in the figures 3-4 to 3-7.

Each figure, apart from the schedule for the corridor, presents three different types of

schedules: occupancy – dark grey, lighting – light grey, equipment – black. The x-axis are

the hours of the day, y-axis the fraction of the schedule from 0 to 1.

18

Figure 3-4 Occupancy and lighting schedule for the corridor zone set for the final building model

Figure 3-4 presents the occupancy schedule and lighting schedule for corridor area. It is

assumed that during the night there is a minimum level of lighting maintained for safety

purposes.

Figure 3-5 Occupancy, lighting and equipment schedule for kitchen and dining room set for the final building

model

Figure 3-5 presents the occupancy, lighting and equipment schedule for the kitchen and

dining area. The lighting is assumed to be lower during the day because the kitchen is

oriented towards south and has large glazed areas. The occupancy schedule corresponds to

the schedule of all inhabitants in the rooms presented in figure 2-3.

0

0.2

0.4

0.6

0.8

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

Fra

ctio

n o

f th

e sc

hed

ule

Hours of the day

occupancy

lighting

0

0.2

0.4

0.6

0.8

1

8 9 10 11 12 13 14 15 16 17 18 19 20

Fra

ctio

n o

f th

e sc

hed

ule

Hours of the day

occupancy

lighting

equipment

19

Figure 3-6 Occupancy, lighting and equipment schedule for administrative and treatment rooms set for the final

building model

Figure 3-6 shows occupancy, lighting and equipment schedules for administrative rooms

located in the northern part of the building. Similarly as for the kitchen, the lighting is higher

in later hours. The occupancy is set as in typical offices.

Figure 3-7 Occupancy, lighting and equipment schedule for administrative and treatment rooms set for the final

building model

Figure 3-7 presents the schedules for storage rooms, where washing and drying machines

are located. The low fraction value of equipment schedule was decided based on the fact that

these machines are not used every day but once for two or three days.

The ventilation was set to be 0,35 l/(m2*s) for the whole building apart from the rooms. The

ventilation in rooms was calculated in section 2.2. The infiltration was calculated the same

way as in point 3.3.1 based on the recommendations of FEBY (2009), which recommends

0,0003 m3/(s*m2) of infiltration flow per envelope area. Building domestic energy was

calculated based on the recommendations of FEBY together with domestic hot water

demand (2009).

0

0.2

0.4

0.6

0.8

1

8 9 10 11 12 13 14 15 16 17 18 19 20

Fra

ctio

n o

f th

e sc

hed

ule

Hours of the day

occupancy

lighting

equipment

0

0.2

0.4

0.6

0.8

1

11 12 13 14 15 16 17

Fra

ctio

n o

f th

e sc

hed

ule

Hours of the day

occupancy

lighting

equipment

20

Daylight Factor simulation was based on the same settings and geometries as in previous

daylight simulation (see chapter 2.3) with only one exception. The simulated area included

part of the corridor that was excluded in other simulations. The Miljobyggnad instructs to

use the whole area of the simulated rooms and crop it by 1 m from the darkest wall in the

room. This forced the simulation to include part of the corridor in the analysis. The simulated

area is presented in the figure below. On the left, there is a room without oriel, which was

the case for room 3 and 4 (see figure 4-7). On the right, there is a room with oriel, which

was the case for all other rooms.

Figure 3-8 Area of the room taken for the analysis of DF in accordance to Miljobyggnad requirements

21

4 Results

This section presents all the results of simulations that were crucial for the study. The section

is divided respectively to the methodology section above and is following similar order.

4.1 Initial analysis

Initial analysis included climate analysis, shading of the case building by adjacent buildings,

study of the shape of the building and summary of limitations. All results are presented in

the chapters 4.1.1-4.1.4.

4.1.1 Climate analysis

Figure 4-1 presents average monthly temperature for Copenhagen and wind roses. Each

column represents the average temperature during one month, starting from January and

includes 24 hours of each day. The highest average temperature occurs in August and is

16,9°C and the lowest is in February, 0,2°C. The graph was divided into warm season and

cold season, represented by light and dark grey respectively. The months between April and

September are the warmest. Although October is still warm, further study of solar irradiation

and sky frequency indicates that it is much less sunny and was counted as cold season.

Figure 4-1 Monthly average dry bulb temperatures, most frequent wind directions and wind speeds

The wind analysis presented on the right side of the figure 4-1 was similarly divided between

two seasons. The graph shows wind speeds higher than 1 m/s. This condition eliminates

4269 hours of the weather file, when the wind is lighter. The wind speed is expressed in m/s

and the brighter the colour, the stronger the wind. Each branch of the graph is one of the

cardinal directions with north being on top. Parts of the graph divided by white, thin lines

stand for 2% frequency of the wind from that direction with a certain speed. One can clearly

see that the most frequent winds come from west, south west and south for both seasons.

The analysis of the sky types frequencies and average solar global irradiation, as presented

in figure 4-2, is completing the figure 4-1. The stacked columns with axis on the left indicate

22

the sky type and the frequency of the certain sky, without accounting for the night. The black

dots with the axis on the right indicate the average monthly horizontal irradiation in kWh/m2.

The graph shows even more clearly the division of warm and cold season and shows that

October can be treated as cold season with lower irradiation and much less sunny skies even

though the average temperature wouldn’t indicate so.

Figure 4-2 Monthly sky frequency, average monthly horizontal irradiation

The figure below shows frequency of different luminance thresholds appearing in 13

different zones of sky based on the data acquired from Satel-Light (2016). The levels were

1000, 5000 and 9000 cd/m2, and the sky was divided into 13 zones. These graphs show how

bright the sky is in different parts and how often it happens. The low bright sky is generally

uniform, but for higher thresholds southern parts of the sky are brighter. This helped

justifying some of the results presented later.

Figure 4-3 Frequency of luminance thresholds in 13 different parts of the sky

4.1.2 Shading by surroundings

Shading analysis was performed for three dates: 21st March, 21st June and 21st December.

An additional study was carried out to visualize how the surrounding buildings would shade

the eastern façade. All pictures are presented in figure 4-4. The upper part shows the shading

of the whole building and surroundings for three dates from sunrise to sunset. The bottom

0

20

40

60

80

100

120

140

160

180

0

20

40

60

80

100

J F M A M J J A S O N D

Avg.

mo

nth

ly g

lob

al h

ori

zonta

l

irra

dia

tio

n /

kW

h/m

2

Sky f

req

uen

cies

/ %

Months

SUNNY INTERMEDIATE CLOUDY

23

part shows how the surrounding buildings shade the eastern façade. For both cases the darker

colour indicates more hours when the area is shaded. The second analysis was performed

after it was clear that the adjacent buildings will shade the case building. The results were

later used (see chapter 4.1.4) to exclude lower parts of the building from the simulation due

to the lower solar gains and possible lower visual issues.

Figure 4-4 Top: Top view with shading marks throughout the whole day for 21st March, June and December;

Bottom: Shading of the east wall on the same dates

4.1.3 Building design

As mentioned in chapter 3.1.3 the building had some geometry restraints, but it allowed for

opening of the building to some extent. The analysis was split into two parts, one included

the rooms facing outwards (light grey, the other, rooms facing inwards. When increasing the

angle between core and wings, rooms facing patio (inwards) are turning to south, which is

the brightest part of the sky (see figure 4-3). The rooms facing outwards are turning to north,

which is the darkest part of the sky.

Figure 4-5 Plan view of the building with 4 angles between core and wings, Daylight Autonomy of two types of

rooms on different floors for 4 angles between core and wings

On the left side of the figure 4-5 there are plans of the building with different angles between

core and wings, as it is shown. The right part is the graph representing dependence of

daylight autonomy on the angle between core and wings in the building. The black lines

show DA500lux for the rooms that are inside, the light grey lines show DA500lux for the rooms

that are outside. Different style of the line means different floors.

24

The results clearly show that “opening” the building has influence on both types of rooms,

however the higher the room is located the less it is influenced. The biggest difference occurs

in the rooms facing inwards on the first floor, but it is only a matter of 4 percentage points

of DA500lux. The equilibrium is somewhere between 95° and 100° angle, but the bigger angle

is chosen as the best performing.

4.1.4 Limitations

The main limitation is the choice of rooms that were studied. Based on the shading analysis

and initial daylight analysis, first two floors were excluded as being directly shaded by other

buildings or the building itself. It was shown in the figure 4-4 where the shading was studied.

Further results of daylight autonomy and the settings of the analysis are shown in the figure

4-6 below.

Figure 4-6 Results of Daylight Autonomy analysis for rooms on the 3rd floor

On the left side of the figure, there is a plan of rooms on the 3rd floor with the results of

daylight autonomy presented in percentage next to it. Darker colour inside the rooms,

indicate lower daylight autonomy. The analysis was performed in order to choose the rooms

for further analysis. The differences between rooms on each façade separately were smaller

than 4%. Based on that and on the fact that the first two floors are more often shaded (see

figure 4-4) the chosen rooms are located on the 3rd floor, where future solar and visual issues

are more probable than on first two floors. The figure 4-7 below shows, which rooms on the

3rd floor were chosen, based on the results presented in the figure above, and the numbers

assigned to them.

25

Figure 4-7 Apartments chosen for further analysis with numbering next to them

The last limitation excluded the wall depth from the simulations. There was an analysis of

the influence of the depth of the wall for the case of room 1. The Daylight Autonomy was

simulated for that room with different depths of the wall. The simulation settings were the

same as described in chapter 2.3 and the GWR was 30%. The results showed that the

difference in DA, when the wall was 0,5 m thick and 0 m thick, is 6 percentage points,

decreasing from 49,9 to 43,9%. The graph visualizing the results is in Appendix A.

4.2 Daylight, thermal comfort and energy performance

4.2.1 Oriel design

In chapter 3.2.2 there were 5 designs of oriel proposed and these design were further

evaluated regarding the view out, daylight, energy performance and thermal comfort.

4.2.1.1 View out - sill height

A first short analysis was aimed to set a height of the low edge of the glazing. It was crucial

for further daylight models. For all designs the distance between the eye of the inhabitant

and the ground was measured in a straight line for different heights of the glazing edge. Left

part of the figure 4-8 visualize the process with values for the base case.

26

Figure 4-8 The analysis process and results of measurements of distance between the eye and the ground for

different heights of the window sill

The small circle is the head of the inhabitant and the lines indicate the eye sight. The “d” is

the distance from the eye of the person staying in bed to the middle of the glazing, and h is

the height of the bottom glazing edge. Distance “d” is different for each design and is the

only variable that differentiate the designs. The graph on the right side of the figure 4-8

shows the results of the analysis. Y-axis shows the distance from the glazing edge to the

floor and X-axis shows the shortest distance between eye and the ground. “BC” refers to the

base case design, “D1…D5” refers to oriel designs from 1 to 5. When the height of the edge

is 40 cm the distance between the eye and the ground for all designs is fairly similar, ranging

from 33 m for base case, to 48 m for design four. This height was chosen as the best result

and middle ground between normal height of the edge of glazing (around 90 cm, when the

sill is 80 cm) and the case when window starts on the floor level.

4.2.1.2 Choice of oriel design

Next step was to choose the best oriel. The following figures from 4-9 to 4-11 show the

results of daylight autonomy, energy performance and thermal comfort for all oriel designs

and for base cases. The results were split between the rooms to keep the clear layout. The

graphs below represent only one room, the rest of the results are presented in the appendix

A. All graphs have the glazing area in m2 as x-axis and daylight autonomy, heating demand,

amount of overheating hours as y-axis, respectively. All results, when daylight autonomy

was higher than 50%, are black, all the rest are light grey. In all graphs the dots are for base

case results and the squares are for all oriel cases. To make the results more clear and to

draw more general conclusions, all oriel designs are marked with the same sign of square.

27

Figure 4-9 Results of daylight autonomy for base case (dots) and oriel designs (squares) for room number 1

In the graph 4-9 it can be clearly seen that the results form short lines. They indicate separate

glazing to wall ratios. Base case has always the smallest glazing area and yet it has the

highest daylight autonomy percentage.

Figure 4-10 Results of heating energy demand for base case (dots) and oriel designs (squares) for room number

1

Heating results are not so distinguishable, the differences between base case and other

designs are not clear. For similar glazing area between six and eight square meters the

heating in case of designs can be both higher and lower than base cases results.

10

20

30

40

50

60

70

80

2 4 6 8 10 12

Day

light

auto

no

my 5

00 lux /

%

Glazing area / m2

R1,<50%

BC R1,<50%

R1,>50%

BC R1,>50%

4

6

8

10

12

14

16

18

2 4 6 8 10 12

Hea

ting e

ner

gy d

eman

d /

(kW

h/(

m2

*yea

r))

Glazing area / m2

R1,<50%

BC R1,<50%

R1,>50%

BC R1,>50%

28

Figure 4-11 Results of overheating hours over 24°C for base case (dots) and oriel designs (squares) for room

number 1

Thermal comfort analysis, based on the amount of hours when the indoor operative

temperature is higher than 24°C, shows a bit more clear results. One can almost distinguish

the groups of the same GWR and the differences between the base case and other designs

are rather small, when looking at the magnitude of the unit.

To make the general conclusions, the results of all designs for each room were averaged for

each GWR and presented in graphs with the results of the base case averaged for each room.

This helped visualize all results in three graphs and draw conclusions.

Figure 4-12 Summary of daylight autonomy, heating demand and overheating hours, averaged for base cases

(dots) and oriel designs (squares) for all rooms 1-6

1000

1500

2000

2500

3000

3500

4000

4500

2 4 6 8 10 12

Over

hea

ting h

ours

, o

ver

24

oC

/ h

Glazing area / m2

R1,<50%

BC R1,<50%

R1,>50%

BC R1,>50%

10

20

30

40

50

60

70

80

2 4 6 8 10 12

Day

light

auto

no

my 5

00 lux /

%

Glazing area / m2

AVG

BC

AVG4

6

8

10

12

14

16

18

2 4 6 8 10 12

Hea

ting e

ner

gy d

eman

d /

[kW

h/(

m2

*yea

r)]

Glazing area / m2

AVG

BC AVG

1000

1500

2000

2500

3000

3500

4000

4500

2 4 6 8 10 12

Over

hea

ting h

ours

, o

ver

24

oC

/ h

Glazing area / m2

AVG

BC AVG

29

Figure 4-12 shows all results averaged as it is described above. It is clear that the base cases

for all GWR have better results. For smaller glazing areas, daylight autonomy is always

higher and overheating periods are shorter. Smaller glazing areas also guarantee smaller

heating demand for each GWR. For the sake of the study the best performing oriel design

was chosen. The choice was based on daylight autonomy, but took other two factors into

account. The fifth design with GWR=40% was chosen as best performing. Tables with all

results of daylight, heating and thermal comfort, based on which the choice was made, are

presented in Appendix A due to the size.

4.2.1.3 View out – horizontal angle

The last factor that was taken into account was the view out from the position of person lying

in bed. The analysis was performed for a chosen GWR and all designs and base case were

compared. Figure 4-13 shows how the geometry was measured along with the results.

Figure 4-13 Analysis process and results of measurements of horizontal view angle from the perspective of

person in bed for base case and oriel designs geometry

It is clearly seen that with relatively small glazing area, in comparison to other designs, the

fifth design offers the biggest horizontal angle of view. The base case has a very similar

result with even smaller glazing area.

4.2.2 Solar control

In sections 4.2.2.1-3 the results of shading analysis are presented. They include initial

optimization part with only the most significant results shown, shading design with the

process description and the results and short analysis of natural ventilation potential.

In the beginning the schedules for shading had to be created. Based on the solar gains through

windows and indoor operative temperatures simulated in room 1 and room 6, the temporal

maps presented in figures 4-14 and 4-15 were created. All graphs show results from March

to October and the y-axis shows the hour of the day. On the left side of both figures, indoor

operative temperatures presented. Every hour when the temperature is below 24°C is white.

On the right side of each figure, direct solar gains through windows are presented.

30

Figure 4-14 Temporal map of operative temperature and direct solar gains from March to October for room 1

Figure 4-14 clearly indicates that the highest solar gains occur from 6 to 12 in the morning.

This is also noticeable on the temperature graph where a slightly darker gradient occurs from

6 in the morning and later. The graph with operative temperature also shows that the first

periods of overheating occur in April and the last in September.

Figure 4-15 Temporal map of operative temperature and direct solar gains from March to October for room 6

Figure 4-15 also clearly shows when the highest solar gains occur. It is noticeable on the

graph with operative temperature. The overheating period starts around 12 and ends around

19. Both figures with operative temperature indicate that the shading should be applied from

April to September. Based on figures 4-14 and 4-15 shading period for rooms facing east is

5 to 12 from April to September and for rooms facing west is 12 to 19 during the same

months. The graphs show only direct solar gains and not diffuse radiation, which could make

a difference in the results. The total, direct and diffuse solar gains were reported for each

window and the table below shows the direct solar gains as a fraction of total gains. The

remaining part is diffuse radiation. Window 1 is always the window facing south, or in case

of room 3 and 4 there is only one window.

Table 4-1 Table with percentage values of ratio direct-total solar gains for all rooms and windows

Percentage of total solar gains

window 1 window 2

window 1 window 2

room 1 41% 23% room 4 39%

room 2 38% 15% room 5 37% 18%

room 3 38% room 6 38% 22%

It can be clearly seen that the direct solar gains are no more than 40% of all solar gains on

average. That would suggest that keeping the shading longer will cut down the overheating

hours.

31

4.2.2.1 Initial optimization

As mentioned in the methodology, the initial optimization was a way to provide some

general idea of the geometry properties that could be later analysed and adjusted. In this

section only the geometry properties and final results are presented due to the size, the whole

table of results can be found in Appendix A. There were four different shading systems

analysed only for room 1 due to the reasons explained in methodology in chapter 3.3.3.1.

Figure 4-16 shows the systems and all variables tested in the optimization.

Figure 4-16 Geometry and transparency properties of shading devices analysed in initial optimization process

For horizontal and vertical blinds there were three variables. The first one was the “D” –

depth of the slats. There were three values tested and for each value there were different

distances (“d” on the figure above) between blinds tested. Each case was simulated with four

different angles. Due to the limitations of the software, the awning could not be simulated

as one piece covering the whole height of the window and was split into two 1 meter-long

parts. It was tested for different angles as shown on the figure 4-16. The screen was a material

that covered the whole window and was simulated with different visual transmittance

properties. The blinds were simulated as metal sheets and awning and screen were simulated

as translucent material. The following table summarizes the best performing setups and their

results.

Table 4-2 Results of daylight autonomy, heating demand and overheating hours for initial optimization process

for the best performing setups of shading devices

Name Setup

Results

Daylight

autonomy, %

Heating demand,

kWh/(m2*year)

Thermal comfort,

hours above 24°C,

h

Horizontal blind D=0,25, d=0,15, a=30 20,3 11 577

Vertical blind D=0,25, d=0,15, a=45 22,6 10,2 1151

Awning a=15 22,1 10,8 690

Screen Vis. trans.=0,2 20,4 10,4 995

According to the initial analysis the best performing shading regarding the amount of

overheating hours are horizontal blinds. The results should not be taken as a comparison for

32

the further analysis, because the shading was simulated to be always on. The results however

are still relevant in respect to each other.

4.2.2.2 Shading design

Chosen designs were further studied and adjusted. For each window in each room an analysis

of hourly solar gains during shading season was carried out. An analysis of hourly sun

position during shading season in regards to the windows was also performed. For each

window the total solar gains were added for each separate hour, and the sun position analysis

allowed to draw the horizontal and vertical angles between window and sun for these hours.

All this data was cross referenced and based on that the following graphs were drawn. Figure

4-17 presents the hourly solar gains analysis only for room 1. The rest of the results are

presented in the Appendix A.

Figure 4-17 Summarized hourly solar gains and horizontal and vertical sun angles for two windows in room 1

The “south” window is the one that is facing south east direction, the other is facing north

east. The vertical sun angle is the angle that is created on a vertical plane between horizontal

line and line between sun position and window. The horizontal angle is the angle created

between the line perpendicular to the window and the line between the sun position and the

window, on a horizontal plane. The angles show the possible sun positions for that hour

during the whole shading period. E.g. sun position at 7:00 on 1st April is around south east

but on 21st June is around east. That is why the hours overlap each other. The numbers next

to each angle state the hour and summed up solar gains for that hour for the whole shading

period. The solar gains are given in kWh/m2 for each window.

The shading was adjusted to cover the highest solar gains but also to block the remaining

gains, which sometimes resulted in more “covering” geometry. The figure below shows for

which dates and hours the horizontal and vertical blinds were adjusted. The remaining two

shading systems were not changed due to theirs lack of adjustable geometry. Figure 4-18

shows the adjusted shading geometry for Room 1. The remaining rooms are presented in the

Appendix A.

33

Figure 4-18 Geometrical adjustments of horizontal and vertical blinds in room 1

Each blind has the same depth of 25cm. Depending on the geometry of the oriel and sun

angles analysis, all blinds were adjusted to appropriate hours and angles. The figure 4-19

shows the result of thermal comfort and daylight autonomy. The x-axis shows the number

of the rooms that were analysed, the y-axis shows amount of hours when the operative

temperature is above 24°C and percentage of daylight autonomy respectively. Different

shading devices can be recognized by different shapes. Below the graphs, there are tables

showing the reduction of values as a percentage of the results without shading applied. It

shows how different solutions work in each room. For both thermal comfort and daylight

autonomy, the results for room 3 and 4 presented in the figure 4-19 are for rooms without

oriel as explained in chapter 3.3.2.

Figure 4-19 Top: Results of overheating hours and daylight autonomy for all rooms and optimized shading

devices; Bottom: relative reduction of the values in comparison to case without shading device,

The values highlighted under the thermal comfort graph show, which device achieved the

biggest reduction of overheating hours in case of each room separately. Similarly the values

highlighted under the left part of figure 4-19 show, which device achieved the smallest

reduction of daylight autonomy, which in this case is positive. The best shading device was

34

chosen based on the thermal comfort results and it was the window screen. It also offers the

best view out, without any obstructions, however the view is dimmed due to material

properties.

Another important fact was the comparison of oriel design with the base case with the same

shading settings. In case of the base case design, the shading adjustments were performed in

the same way; they were carried out for oriel design, using hourly solar gains and sun angles

analysis. The figures with adjusted shading geometry can be found in the appendix A.

Table 4-3 Results of overheating hours and daylight autonomy presented as a percentage of the values for room

with oriel

Room

Shading

Thermal comfort Daylight autonomy

1 2 3 4 5 6 1 2 3 4 5 6

HOR 101% 102% - - 105% 104% 120% 120% - - 114% 113%

VER 94% 93% - - 96% 96% 117% 119% - - 110% 112%

AWN 94% 85% - - 97% 97% 115% 115% - - 112% 111%

SCR 95% 85% - - 96% 95% 118% 117% - - 113% 114%

Table 4-3 shows the base case results related to the results for oriel design (see figure 4-19)

in percentage. The base case design has 4 percentage points lower amount of overheating

hours and 15 percentage points higher daylight autonomy on average.

4.2.2.3 Natural ventilation

As it is clearly shown in figure 4-19, the amount of overheating hours is far from the

requirements set for the building (less than 240 hours above 24°C) and there is a need to

further lower the temperature during the shading season. Based on the wind analysis in

chapter 4.1.1 the most common wind is from west. The most overheated room is Room 6

facing west. Based on the information provided by Teppner (2014), the amount of air

changes was set to 5 h-1. The windows were constantly open during the analysed period.

Figure 4-20 shows the results of the analysis for the warmest days in the weather file, which

are first days of August.

35

Figure 4-20 Lefts axis: operative temperatures for 1st-8th August in Room 6, without shading, with shading, with

shading and natural ventilation; right axis: outdoor dry bulb temperatures

The left y-axis shows the indoor operative temperature, the right y-axis shows outdoor dry

bulb temperature. The black line is the temperature in the room without shading, dark grey

line is the temperature when the chosen shading is applied, and light grey line is the

temperature when the windows are open. The dashed horizontal line is the requirement that

the indoor temperature will not rise over 26°C. The dark grey dotted line is the outdoor dry

bulb temperature.

The graph very clearly shows the impact of both shading device and natural ventilation. For

the peak temperature on 5th August, the shading reduced the operative temperature by 13°C

and the natural ventilation reduced it further by 5°C. The indoor operative temperature will

not rise above 26°C, which is the requirement for the building. Looking at the impact of the

shading device coupled with natural ventilation, it can be assumed that this should solve

most of the overheating problems, when the temperature rises over 24°C.

4.2.3 Visual control

The glare analysis was performed only for two rooms on two opposite facades, room 1 and

room 6. The simulations were carried out from the point of view of a person staying in bed.

Annual Daylight Glare Probability simulation checks every hour of the year and if the DGP

was higher than 0.35, it was noted on the graph in grey scale. Darker colour means bigger

glare issues, black colour means that the glare is intolerable and DGP is over 0.45. The figure

below shows the results when there is no shading applied, also during shading season and

when the person turns the head away from the window.

15

20

25

30

35

40

45

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9

Outd

oo

r d

ry b

ulb

tem

p.

/ °C

Ind

oo

r o

per

ativ

e te

mp

erat

ure

/ °

C

Days

no shade shade shade+vent outdoor

36

Figure 4-21 Results of annual Daylight Glare Probability for room 1 in case of normal view and view when

looking away from the window

On the left side of the figure 4-21 there are approximate fields of view from the point where

the head of the observant is. First one is for the person looking at the window, second is

when the person looks away. On the right side of the figure there are temporal maps of

Daylight Glare Probability for both views. Turning the head away solves almost all problems

but the remaining issues are in the range of intolerable glare. In this case it is 69 hours of

intolerable glare. Applying the shading during summer and additionally lowering it during

the hours when there is direct sunlight reduces the amount of hours with intolerable glare to

13h without looking away. However lowering the shading reduces the solar gains during the

heating season and increases the heating from 9,4 to 10,9 hWh/(m2*year), thus by 16%. It

will also influence the amount of light in the room, which will affect the electric lighting

design. The table below shows the effects of keeping the head away and applying shading

on heating demand in base case for Room 1 and in the other two cases for room 6.

Table 4-4 Results of annual DGP and corresponding heating demand when using chosen external shading

device as a visual control device

Room 1–oriel Room 1–base case Room 6–oriel Room 6–base case

Shading Away Shading Away Shading Away Shading Away

DGP, hours over

0,35 13 69 36 94 15 183 11 202

Heating demand,

kWh / (m2*year)

10,9 9,4 11,0 9,9 13,2 8,7 13,5 8,3

It is clear that using external shading during heating season as a visual control device will

result in increased heating demand.

4.2.4 Electric light design

There were three different electric light designs proposed. There were suspended T5

fluorescence lamps, LED indirect lighting and recessed LED lamps tested. The figure below

shows the DIALux (2015) software proposals of lamps placement in the room, except the

indirect lights design, which is the author’s.

37

Figure 4-22 Proposal of electric light designs for three different systems: T5 fluorescent lights, indirect LED,

recessed LED

The reflectance of the surface was set to be the same as in the daylight simulations: floor-

20%, wall-50%, ceiling-80%. The table below presents all relevant information about the

designs.

Table 4-5 Properties of analysed systems

Name Power, W

Luminous

flux, lm

Luminous

efficacy, lm/W

Power intensity,

W/m2

T5 Philips, TPS466, 3xTL5 4 * 84 7800 93 19,4

LED indirect Philips, WL484W 5 * 50 5200 104 14,5

LED recessed Philips, RC120B 6 * 38 3700 97 13,2

In order to calculate the real lighting demand, the illuminance sensor was placed in the

middle of the simulated room and the illuminance was calculated. The shading was always

turned on in regards to the shading schedule in the room. Apart from shading period the

situations tested were, when the shading was used as a visual control device or when the

inhabitant was turning the head away without using the shading. The whole analysis was

performed for room 1 and 6 for the base case and for the case with oriel design.

The initial calculation was ran for the case when the light is always fully on if it is needed,

based on the illuminance values for two visual control solutions. There were two energy

saving systems tested- dimming system and occupancy sensors. The energy for dimming

system was calculated based on the fraction of the illuminance that was remaining to reach

the target of 500 lux. E.g. if the daylight provided 300 lux, the system without dimmer would

switch on the lights with full power and the system with dimmer would switch on the lights

with (500-300)/500 = 0.4 of the full power. The occupancy sensors were simulated based on

the occupancy schedules of the room presented in figure 2-3.

Figure 4-23 presents the annual lighting energy demand of three different designs with and

without dimming system, while using the shading as a visual control device and while

turning the head away. The results are presented for room 1.

38

Figure 4-23 Results of lighting energy demand for two different visual control solutions and comparison of

energy demand for systems with and without dimming system

The columns with solid colour represent the results for the designs without dimming system

e.g. “T5 N” in the legend, while the columns with pattern represent the results for the same

designs with dimming system e.g. “T5 D”. It can be clearly seen that the lighting provided

by LED lamps requires 25-32% less energy than the T5 design. What is important, the visual

control solution does not affect the lighting in a significant way.

Table 4-6 shows the results for the rest of the rooms. The column “dimming system” shows

the average percentage difference between the design with and without dimming system.

The column “occupancy sensors” presents the percentage difference between the designs

(already equipped in dimming system) with and without occupancy sensors. The last column

presents the final lighting energy demand with dimming system and occupancy sensor

installed.

20.0

30.0

40.0

50.0

60.0

70.0

SHADE AWAY

Lig

hti

ng e

ner

gy d

eman

d /

(kW

h/(

m2*yea

r))

T5 N T5 D LED 1 N LED 1 D LED 2 N LED 2 D

39

Table 4-6 Results of lighting energy demand for different visual control solutions and two energy saving

systems: dimming and occupancy sensors, and final lighting energy demand with energy saving systems

applied

Room

Lighting

design

Lighting energy

demand /

kWh/(m2 *year) Dimming

system

Occupancy

sensors

Final lighting energy

demand/ kWh/(m2 *year)

Visual control solutions Visual control solutions

Shade Away Shade Away

Room 1

T5 65,1 64,2

-25% -24%

36,7 36,5

LED indirect 48,5 47,7 27,3 27,2

LED recessed 44,2 43,5 24,9 24,8

Room 1 -

base case

T5 55,6 54,9

-21% -22%

34,1 34,0

LED indirect 41,4 40,9 25,4 25,3

LED recessed 37,7 37,3 23,2 23,1

Room 6

T5 59,6 56,7

-18% -24%

36,5 35,9

LED indirect 44,3 42,2 27,2 26,7

LED recessed 40,4 38,5 24,8 24,4

Room 6 –

base case

T5 52,8 50,9

-19% -21%

33,2 32,9

LED indirect 39,3 37,8 24,7 24,5

LED recessed 35,8 34,5 22,6 22,3

The dimming systems have a possibility to reduce the energy demand by 18-25% and the

occupancy sensors can further decrease the demand by 21-24%. The average total reduction

of energy expenditures is the highest for room 1 – oriel and is 43% of the initial energy, and

the lowest reduction is for room 6 – base case and is 36%.

Looking at the results it becomes clear that the initial settings used in the model were too

optimistic and too low. Figure 4-24 below presents the comparison of two annual fraction

schedules, first is the schedule for Room 1 with oriel design, calculated based on the actual

need and with application of occupancy sensors and dimming systems, the second is the

initial schedule of lighting. It can be clearly seen that the biggest difference occurs during

summer, when for the optimized system the light is almost always off apart from evening

hours 20-23.

40

Figure 4-24 Comparison of fractional schedules of lighting, first for the optimized lighting system with

occupancy sensors and dimmer, second for initial lighting system

To visualise the differences in the schedules, for each hour the fraction of the power was

averaged throughout the whole year i.e. for all 13:00 all fractions were added and divided

by 365. Figure 4-25 shows the difference between initial lighting schedule (light grey) and

the resulting average schedule created from the results of the analysis (dark grey).

Figure 4-25 Comparison of initial lighting use schedule and schedule calculated and averaged for room 1 and 6

Surprisingly the differences between schedules are not so big apart from the evening hours

20-22 when the initial schedule underestimated the needs of the inhabitants. Apart from the

schedule, the power intensity in case of proposed designs is at least 45% higher than the

initial settings, which results in significantly higher lighting energy demand. The energy for

the initial calculation settings was 15,5 kWh/(m2*year). Comparing the designs of the

lighting, the best choice is the LED recessed system, however based on the literature review

the recommended system would be the indirect LED solution. Due to the fact that the

0.0

0.2

0.4

0.6

0.8

1.0

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Fra

ctio

n o

f th

e to

tal

lighti

ng p

ow

er

Hour

average initial

41

differences in energy demand of two systems are less than 10%, the system with indirect

light was chosen as best performing.

4.2.5 Final energy demand and Daylight Factor

This chapter summarizes the energy demand for heating, domestic hot water and building’s

electricity. Domestic hot water and building’s electricity was estimated based on the FEBY

specification (FEBY, 2009). The calculation of hot water is presented below.

𝑉𝑣𝑣 = 18 [𝑚3

𝑝𝑒𝑟𝑠𝑜𝑛] (8)

𝐸𝑣𝑣 = 𝑉𝑣𝑣 ∙55

𝐴𝑡𝑒𝑚𝑝 [

𝑘𝑊ℎ

𝑚2 ∙ 𝑦𝑒𝑎𝑟] (9)

where

Vvv – annual hot water demand per person, m3

Evv – annual energy demand for domestic hot water, kWh/m2*year

Atemp – area with active temperature control, heated above 10°C

The whole area of the building is 3909 m2 and the amount of inhabitants is 54 with the

employees excluded. Based on the recommendations, it can be assumed that water demand

is 20% lower if using energy-efficient faucets. In the case study building that is supposed to

fulfil the energy requirement of Miljobyggnad certification, it is a safe assumption.

𝐸𝑣𝑣 = 0,8 ∙ 54 ∙ 18 ∙55

3909= 10,9 [

𝑘𝑊ℎ

𝑚2 ∙ 𝑦𝑒𝑎𝑟]

The building’s electricity demand was estimated to 10 kWh/(m2*year). The table below

summarizes the calculated energy demands for heating, hot water and building’s operation.

Table 4-7 Summary of energy demand for each room including the domestic hot water and building’s

electricity demand

Result

Heating energy

demand kW

h / m

2*y

ear

26,2

Domestic hot

water 10,9

Building’s

electricity 10

Total energy

demand 47,1

The Miljobyggnad silver certificate requires the total building’s energy demand to be below

75% of the BBR requirement of 80 kWh/m2*year, which gives 60 kWh/m2*year. The final

calculated energy demand is 47,1 kWh/m2*year. The highest peak load occurred in north-

east rooms on the third floor and was equal to 17,4 W/m2. The requirement is that the peak

load should be lower than 40W/m2, thus it is fulfilled.

The Daylight Factor was simulated for the same rooms on the third floor that were analysed

in previous points. Additionally the wall thickness was simulated in the model to ensure the

42

compliance with the requirements. According to the Table 2-1, the wall thickness was

estimated to be 40 cm. The table below summarizes the results for all rooms.

Table 4-8 Summary of the results of Daylight Factor analysis according to Miljobyggnad requirements

Room 1 Room 2 Room 3 Room 4 Room 5 Room 6

DF calculated, no

wall thickness / % 3,1 3,2 4,61 4,63 3,6 3,6

DF calculated, wall

thickness / % 2,2 2,3 3,5 3,5 2,6 2,6

DF required / % 1,2

It can be clearly seen that the rooms comply with the requirement of Miljobyggnad Silver

certificate even if the wall thickness is accounted for.

43

5 Discussion

In this point all the results are analysed and discussed. Starting with initial analysis of the

building and limitations, the discussion will follow the order of the results section.

5.1 Initial analysis

The initial analysis gave a general overview of the building properties and provided data that

helps explain the results. Adjacent buildings have an impact on all results even in case of the

third floor where the influence is kept to a minimum. Even though the building is almost

symmetrical towards the south, it has an impact on the results. Due to the fact that the

building is turned to east by 4,2°, the results for rooms with different orientations does not

match each other. East part of the sky is slightly darker than west and high luminance values

are less frequent according to data from Satel-Light.

The first two floors were excluded from the analysis due to shading by other buildings, which

would most probably affect the results for shading and visual control design. However it

hasn’t been proved in the thesis. It is possible that lower floors require less shading hours or

more transparent device could be used to achieve similar results. On the other hand context

shading would affect the general illuminance values in the room and therefore the electric

light design and results.

Depth of the wall was also excluded due to simplistic approach to the simulation process.

The difference in DA between wall thickness of 0 cm and 50 cm is 12% or 6 percent points.

The analysis was only performed for room 1 and it is hard to estimate the impact on daylight

for other rooms. It would also influence the energy performance and thermal comfort due to

reduced solar gains.

5.2 Daylight, thermal comfort and energy performance

Generally the rooms facing east direction (room 1, 2, 4) have lower daylight autonomy when

comparing to their symmetrical cases. The amount of overheating hours is lower. This can

be explained by the fact that the building is turned from south to east by 4,2° and that the

eastern part of the sky is darker than western part. DA is always better for the base case

design even though the glazing area is smallest for each considered glazing to wall ratio.

This can be caused by the fact that floor area of the oriel was not considered in the analysis

grid. In the base case design the window is closer to the grid and therefore the results are

higher. Due to the fact that the windows are smaller, the heating demand is smaller because

the losses are smaller. Also the overheating period is shorter because the solar gains are

smaller. The only drawback of the base case is the lack of area in the room, which would

provide enhanced view out for the inhabitants staying indoors.

Among all oriel designs, design number 5 was the best performing because it had the highest

horizontal view angle and relatively good distance from the eye of the inhabitant to the

ground. In comparison to the second best design, design 1, in case of rooms 1 and 2 the DA

was slightly lower but for rooms 5 and 6 it was higher. Design 5 has the smallest envelope

area, which implies the smallest glazing area for each GWR and yet the daylight results are

comparable to design 1. The overheating period in case of design 5 is higher for room 1 and

44

2 and lower for room 5 and 6, in comparison to design 1. This may be caused by the

orientation of the windows but also by the fact that all solar gains were considered to fall on

the floor instead of escaping through the other window in some cases.

The shading was simulated as not movable however the schedule assumed that the device is

either on or off for specific hours. For the initial optimization of shading, it was simulated

as always on and that is why daylight results were significantly smaller and overheating

period was much shorter than for the final results. Based on the analysis of direct/diffuse

ratio of solar gains, keeping the shading for longer period would imply significantly better

results regarding overheating but obviously it would affect the daylight in a negative way.

The best performing device was a window screen. It has the lowest amount of overheating

hours for each room, but it performs worse than the awning regarding the daylight. Reinhart

(2014) concluded that office area with DA300lx of 50% will be considered as daylit. Changing

the threshold to 500 lx makes the task much more problematic and therefore the results of

DA500lx of 45-50% can still be acceptable due to the general increased illuminance. The

advantage of this system is the transparency of the material, lack of any obstructions and

lack of additional architectural parts of the façade. In comparison to the base case with

shading, as expected base cases have smaller overheating issues and provide better daylight

but it may be caused by the fact that base case design performed better without shading than

any oriel design with the same GWR.

Natural ventilation proved to have a significant role in cooling down the building. Although

the annual influence of natural ventilation was not checked, it can be clearly observed that

using windows will allow to cool down the building to the required temperatures.

Additionally cross ventilation can be achieved by implementing operable windows above

the door to each apartment and keeping them open during shading period. Further

simulations are required to confirm the prediction.

Visual comfort was evaluated based on annual glare probability. Using the external shading

as a visual control device significantly cut down the amount of overlit hours but also

influenced the heating in a negative way. Following the findings of Atzeri et al. (2014) using

the internal shading device does not mean that the solar heat gain will be preserved as if

there was no shading and it is generally not recommended. The visual discomfort analysis

was carried out for a very specific case when the inhabitant cannot move from the bed. In

these cases people could be moved to east facing apartments on lower floors were expected

visual issues are already smaller. This would decrease the need to use the shading as visual

control device and therefore decrease the heating by allowing more solar gains. For the rest

of the inhabitants, changing their position is the optimal solution. As mentioned in chapter

3.3.4, in case of offices or working spaces the position is fixed but in apartments and houses

the position can be adjusted to fit the requirements and the glare is not a large (scale)

problem.

Electric light analysis was entirely based on previous results. Although the lighting was

required to be delivered by LED systems, the T5 fluorescent lamps were used for comparison

reasons. The initial settings of lighting intensity and schedule proved to be too optimistic

and even in the best case the intensity had to be 63% higher than the initial. The chosen

system is slightly worse than the optimal, but this type of luminaires was recommended by

45

IESNA (2009) and it eliminates any probable electric light glare issues due to the fully

indirect light. Increasing the ceiling reflectance will prove beneficial but it may not be

enough to decrease the amount of lamps so the lighting intensity would remain unchanged.

The optimisation process proved that there is significantly lower demand for light in the

summer until late hours and this can positively affect the overheating period.

The final simulation of building was performed only to check the heating demand for the

whole building because the calculation for rooms was not representative for the bigger scale.

Even though the model was divided into separate zones with different schedules and gains,

there are some uncertainties left e.g. differences in overall infiltration, the influence of the

lighting density and schedules in the zones other than rooms ,which can greatly influence

the result and which explain the disparity between room’s and building’s heating demand.

The building fulfils the simulated energy requirements of the Miljobyggnad Silver

certificate. There is still some room for all the factors to increase. For example if the shading

was used as the analysis in chapter 4.2.3 suggested, the heating demand of the rooms would

probably rise by 50% (as in Room 6). Even if this happened, there is still 13 kWh/(m2*year)

remaining energy demand before the building exceeds the requirements.

The Daylight Factor analysis did not include the whole building due to the size of the

analysis. The analysis for rooms confirms that the building will most probably pass the

requirement but it needs an analysis in more problematic areas such as kitchen zone.

Miljobyggnad instructs to crop the analysis area by 1 meter from the darkest wall in the

room. In the study, the small corridor was accounted as a part of the room, but if it was

excluded as in all other points the results would be significantly higher. The issue of analysis

area is a matter of discussion and should be checked by a professional Miljobyggnad

Assessor.

46

6 Conclusion

The thesis analysed energy performance, thermal comfort, daylight design and view out in

an energy-efficient case study building for elderly people. Research analysed in the

introduction suggested that elderly people are less sensitive to heating in winter and cooling

in summer i.e. they need higher temperatures in winter and lower in summer. They also need

higher illuminance threshold, both provided by daylight and by electric lights. In order to

fulfil the requirements, there was an optimization of envelope design and glazing areas.

Based on the results of the analysis the following conclusions were drawn.

Base case design i.e. no oriel design and GWR: 20-60% always performed better than oriel

design with respective GWR, but the only issue was the lack of additional view out that oriel

designs were offering. The possible drawbacks of oriel designs such as higher amount of

overheating hours can be easily overcome with the use of shading devices, and slightly lower

daylight levels are still considered acceptable and will be rewarded by enhanced view out.

In order to achieve the Daylight Autonomy500lux threshold of 50% in constantly occupied

apartments, the GWR should be around 35-40%.

Shading solves most of the thermal comfort issues of high indoor temperatures in energy-

efficient buildings and applying simple way of natural ventilation - by opening the windows

- will bring the temperature to acceptable levels. Although blocking direct solar gains brings

the desired effect of lowering the indoor temperatures during the summer, blocking part of

diffuse solar gains yields additional potential. This can be achieved by keeping the shading

after/before the time, when the direct gains occur.

Using external shading device as a visual control solution may prove as a good choice

regarding the visual issues but it is not recommended due to the increased heating energy

demand. Analysis of glare probability is important in very specific cases, when the position

of the inhabitant is fixed for some reason. Other than that, annual DGP analysis is not

required for apartments.

The recommended values of lighting power intensity of 8W/m2 are significantly too low for

the case, when inhabitants require more light. Applying the energy saving systems i.e.

dimming system and occupancy sensors will reduce the lighting energy demand by 30-40%

compared with the case without the systems. Optimization of the lighting system may

positively affect both energy and thermal performance of the building.

In summary, achieving the energy-efficient standards such as Miljobyggnad certificate in

the building designed for elderly people is a complex issue and requires a broad analysis of

several different aspects influencing the architecture and general design of the building and

interiors.

47

7 Further studies

The analysis included only six rooms located on different facades and did not account for

some of the aspects such as buoyancy or heating up of the building during the whole day or

did not allow for thorough analysis of cross-ventilation. The next suggested step would be

to create the full model of the building with recommended settings and to check the

performance of the whole building.

48

8 Summary

For the past century the population of people was rapidly growing from 2,5 billion in 1950s

to more than 7 billion in 2015. However in most of well-developed countries, such as

Sweden, the societies are getting older. Together with the need to decrease energy

consumption and expenditures, it all creates the demand for housing for elderly adults.

General research claims that elderly people need warmer indoor conditions, shorter

overheating periods and higher levels of light. The needs of elderly people contradict with

the aim of energy reduction i.e. keeping higher indoor temperature in the winter is not

energy-efficient, higher illuminance requirements imply higher lighting power. What is

more, higher lighting requirements demand more glazed areas, which implies higher solar

gains, longer overheating periods and more visual issues.

This study optimises the façade shape and properties in energy-efficient apartments for

elderly people. The work was conducted with a holistic approach and took factors such as

daylight and visual comfort, heating energy demand, thermal comfort and electric light

design into consideration. The method used in the analysis was divided into parts that were

closely related to each other. The results of the one part were further analysed and used in

the next part. The study contains:

initial analysis of the building and climate conducted in order to understand the

surroundings and to help draw the conclusions

façade shape analysis with daylight, heating energy demand and indoor thermal

comfort analysis carried out to choose best performing façade design

shading design analysis carried out to reduce the overheating issues

visual comfort simulation conducted to ensure reasonable visual conditions in the

rooms

electric light design analysis conducted to propose an energy-efficient system of lights

final building energy demand simulation to check energy certificate compliance.

First, it was needed to optimize the shape of the facade and the areas of windows. Bigger

windows meant improved daylight conditions but also increased thermal comfort issues.

Together with glazing size optimization, there was a need to design solar and visual control

devices, which would maintain the enhanced and not obstructed view out from the

apartments. Based on the solar and visual control analysis, the electric light designs were

proposed and comparison with initial settings was carried out.

The results showed that expanding the façade only serves a better view out, while the rest of

the factors are significantly worse than in case of usual flat facade. The analysis also proved

that due to the increased requirements of light, the building will suffer overheating but this

can be fixed with shading and natural ventilation. It also proved that the electric light

assumptions that are commonly used were too low. Finally it showed that even with

increased indoor temperature during heating season, the building fulfils the requirements of

an energy-efficiency certificate.

The analysis gives the architects an insight into what happens in the building if some of the

ideas are applied. The whole process of designing should be carried out as a cooperation of

engineers and architects in order to achieve the best results.

49

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51

A. Appendix

The section below presents all remaining results and data that was not necessary to present

in the thesis, but was conducted and is crucial for proving the completeness of results.

Daylight autonomy results for all oriel designs and base cases are presented in the table

below.

GWR / % 20 25

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 3,1 3,3 3,5 3,8 3,0 2,7 3,9 4,1 4,4 4,7 3,7 3,3

R1 25,6 22,6 19,0 17,7 27,1 31,3 33,2 30,2 25,9 24,1 35,6 40,5

R2 24,4 21,0 17,4 16,3 26,1 30,4 32,5 29,5 24,1 22,9 34,9 40,4

R3 44,3 44,3 52,6 52,6

R4 39,5 39,5 48,2 48,2

R5 33,0 32,9 29,3 27,8 38,0 42,4 41,4 42,1 37,7 35,6 46,6 51,5

R6 34,4 34,3 30,3 29,2 38,9 43,4 42,5 42,9 38,3 36,8 47,6 52,1

GWR / % 30 35

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 4,7 4,9 5,3 5,6 4,5 4,0 5,5 5,8 6,2 6,6 5,2 4,7

40

42

44

46

48

50

52

0.0 0.1 0.2 0.3 0.4 0.5

Day

light

Auto

no

my

500lu

x/

%

Sill depth / m

52

R1 40,0 37,5 32,1 29,9 42,6 48,0 45,6 43,6 37,2 35,3 48,3 53,9

R2 39,7 37,5 31,1 29,1 42,2 47,8 45,8 43,4 36,9 34,6 48,4 54,3

R3 58,8 58,8 63,4 63,4

R4 54,7 54,7 59,8 59,8

R5 48,4 49,4 44,5 42,1 53,1 58,1 53,5 54,7 49,8 47,6 58,2 63,1

R6 49,3 50,1 44,7 43,3 54,1 58,7 54,4 55,7 50,2 48,4 59,1 63,6

GWR / % 40 45

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 6,3 6,6 7,0 7,5 6,0 5,3 7,0 7,4 7,9 8,4 6,7 6,0

R1 53,2 48,0 47,6 46,2 52,9 58,7 58,8 54,9 54,9 54,1 58,5 62,4

R2 53,5 48,2 47,7 46,4 53,1 58,9 59,0 55,2 55,1 54,2 58,8 62,7

R3 67,0 67,0 69,8 69,8

R4 63,8 63,8 67,3 67,3

R5 60,5 58,9 58,6 57,2 62,1 66,9 65,2 64,6 64,6 63,7 66,9 69,8

R6 61,1 59,5 59,0 58,0 62,9 67,4 65,8 65,3 64,9 64,4 67,5 70,3

GWR / % 50 55

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 7,8 8,2 8,8 9,4 7,5 6,7 8,6 9,1 9,7 10,3 8,2 7,3

R1 62,9 60,0 60,1 59,7 62,4 65,4 65,9 63,9 64,0 63,8 65,6 67,6

R2 62,9 60,3 60,4 60,1 62,9 65,6 66,3 64,3 64,5 64,2 65,8 68,0

R3 72,0 72,0 73,8 73,8

R4 69,9 69,9 71,9 71,9

R5 68,8 68,5 68,6 68,2 70,2 72,3 71,3 71,4 71,6 71,2 72,6 74,0

R6 69,2 69,0 69,0 68,7 70,7 72,6 71,7 71,8 71,9 71,8 73,0 74,3

GWR / % 60

design D1 D2 D3 D4 D5 BC

glazing area

/ m2 9,4 9,9 10,6 11,3 9,0 8,0

R1 68,3 66,7 67,1 66,7 67,9 69,5

R2 68,7 67,2 67,5 67,2 68,2 69,9

R3 75,1 75,1

R4 73,4 73,4

R5 73,3 73,6 73,8 73,6 74,3 75,4

R6 73,7 73,9 74,0 73,9 74,7 75,7

53

Heating energy demand for all oriel designs and base cases are presented in the table below.

GWR / % 20 25

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 3,1 3,3 3,5 3,8 3,0 2,7 3,9 4,1 4,4 4,7 3,7 3,3

R1 7,8 7,4 7,9 8,6 7,0 6,5 8,6 8,1 8,7 9,4 7,5 7,3

R2 8,0 7,4 7,9 8,2 7,1 7,1 8,9 8,0 8,6 8,9 7,7 7,9

R3 5,3 5,3 5,9 5,9

R4 6,0 6,0 6,5 6,5

R5 7,1 7,3 7,8 7,6 6,7 6,3 7,8 8,0 8,6 8,3 7,3 6,9

R6 6,7 7,0 7,1 8,3 6,4 6,0 7,3 7,6 7,6 8,9 6,9 6,5

GWR / % 30 35

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 4,7 4,9 5,3 5,6 4,5 4,0 5,5 5,8 6,2 6,6 5,2 4,7

R1 9,4 8,8 9,4 10,2 8,2 8,0 10,3 9,5 10,1 11,0 8,8 8,9

R2 9,7 8,7 9,2 9,6 8,4 8,8 10,6 9,4 9,9 10,3 9,0 9,6

R3 6,4 6,4 7,0 7,0

R4 7,1 7,1 7,7 7,7

R5 8,5 8,7 9,4 8,9 7,9 7,6 9,2 9,5 10,2 9,6 8,6 8,2

R6 8,0 8,3 8,2 9,5 7,5 7,1 8,6 9,0 8,8 10,1 8,1 7,7

GWR / % 40 45

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 6,3 6,6 7,0 7,5 6,0 5,3 7,0 7,4 7,9 8,4 6,7 6,0

R1 11,3 10,2 11,2 12,2 9,4 9,9 12,3 11,1 12,2 13,2 10,2 10,5

R2 11,6 10,0 11,1 11,5 9,7 10,5 12,7 11,0 12,2 12,7 10,6 11,1

R3 7,9 7,9 8,4 8,4

R4 8,3 8,3 8,9 8,9

R5 10,1 10,3 11,2 10,6 9,3 9,0 11,1 11,2 12,2 11,6 10,0 9,6

R6 9,4 9,7 9,8 11,1 8,7 8,3 10,2 10,5 10,6 11,9 9,4 8,9

GWR / % 50 55

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 7,8 8,2 8,8 9,4 7,5 6,7 8,6 9,1 9,7 10,3 8,2 7,3

R1 13,3 12,1 13,3 14,3 11,1 11,3 14,4 13,1 14,3 15,4 11,9 12,1

R2 13,7 12,0 13,2 13,8 11,6 12,0 14,8 13,2 14,3 15,0 12,4 12,7

R3 9,0 9,0 9,6 9,6

54

R4 9,6 9,6 10,2 10,2

R5 11,9 12,1 13,3 12,6 10,8 10,3 12,9 13,1 14,4 13,6 11,6 11,0

R6 11,1 11,4 11,5 12,8 10,1 9,6 11,9 12,3 12,3 13,6 10,9 10,2

GWR / % 60

design D1 D2 D3 D4 D5 BC

glazing area

/ m2 9,4 9,9 10,6 11,3 9,0 8,0

R1 15,4 14,0 15,3 16,5 12,8 13,0

R2 15,9 14,2 15,3 16,1 13,2 13,7

R3 10,2 10,2

R4 10,9 10,9

R5 13,8 14,0 15,4 14,5 12,4 11,7

R6 12,8 13,1 13,1 14,4 11,5 10,8

Thermal comfort results for all oriel designs and base cases are presented in the table below.

GWR / % 20 25

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 3,1 3,3 3,5 3,8 3,0 2,7 3,9 4,1 4,4 4,7 3,7 3,3

R1 2257 2441 2525 2428 2400 2369 2689 2904 2987 2903 2851 2781

R2 2146 2414 2537 2577 2282 2177 2582 2840 2945 2988 2728 2508

R3 2722 2722 3105 3105

R4 2802 2802 3127 3127

R5 2565 2597 2653 2897 2560 2659 2994 2998 3037 3222 2973 3009

R6 2729 2679 2856 2805 2690 2817 3113 3069 3211 3133 3078 3137

GWR / % 30 35

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 4,7 4,9 5,3 5,6 4,5 4,0 5,5 5,8 6,2 6,6 5,2 4,7

R1 2992 3169 3256 3166 3117 3023 3191 3387 3477 3383 3337 3199

R2 2830 3087 3165 3200 2989 2756 3032 3262 3380 3439 3158 2938

R3 3374 3374 3530 3530

R4 3388 3388 3621 3621

R5 3211 3209 3252 3494 3185 3232 3426 3421 3452 3677 3394 3419

R6 3371 3311 3465 3414 3322 3386 3554 3499 3662 3602 3502 3547

GWR / % 40 45

55

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 6,3 6,6 7,0 7,5 6,0 5,3 7,0 7,4 7,9 8,4 6,7 6,0

R1 3348 3599 3648 3573 3554 3363 3468 3747 3768 3728 3695 3495

R2 3133 3460 3489 3553 3334 3092 3224 3561 3573 3660 3438 3213

R3 3663 3663 3785 3785

R4 3793 3793 3907 3907

R5 3546 3543 3556 3802 3536 3556 3647 3663 3666 3900 3651 3678

R6 3690 3639 3780 3739 3658 3693 3809 3778 3894 3859 3788 3829

GWR / % 50 55

design D1 D2 D3 D4 D5 BC D1 D2 D3 D4 D5 BC

glazing area

/ m2 7,8 8,2 8,8 9,4 7,5 6,7 8,6 9,1 9,7 10,3 8,2 7,3

R1 3583 3847 3873 3842 3800 3605 3665 3919 3943 3932 3884 3697

R2 3307 3659 3688 3745 3488 3285 3357 3690 3768 3826 3605 3359

R3 3922 3922 4035 4035

R4 3996 3996 4064 4064

R5 3755 3770 3761 4000 3762 3784 3841 3844 3845 4068 3854 3877

R6 3915 3889 4022 3989 3900 3944 3997 3973 4108 4079 3996 4025

GWR / % 60

design D1 D2 D3 D4 D5 BC

glazing area

/ m2 9,4 9,9 10,6 11,3 9,0 8,0

R1 3733 3986 4003 3997 3959 3785

R2 3397 3756 3841 3887 3689 3415

R3 4126 4126

R4 4126 4126

R5 3901 3916 3908 4150 3927 3948

R6 4061 4036 4180 4162 4063 4092

The results of initial optimization for horizontal blinds are presented below.

angle / ° 0

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 10,0 9,7 9,6 10,0 9,7 9,6 9,7 9,6 9,6

overheating hours /

h 1545 2470 2961 1746 2592 3025 2608 2877 3030

56

daylight autonomy

/ % 26,2 39,6 44,1 28,0 40,3 44,8 42,2 43,9 45,7

angle / ° 15

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 10,5 10,0 9,7 10,4 9,9 9,7 9,9 9,8 9,7

overheating hours /

h 872 1804 2418 950 1896 2467 1906 2248 2478

daylight autonomy

/ % 21,0 33,8 40,7 22,5 35,2 41,2 36,9 39,5 42,1

angle / ° 30

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 11,1 10,3 9,9 11,0 10,3 9,9 10,3 10,1 9,9

overheating hours /

h 549 1120 1902 577 1174 1940 1177 1586 1958

daylight autonomy

/ % 19,1 27,3 35,8 20,3 28,7 36,2 30,7 33,9 37,6

angle / ° 45

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 11,6 10,8 10,2 11,5 10,8 10,2 10,8 10,4 10,2

overheating hours /

h 415 709 1323 431 735 1356 737 990 1357

daylight autonomy

/ % 16,7 21,8 30,4 17,7 23,2 30,3 24,8 27,8 32,0

The results of initial optimization for vertical blinds are presented below.

angle / ° 0

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 9,9 9,7 9,6 9,9 9,6 9,6 9,6 9,6 9,5

overheating hours /

h 1991 2690 3050 2200 2845 3122 2840 3027 3152

57

daylight autonomy

/ % 26,2 40,6 44,1 35,8 42,7 45,1 42,2 45,4 46,5

angle / ° 15

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 10,0 9,7 9,6 9,9 9,7 9,6 9,7 9,6 9,5

overheating hours /

h 1873 2603 3007 2087 2734 3088 2727 2976 3120

daylight autonomy

/ % 30,9 38,6 42,7 33,8 40,9 43,6 40,9 43,6 45,1

angle / ° 30

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 10,1 9,8 9,6 10,0 9,7 9,6 9,7 9,6 9,6

overheating hours /

h 1541 2375 2838 1756 2496 2943 2485 2752 3014

daylight autonomy

/ % 26,3 34,2 39,2 28,9 37,0 40,0 36,7 40,1 42,0

angle / ° 45

distance / m 0,05 0,25 0,5

depth / m 0,03 0,05 0,07 0,15 0,25 0,35 0,5 0,6 0,7

heating energy

demand /

(kWh/(m2*year)) 10,3 9,9 9,7 10,2 9,8 9,7 9,9 9,7 9,7

overheating hours /

h 1013 1998 2522 1151 2107 2599 2089 2387 2697

daylight autonomy

/ % 20,2 28,0 34,1 22,6 30,9 35,0 30,3 35,0 37,5

The results of initial optimization for awning are presented below.

angle / ° 0 15 30 45 60 75

heating energy

demand /

(kWh/(m2*year)) 9,7 9,9 10,1 10,4 10,6 10,8

overheating hours /

h 2646 2024 1430 1024 826 690

daylight autonomy

/ % 44,1 39,6 35,1 30,9 26,3 22,1

58

The results of initial optimization for shade are presented below.

visual

transmittance 0,4 0,3 0,2 0,1 0

heating energy

demand /

(kWh/(m2*year)) 9,8 10,0 10,4 11,3 13,0

overheating hours

/ h 2302 1715 995 474 211

daylight

autonomy / % 29,7 24,9 20,4 16,7 14,0

The remaining hourly solar gains and sun angles analysis is presented in the figures below

together with respective adjusted horizontal and vertical blinds.

Hourly solar gains and sun angles, and adjusted shading devices in the room 2.

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Hourly solar gains and sun angles, and adjusted shading devices in the room 3.

Hourly solar gains and sun angles, and adjusted shading devices in the room 4.

60

Hourly solar gains and sun angles, and adjusted shading devices in the room 5.

61

Hourly solar gains and sun angles, and adjusted shading devices in the room 6.

Dept of Architecture and Built Environment: Division of Energy and Building Design Dept of Building and Environmental Technology: Divisions of Building Physics and Building Services