Division of Architecture and Infrastructure Energy …991529/...Division of Architecture and Infrastructure Energy Simulation and Life Cycle Costs Estimation of a Building’s Performance

LICENTIATE T H E S I S

Department of Civil, Mining and Environmental EngineeringDivision of Architecture and Infrastructure

Energy Simulation and Life Cycle Costs

Estimation of a Building’s Performance in the Early Design Phase

Jutta Schade

ISSN: 1402-1757 ISBN 978-91-86233-53-2

Luleå University of Technology 2009

Jutta Schade Energy Sim

ulation and Life Cycle C

osts Estimation of a Building’s Perform

ance in the Early Design Phase

ISSN: 1402-1544 ISBN 978-91-86233-XX-X Se i listan och fyll i siffror där kryssen är

Luleå University of Technology

Division of Architecture and Infrastructure Department of Civil Mining and Environmental Engineering

Luleå University of Technology SE - 971 87 LULEÅ

www.ltu.se/shb cee.project.ltu.se

LICENTIATE THESIS

ENERGY SIMULATION AND LIFE CYCLE COSTS

ESTIMATION OF A BUILDING’S PERFORMANCE IN THE EARLY DESIGN PHASE

Jutta Schade

Luleå 2009

Tryck: Universitetstryckeriet, Luleå

ISSN: 1402-1757 ISBN 978-91-86233-53-2

Luleå 2009

www.ltu.se

If you know exactly what you are going to do,

what the point of doing it? (Pablo Picasso)

Abstract

I

Abstract

Sustainable development and the protection of the environment are key issues in our society today. The building stock in Europe accounts for over 40% of the final energy consumption, CO2 emissions and generation of waste. A large part of the life cycle performance is determined early. Investigations show that when 1% of the project costs are spent, roughly 70% of the lifecycle cost of the building has been committed indicating that decisions taken early greatly affect the life cycle performance. The building’s shape, selected materials, structural system, internal room distribution, and building services systems are some of the most important factors that influence the environmental and energy performance of a building throughout its lifecycle.

The objective of the conducted research was to investigate what kind of energy analyses are possible to carry out in the early design phase to give the decision makers a more holistic view of the energy performance of a building over its life cycle. Three research questions have guided the research work; (1) What types of energy simulations are possible to make in the early design phase? (2) How reliable are early energy estimations compared to results when detailed models are available? (3) How does energy consumption affect the life cycle cost of a building?

The research work is based on literature reviews, a theoretical framework for model based design of life cycle aspects in general and energy performance in particular developed in the European Union sixth framework project InPro. The study includes a number of energy performance calculations at different levels of information maturity in the early design phase. Different energy simulation programs were used for this purpose. The study was performed for an existing building where design parameters like window area, building

Energy simulation and life cycle costs

II

envelope and indoor climate been changed. In total, 28 cases were analysed using four different energy analysis tools at three levels of information maturity. The resulting life cycle costs were also estimated for the different cases with varying rates of interest and forecasts of the energy price.

The result of this study shows that energy calculations usable for design decision can be made at different levels of information maturity. Depending on the maturity level more or less detailed design information is available which influences the estimated energy consumption. Therefore early estimations when the information maturity is low should only be used to compare different design alternatives at the same design stage. However, the result shows that these early estimations can give a clear tendency guiding the design in a more energy efficient direction. When the information maturity is higher and indoor climate simulations are possible to make at room level, the result gets more accurate. However, the use of more sophisticated energy simulations tools is time consuming and error prone since the amount of input data needed is much higher. This calls for better integration between the design and energy analysis especially when more advanced energy simulations are performed.

The resulting life cycle costs of the different cases are strongly affected by the estimated energy consumption, the selected real rate of interest, the forecast of energy prices as well as the discount time.

The conclusion of this study is that energy calculations are usable for the decision making of design alternatives in the early design phase. Also, life cycle cost estimates can support the decision makers in the analysis of different financial scenarios.

Key words: Building design, Energy analysis, Life cycle cost analysis, LCC, early design phase

Contents

III

Contents

ABSTRACT.........................................................................................................I

CONTENTS...................................................................................................... III

PREFACE ....................................................................................................... VII

ABBREVIATIONS ..........................................................................................IX

1 INTRODUCTION .....................................................................................1 1.1 Background and motivation .............................................................1 1.2 Aim and objective ............................................................................4 1.3 Limitations .......................................................................................5 1.4 Thesis outline ...................................................................................6

2 METHODOLOGY ....................................................................................7 2.1 Research design................................................................................7 2.2 Research work..................................................................................9

2.2.1 Phase I ..................................................................................9 2.2.2 Phase II ...............................................................................11 2.2.3 Phase III..............................................................................12

2.3 Validity, reliability and generalization...........................................12

3 THEORETICAL FRAMEWORK...........................................................15 3.1 Sustainable design aspects .............................................................15 3.2 Life Cycle Cost - LCC ...................................................................17

3.2.1 Definition of LCC ..............................................................17 3.2.2 Calculation methods ...........................................................19 3.2.3 Data required for life cycle cost calculation.......................21 3.2.4 Problems and difficulties using LCC for buildings today..22


IV

3.3 CO2 emission ................................................................................. 23 3.4 Energy consumption in the building sector ................................... 23 3.5 Energy performance of buildings in the EU .................................. 26

3.5.1 The EU Directive 2002/91/EC - EPBD ............................. 26 3.5.2 Implementation of the EPBD in Sweden and Germany..... 26 3.5.3 Low energy houses............................................................. 29 3.5.4 Passive houses.................................................................... 31

3.6 Energy calculations ........................................................................ 33 3.6.1 Steady state calculations .................................................... 33 3.6.2 Dynamic calculation........................................................... 35 3.6.3 Parameters which influence the energy consumption........ 35

3.7 Conclusion ..................................................................................... 36

4 THE DESIGN PROCESS ....................................................................... 39 4.1 Introduction.................................................................................... 39

4.1.1 Concurrent engineering...................................................... 40 4.1.2 Building Information Models and Virtual Design and

Construction ....................................................................... 41 4.1.3 Model based design............................................................ 42 4.1.4 Early design........................................................................ 46

5 EXPERIMENTAL STUDY OF SENATE PROPERTIES HEADQUARTERS................................................................................. 47 5.1 Energy analysis at different levels of maturity .............................. 47 5.2 The Senate Properties Headquarters .............................................. 49 5.3 Design of the Senate Properties Headquarters simulation study ... 51 5.4 Energy analysis software ............................................................... 54 5.5 LCC analysis.................................................................................. 60

6 RESULTS AND ANALYSIS ................................................................. 65 6.1 Energy performance analysis ......................................................... 65

6.1.1 Energy analysis at conceptual maturity level..................... 66 6.1.2 Energy analysis at functional maturity level...................... 69 6.1.3 Energy analysis at system maturity level ........................... 71 6.1.4 Summary of the energy calculations .................................. 73

6.2 LCC calculation at the system maturity level ................................ 75 6.2.1 Summary of LCC ............................................................... 79

7 DISCUSSION AND CONCLUSION..................................................... 81 7.1 Addressing the research questions ................................................. 81 7.2 Contribution of the research........................................................... 84 7.3 Suggestion for further research...................................................... 84

Contents

V

8 REFERENCES ........................................................................................87

APPENDIX A. INPUT DATA .........................................................................93

Preface

VII

Preface

This Licentiate Thesis reflects a part of my research journey as a postgraduate student. This study has been made possible thanks to many persons in my surroundings. In this preface I would like to take the opportunity to acknowledge the help and assistance of these people and organisations.

First of all I would like to express my gratitude to my scientific advisors Professor Thomas Olofsson and Assistant Professor Ulf Ohlsson. Thomas, thank you for your patience and confidence in me during this research journey. Thank you for all your support and enthusiastic ideas. It is very inspiring working with you. Ulf, thank you for your profound interest and for always taking your time and helping me when I got lost in the jungle of my calculations.

I would like to acknowledge the finical support I received from the European Project InPro, an integrated project co-funded by the European Commission within the Sixth Framework Programme. I also want to thank all my colleagues in the European Project InPro. It is great working with you.

This research work would be never been achieved without the support of my dear colleagues in our growing research group. Thank you, Anders Vennström, Kajsa Simu, Katja Osipova, Håkan Norberg, Kristina Laurell-Stenlund, Per Erik Eriksson, Tamas Racz, Rom Rwamamara, Robert Lundkvist, Annelie Karlsson, Jan Jonsson, Ove Lagerqvist, Lu Weizhuo, Andres Rönneblad, Stefan Sandesten and Thomas Olofsson for this friendly and supportive atmosphere in our group. Eva, Monica and Carina, thank you for all the help with the sometimes confusing administrative stuff.


VIII

Special thanks are due to Assistant Professor Ylva Sarden for many valuable comments at my pre-seminar “paj-seminariet”. They really helped to improve my work.

Further, I would like to thank all my colleagues and friends at the Department of Civil, Mining and Environmental Engineering for their support in and outside the University.

Great thanks to my dear colleagues at the Department of Construction Management of the Technical University Dortmund. Thank you for making my stay in Dortmund such a good time.

Last but not least I would like to thank my family in Germany and all my friends in Germany, Sweden, Switzerland, Austria and all over the world, who have given me support throughout these years. It is great that you all exist.

Thanks to all

Luleå, April 2009

Jutta Schade

Abbreviations

IX

Abbreviations

3D The three dimensions in spaces (x, y, and z)

ACH Air changes per hour

AEC Architectural, Engineering and Construction

BIM Building Information Model/Modelling

CAD Computer Aided Design

CE Concurrent Engineering

DPP Discount payback method

ECA Equivalent annual cost

HVAC Heating, Ventilating and Air Conditioning

ICE Integrated Concurrent Engineering

IFC Industry Foundation Classes

IRR Internal rate of return (IRR)

kWh/m2 Kilowatt hours per square meter

kWh/(m2 a) Kilowatt hours per square meter and year

LCA Life cycle assessment


X

LCC Life cycle cost

LH Low energy building envelope

l/(sm2) Litre per second and square meter

NPV Net present value

NS Net saving

OIE Open information environment

OIP Open information platform

PH passive house building envelope

SH Standard building envelope

U-value Heat transfer coefficient in W/m2 K

VDC Virtual Design and Construction

VR Virtual reality

WLA Whole life appraisal

WLC Whole life cost

W/m2K Watt per square meter and Kelvin

Introduction

1

1 INTRODUCTION

In this Chapter the background to and motivation for the thesis are presented followed by the aim and research questions. Finally the structure of the thesis is outlined.

1.1 Background and motivation

Sustainable development and the protection of the environment are key issues in our society today. The international concern about CO2 emission led to the Rio de Janeiro Convention of 1992 and to the Kyoto protocol 1997. The European Union declared itself willing to reduce its emission by 8% based on 1990 levels by 2008-2012.

The building stock in Europe accounts for over 40% of the final energy consumption in the European Union of which dwellings represent 67% of the total energy consumption (ENERDATA, 2003). It is certainly clear that the building sector can improve a lot in order to reduce the greenhouse gas emission to embark the climate changes.

Buildings represent a large and long term investment in economic as well as in other terms (Sarja, 2002). Most commonly, the production cost is the main cost factor to be minimized in the design which does not improve the life cycle performance of the building (Öberg, 2005).

The commercial relationships among the many actors involved have created a sector characterized by fragmentation and non-integration of the value chain (WBCSD, 2008). The different stakeholders involved have there own interest in the project. The investors are foremost interested in the profits made by their


2

investment. The building owner, or the main user of the building, is concerned with the everyday performance of the building (InPro-D14a, 2009), Figure 1-1.

Figure 1-1. The many actors involved in a construction project, from WBCSD(2008)

Also, the construction process is mainly sequential (Harris and McCaffer, 2001). The different phases of a construction project are often separated in time and space where each phase has a predetermined purpose and at the end a decision point at which the progress can be reviewed and further actions identified (Smith et al., 2006, Harris and McCaffer, 2001). The isolation, ineffective coordination and poor communication among the different stakeholders in construction projects have as a consequence created operational islands (Mattar, 1983), where:

- Investments and operating cost are usually split among different actors, i.e. those who invest do not benefit from low operational cost.

- End-users or facility managers responsible for the operational phase of a building’s life-cycle have normally very little opportunity to provide feedback to developers and designers in the design phase.

- The long term performance of a building is often underestimated, as the focus is on the initial cost (Flanagan and Jewell, 2005).

These operational islands make it difficult to take decisions that optimize the life cycle performance of a building, Figure 1-2.

Introduction

3

Figure 1-2. Operational islands, after Mattar (1983), from WBCSD (2008).

To improve the life cycle performance the sustainable aspects must be considered already at the early stage of the design. On average, by the time 1% of project costs are spent, roughly 70% of the lifecycle cost of the building has been committed (Romm, 1994); Figure 1-3.

Figure 1-3. The benefits of early integration, from WBCSD (2008)

By optimising the building shape, isolation and orientation, the space heat consumption can be reduced up to 80% (Feist et al., 2005), i.e. higher


4

investment costs can decrease the total life cycle cost (LCC) for a building. As stated by Kotaji et al. (2003) it is particularly important to show the relation between the design choices and the resulting life time cost (energy, maintenance and operation). An office building will cost about three times its initial cost to operate and maintain over a 25-year period (Flanagan and Jewell, 2005).

According to Clarke (2001) energy simulations for buildings will lead to outcomes that better match society’s aspirations for sustainable practices, environmental protection and improvement of climate changes improvement. Better designed buildings can reduce the energy consumption by 50-75% compared to a building level standard from 2000 (Clarke, 2001).

The energy performance of a building is usually considered at the later stage of the design process as the existing tools for energy simulation need fairly detailed design information. As Figure 1-3 suggests the effectiveness of a decision made early has a great impact on the cost of the building over its life cycle. The decisions do not only influence the cost but also the performance, such as the energy consumption of the building.

It is therefore important to increase the understanding of how energy efficient design can be accomplished in the early design of the building.

1.2 Aim and objective

The aim of this research is to investigate how an energy efficient design can be accomplished in the early design stage of buildings. What type of energy simulations can be made and how can the result from these simulations contribute to better energy efficient design and lower life cycle cost as a result?

Three research questions have been formulated to guide the research work.

Research question I

What types of energy simulations are possible to make in the early design phase?

This question also includes the question of what input data is needed to make early energy estimation/calculation, i.e. at what stages in the early design phase it is possible to make estimations of the energy performance.

Introduction

5

Research question II

How reliable are early energy estimations compared to results when detailed models are available?

This research question attempts to identify the reliability of the results of early energy estimation. How early estimations differ from results when more detailed models are available.

Research question III

How does energy consumption affect the life cycle cost when considering the investment cost for the building envelope?

This research question aims to investigate how life cycle costs are affected over certain time period by considering the construction cost of the building envelope and operating energy consumption.

1.3 Limitations

Many factors need to be considered in the life cycle performance of a building. This study is limited to the early design phase and how energy estimation can be used to influence the life cycle performance of a building, Figure 1-4.

Economic aspects

Human aspects

Life Cycle Design

Functionality aspects

Ecological aspects

Cultural aspects

Non- construction cost

Life cycle costIncome

Raw materialUse of energy

Use of waterWasteBiodiversityPollution

Economic aspectsEconomic aspects

Human aspectsHuman aspects

Life Cycle DesignLife Cycle Design

Functionality aspectsFunctionality aspects

Ecological aspects

Cultural aspects

Non- construction cost

Life cycle costIncome

Raw materialUse of energy

Use of waterWasteBiodiversityPollution

Figure 1-4. Delimitations of the study


6

The energy consumption has been focused on the building envelope design and to some extent the indoor temperature, i.e. the ventilation system has not been studied. The life cycle cost calculations in this study only include the operating energy cost and the investment cost of the building envelope. Furthermore, only one case building has been included in the study.

1.4 Thesis outline

The thesis is divided into the following Chapters.

Chapter 1 introduces the reader to the background and to motivation for this thesis. Also the aim, objective and research questions are presented.

Chapter 2 gives an overview of the research design and the methods used in this research.

Chapter 3 presents the theoretical framework, technologies and the state of the art on which this research is based.

Chapter 4 describes the design process proposed by the EU-Project InPro. This framework has been evaluated in the thesis with regard to energy performance analysis.

Chapter 5 describes the experimental set up for the energy and life cycle cost calculations and the used analytical tools.

Chapter 6 contains the results and analysis of the energy and LCC calculations.

Chapter 7 offers conclusions, discussions and recommendations for future research. The research questions are first answered followed by a discussion about the contribution of this thesis, and further research to be done.

Methodology

7

2 METHODOLOGY

This Chapter starts with a discussion and description of the chosen research design. The Chapter concludes with a discussion of the trustworthiness of this research.

2.1 Research design

When undertaking research it is important to choose a suitable strategy, to ensure that the research objectives can be met and that the findings can be validated (Fellows and Liu, 2003). Research design is a strategy for linking the research questions together in the research project (Robson, 2002). To find the right strategy for answering the research questions, different types of approach have been considered. Yin (1994) presents five types of research strategies to be used for different types of research questions.

- Experiment

- Survey

- Archival analyses

- History

- Case study

According to Yin (1994) the choice of research strategy depends on what kind of research questions there are. The research questions in this study are “what” and “how” questions.


8

Research questions starting with “what”, will give two possible approaches to the strategy. The first one is exploratory. The second type of “what” questions is more a form of asking “how many” or “how much” which can be answered more likely with the help of a survey or archival strategies. The exploratory research question could use any of the five mentioned strategies. Experiments as well as case studies can answer this kind of “what” question. As the first research question in this study is more of an exploratory type, all five research strategies can be used. The aim of the first research question is to identify a complex array of variable to set up a theoretical frame which should be tested in the further study. Hence the survey strategy was chosen.

The second and third research questions start with “how”. Research questions that begin with “how" or “why” are more explanatory. These questions seek to explain phenomena that occur over time and the way in which they occur. To answer these questions, it is more likely that the researcher will favour the use of case, histories and experiments (Yin, 1994). When considering Yin’s theory, a possible way of answering these questions would be an experimental study. Experimental studies are best suited to bound problems or issues in which the variables involved are known (Fellows and Liu, 2003). Further phenomena within its context can be discovered with experiments so that attention can be focused on a few variables only. As the second and third research questions aim to study how certain variables affect the energy performance and the life cycle cost an experimental study was chosen.

In Table 2-1, the main objectives, the methods used and the relation to the different research questions are summarised.

Methodology

9

Table 2-1. Objectives, research questions and methods

Main objectives Research question Method

To define the early design and investigate what type of energy simulations can be made at different levels of information maturity

RQ I - What types of energy simulations are possible to make in the early design phase?

Literature study

InPro EU-project

Interviews

To select a test building, preferably where the energy consumption is known. Compare energy simulations at different levels of maturity with known consumptions

RQ II - How good are early energy estimations compared to results when detailed models are available?

Energy simulations with different programs and levels of maturity.

InPro EU-project

Extend the test case by varying different parameters such as window area, building shell and indoor climate. Evaluate the life cycle costs for each set of parameters.

RQ III - How does the energy consumption affect the life cycle cost of a building?

Literature study

Energy simulations of the different test cases

LCC calculations

2.2 Research work

The work in this research project has been divided into three phases, phase I – III, as illustrated in Figure 2-1.

2.2.1 Phase I

Literature study: The research started with a literature study of life cycle cost and life cycle design. A postgraduate course in life cycle cost was taken, which gave a good overview of the difficulty of collecting reliable data for the life cycle cost analysis. The literature study was extended to energy calculation and simulations for buildings. The main sources for the literature research are scientific journal articles, conference papers, technical reports, books and theses. The literature was found in scientific databases, conference proceedings, libraries databases, and reference list of the read material. The key


10

words were life cycle cost (LCC) and life cycle costing, whole life cost (WLC) and whole life appraisal, life cycle design, energy simulation for buildings, building physics, passive house, low energy houses, Energy Performance of Buildings Directive; EnEV; BBR, BIM, etc.

Research Design

Phase II Phase IIIPhase I

Literature study

InProEU-Project

Interview InPro approach for the design process LCC calculations

Analysis

Discussion and

conclusion

Analysis

Energy estimation/ calculation at the

different maturity levels

Figure 2-1. Research design

Interview: The interviews made were supplementing the literature study and attaining a deeper understanding of the underlying motives, the problems and the current practice. Further, the interviews were conducted to gain a better understanding of what objective targets the construction client and the energy consultations would like to achieve. The interviews were semi- structured interviews with one construction client and five energy consultants.

InPro Project: The work was to a great extent performed by stakeholders of the construction value chain supported by expertise in model based design methods.

The methodology used the task of model-based life cycle design was a combination of:

Methodology

11

- Literature reviews.

- Unstructured interviews with clients, contractors and energy consultants.

- The participants’ own experience.

- Workshops and project meetings within the InPro consortium.

- Development of demonstration scenarios

InPro approach for the design process is a proposal from the InPro project regarding model based design in the early phase of a building project. The proposals including definitions of early design and levels of information maturity has been used to design the experimental setup in the thesis. See also methods used in the InPro project above.

2.2.2 Phase II

Energy calculation: The energy calculations were performed on the basis of the developed InPro approach for the energy process and the defined maturity levels in the early design phase. Energy calculations were carried out at the conceptual, functional and system maturity levels, with different calculation/estimation programs (see Chapter 5). The study was performed for an existing building where design parameters like window area, building envelope and indoor climate were changed. In total, 28 cases were analysed using four different energy analysis tools.

Figure 2-2. Experimental set up


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Analysis: The calculated annual energy consumption for all 28 cases was collected into a Microsoft Excel sheet. The energy consumption for this study was defined according to the Swedish National Board of Building, Planning and Housing (Boverket) which includes energy consumption for heating and cooling but excludes domestic (household) electricity. In order to illustrate the energy consumption results graphs were constructed in Microsoft Excel and Grapher from Golden software. The annual energy consumption for the different cases was analysed, in relation to how the design parameters affect the consumption, but also in relation to the original building and the actual energy consumption.

2.2.3 Phase III

LCC calculation: The LCC calculation was performed for the 28 different cases in Microsoft Excel. The LCC calculations include the resulting annual energy cost based on the energy consumption calculated at the system maturity level and the investment cost for the different building envelopes. The net present value method was used for this calculation based on a fixed discounting time period. Parameters like rate of interest and the energy prices development were changed to investigate their effect on the result of the life cycle cost.

Analysis: In order to analyse the different LCC calculations graphs in Microsoft Excel were created. The results were analysed in relation to how the different modified parameters affect the results and how energy consumption and investment cost affect the LCC over a certain time period.

Discussion and conclusions: Finally, the results of the research work were collected and concluded by answering the research questions and stating the contribution of the research, both scientific and practical.

2.3 Validity, reliability and generalization

For the reliability and validity of an experimental research approach/project it is important that the researcher provides sufficient information on the methods used and the justification for their use (Robson, 2002). The reliability depends on how reliable the results of the study are and on whether the same results would be obtained when repeating exact the same study. If the data support the verification of the theory, the results can be used to generalise the theory, but not the results themselves in other contexts (Fellows and Liu, 2003).

Methodology

13

To ensure the reliability of this research project, the steps of each process were documented. The used input data for the energy calculation are documented in Appendix A. The interviews were audio-taped and transcribed into structured notes, to ensure that no important information was lost.

The experiment was performed for an existing office building in Finland. The advantage of that is that the actual energy consumption of this building could be compared with the calculated energy consumption. The weakness in this study is that the different energy calculation programs needed different input data and that not all input data for the different programs was available. In this case assumptions were made, which might influence the calculated energy consumption. As the experiment was performed for a certain building type, a generalisation in the sense that the findings in this research could be applied to similar projects might be restricted to this building type. However, this study can be used for generalisation, in the sense that the results and methodologies can be used to generalise the theory.

Theoretical framework

15

3 THEORETICAL FRAMEWORK

This Chapter describes the theoretical framework needed to address the research questions in this study.

3.1 Sustainable design aspects

A life cycle design is a design which takes all aspects into account that affect the building during its estimated life time. The World Commission on Environment and Development defines sustainable development as “seeks to meet the needs and aspirations of the present without compromising the ability to meet those of the future” (Allaby, 2006). Curl (2006), defines sustainable architecture as an architecture that does not guzzle energy, require expensive maintenance or is subject to massive heat-loss or gain trough poor insulation or too much glazing. According to Kohler (1999) a sustainable building consists of three main parts of sustainability; ecological, economic and social/cultural development, see Figure 3-1.

The ecological sustainability is related to protection of the resources and the ecosystem (Kohler, 1999). A common quantitative framework for ecological sustainability is the mass flows and energy consumptions in time and space. Therefore different life cycle assessment (LCA) tools are applicable.

Economic sustainability should consider the long term resource productivity of solutions with high durability and reusability of used resources. But also low running costs, i.e. buildings with low energy consumption, that are easy to clean, operate and maintain. Buildings with low running costs have generally a low environmental impact (Kohler, 1999).


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According to Kohler (1999), social and cultural aspects of sustainability include comfort, well being, human health, protection of the users and workers in the building.

Figure 3-1. The three dimensions of sustainability and some associated goals for buildings, Kohler (1999)

Sarja (1997) proposed the following definition, “sustainable building is a technology and practice which meets the multiple requirements of the people and society in an optimal way during the life cycle of the built facility”. Sarja (2002) further defined sustainability to include social, economic, functional, technical and ecological aspects of a building

He also proposed an integrated life cycle design methodology to improve the life cycle quality where the aspects of human conditions, financial costs, culture and ecology were included (Sarja, 2002), see Figure 3-2.


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Ecological aspects•Energy costs•Raw materials costs•Environmental burdens costs •Waste costs

Financial cost aspects•Investment costs•Construction costs•Running costs•End of life cycle

Human conditions•Functionality•Functionality in use•Convenience•Safety•Health

Cultural aspects•Building traditions•Business culture•Architectural styles and trends•Aesthetics•Image

Integrate life cycle designLife cycle qualityLife cycle performance

WLC

Ecological aspects•Energy costs•Raw materials costs•Environmental burdens costs •Waste costs

Financial cost aspects•Investment costs•Construction costs•Running costs•End of life cycle

Human conditions•Functionality•Functionality in use•Convenience•Safety•Health

Cultural aspects•Building traditions•Business culture•Architectural styles and trends•Aesthetics•Image

Integrate life cycle designLife cycle qualityLife cycle performance

WLC

Figure 3-2. Main aspects of integrated life cycle quality. After Sarja (2002).

3.2 Life Cycle Cost - LCC

3.2.1 Definition of LCC

Most sustainability models include some form of financial estimates of the costs over the life cycle, so called life cycle cost (LCC).

The LCC analysis of construction projects consists of the following four main cost items:

- Construction cost (including design and engineering costs)

- Operation costs (rent, insurance, cyclical regulatory costs, utilities, tax etc)

- Maintenance costs (including costs for adaptation)

- End of life costs (disposal and demolition)


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The new draft for the ISO 15686-5(2007) (Building and construction assets, service life planning) declares that the whole life cost (WLC) should take all cost elements into account which accumulate during the entire life cycle of a building/construction.

As shown in Figure 3-3, WLC includes LCC plus non construction costs and income. Non construction costs can be understood as costs that arise during the life cycle but cannot be directly linked to the building or construction costs like for example IT service, reception, helpdesk, parking charge, furniture. Income can be understood as the income for the building owner during the life cycle for example in the form of rent and service charges (ISO, 2007).

Figure 3-3. Whole life cost and Life cycle cost Elements, draft ISO 15686-5 (2007)

There are different terms used in the literature today like, “cost in use”, “life cycle costs” (LCC), “whole life costing” (WLC) and “whole life appraisal” (WLA). Flanagan and Jewell (2005) describe that the terminology has changed over the years from “cost in use” to “life cycle costing” and further to “whole life costing”. They define the new term “whole life appraisal” which is globally used today and which contains consideration of the cost benefits and performance of the facility/ asset over its lifetime.


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The ISO draft 15686-5 (ISO, 2007) instead makes a difference between the expressions WLC and LCC. Its argument is that WLC is equivalent to LCC plus external cost. It is also admitted there that sometimes all terms are used interchangeably, but the ISO Standard does try to interpret those terms more narrowly. The ISO standard states that LCC should be used to describe a limited analysis of a few components where instead “life cycle costing” should be understood as the cost calculations themselves and WLC should be seen as a broader term, which covers a wide range of analysis. The Norwegian standard 3454 (NS, 2000) defines LCC as including both original costs and cost incurred throughout the whole functional lifetime including demolition.

Discussions about wording bring a lot of confusion into this field. In this Thesis, the definition from the ISO draft 15686-5 is used as presented in Figure 3-3.

3.2.2 Calculation methods

Several cost-based LCC calculation methods are available for the construction sector. They all have their advantages and disadvantages. According to the reviewed literature, the most suitable approach to life cycle cost in the construction industries is the net present value method (NPV) or, in the case of comparing alternative schemes with different lifetimes, the Equivalent Annual Cost, ECA. The NPV method is mainly used in existing LCC tools today. The user should bear in mind that different methods have been formed for different purposes. For example, in the case of a rough estimate, to distinguish if the investment is profitable, or not, the payback method can be the most suitable. Consequently, other measures shown in Table 3-1 can be used if the purpose of the LCC calculations is considered.


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Table 3-1. Advantages and disadvantages of evaluation methods for LCC

Can

be

used

to c

ompa

re

inve

stm

ent o

ptio

ns (

ISO

, 20

04).

But

onl

y if

the

inve

stm

ent g

ener

ates

an

inco

me

(Kis

hket

al.,

20

03).

NS

can

onl

y be

use

d if

the

inve

stm

ent g

ener

ates

an

inco

me

(Kis

hket

al.,

200

3).

Eas

ily u

nder

stoo

d in

vest

men

t ap

prai

sal t

echn

ique

(Kis

hket

al.,

20

03).

The

NS

is c

alcu

late

d as

the

diffe

renc

e be

twee

n th

e pr

esen

t w

orth

of t

he in

com

e ge

nera

ted

by a

n in

vest

men

t and

the

amou

nted

inve

sted

. The

alte

rnat

ive

with

the

high

est n

et

savi

ng is

the

best

(Kis

hket

al.,

200

3).

Net

sav

ing

(NS)

Can

onl

y be

use

d if

the

inve

stm

ents

will

gene

rate

an

inco

me,

whi

ch is

not

al

way

s th

e ca

se in

the

cons

truct

ion

indu

stry

(Kis

hket

al.,

20

03).

Cal

cula

tions

nee

d a

trial

and

er

ror p

roce

dure

. IR

R c

an o

nly

be c

alcu

late

d if

the

inve

stm

ents

will

gen

erat

e an

in

com

e (F

lana

gan

et a

l.,

1989

).

Res

ult i

s pr

esen

ted

in p

erce

nt

whi

ch g

ives

an

obvi

ous

inte

rpre

tatio

n (F

lana

gan

et a

l.,

1989

).

The

IRR

is a

dis

coun

ted

cash

flow

crit

erio

n w

hich

det

erm

ines

an

ave

rage

rate

of r

etur

n by

refe

renc

e to

the

cond

ition

that

th

e va

lues

be

redu

ced

to z

ero

at th

e in

itial

poi

nt o

f tim

e (M

oles

and

Ter

ry, 1

997)

. It i

s po

ssib

le to

cal

cula

te th

e te

st

disc

ount

rate

that

will

gen

erat

e an

NP

V o

f zer

o. T

he

alte

rnat

ive

with

the

high

est I

RR

is th

e be

st a

ltern

ativ

e (IS

O,

2004

)

Inte

rnal

rate

of

retu

rn (I

RR

)

Com

parin

g di

ffere

nt

alte

rnat

ives

with

diff

eren

t lif

e-le

ngth

s (IS

O, 2

004)

.

Just

giv

es a

n av

erag

e nu

mbe

r. It

does

not

indi

cate

th

e ac

tual

cos

t dur

ing

each

ye

ar o

f the

LC

C (I

SO

, 200

4).

Diff

eren

t alte

rnat

ives

with

di

ffere

nt li

fe-le

ngth

s ca

n be

co

mpa

red

(ISO

, 200

4).

This

met

hod

expr

esse

s th

e on

e tim

e N

PV

of a

n al

tern

ativ

e as

a

unifo

rm e

quiv

alen

t ann

ual c

ost,

beca

use

it ta

kes

the

fact

or

of p

rese

nt w

orth

of a

nnui

ty in

to a

ccou

nt (K

ishk

et a

l., 2

003)

.

Equi

vale

nt a

nnua

l co

st (E

CA)

Mos

t LC

C m

odel

s ut

ilize

the

NP

V m

etho

d (K

ishk

et

al.,

2003

). N

ot u

sabl

e if

the

alte

rnat

ives

hav

e di

ffere

nt

life-

leng

ths

(Fla

naga

n et

al

., 19

89).

Not

usa

ble

whe

n th

e co

mpa

ring

alte

rnat

ives

hav

e di

ffere

nt li

fe-le

ngth

s.

Not

eas

y to

inte

rpre

t (K

ishk

et

al.,

2003

).

Take

s th

e tim

e va

lue

of m

oney

in

to a

ccou

nt.

Gen

erat

es th

e re

turn

equ

al to

th

e m

arke

t rat

e of

inte

rest

. It

uses

all

avai

labl

e da

ta

(Fla

naga

n et

al.,

198

9).

NP

V is

the

resu

lt of

the

appl

icat

ion

of d

isco

unt f

acto

rs, b

ased

on

a re

quire

d ra

te o

f ret

urn

to e

ach

year

’s p

roje

cted

cas

h flo

w, b

oth

in a

nd o

ut, s

o th

at th

e ca

sh fl

ows

are

disc

ount

ed to

pr

esen

t val

ue. I

n ge

nera

l, if

the

NP

V is

pos

itive

, it i

s w

orth

w

hile

inve

stin

g (S

mul

len

and

Han

d, 2

005)

. But

as

in L

CC

the

focu

s is

on

one

cost

rath

er th

an o

n in

com

e, th

e us

ual p

ract

ice

is to

trea

t cos

t as

posi

tive

and

inco

me

as n

egat

ive.

C

onse

quen

tly th

e be

st c

hoic

e be

twee

n tw

o co

mpe

ting

alte

rnat

ives

is th

e on

e w

ith m

inim

um N

PV

(Kis

hket

al.,

20

03).

Net

pre

sent

val

ue

(NPV

)

Sho

uld

only

be

used

as

a sc

reen

ing

devi

se, n

ot a

s a

deci

sion

adv

ice(

Flan

agan

et a

l., 1

989)

.

Igno

res

all c

ash

flow

out

side

th

e pa

ybac

k pe

riod(

Flan

agan

et a

l., 1

989)

Take

s th

e tim

e va

lue

of m

oney

in

to a

ccou

nt (F

lana

gan

et a

l.,

1989

).

Bas

ical

ly th

e sa

me

as th

e si

mpl

e pa

ybac

k m

etho

d, it

just

ta

kes

the

time

valu

e in

to a

ccou

nt (F

lana

gan

et a

l., 1

989)

.D

isco

unt p

ayba

ck

met

hod

(DPP

)

Rou

gh e

stim

atio

n of

w

heth

er th

e in

vest

men

t is

prof

itabl

e (F

lana

gan

et a

l.,

1989

).

Doe

s no

t tak

e in

flatio

n,

inte

rest

or c

ash

flow

into

ac

coun

t (Ö

berg

, 200

5,

Flan

agan

et a

l., 1

989)

.

Qui

ck a

nd e

asy

calc

ulat

ion.

R

esul

t eas

y to

inte

rpre

t (F

lana

gan

et a

l., 1

989)

.

Cal

cula

tes

the

time

requ

ired

to re

turn

the

initi

al in

vest

men

t. Th

e in

vest

men

t with

the

shor

test

pay

back

tim

e is

the

mos

t pr

ofita

ble

one

(Fla

naga

n et

al.,

198

9).

Sim

ple

payb

ack

Usa

ble

for

Dis

adva

ntag

eA

dvan

tage

Wha

t doe

s it

calc

ulat

eM

etho

d

Can

be

used

to c

ompa

re

inve

stm

ent o

ptio

ns (

ISO

, 20

04).

But

onl

y if

the

inve

stm

ent g

ener

ates

an

inco

me

(Kis

hket

al.,

20

03).

NS

can

onl

y be

use

d if

the

inve

stm

ent g

ener

ates

an

inco

me

(Kis

hket

al.,

200

3).

Eas

ily u

nder

stoo

d in

vest

men

t ap

prai

sal t

echn

ique

(Kis

hket

al.,

20

03).

The

NS

is c

alcu

late

d as

the

diffe

renc

e be

twee

n th

e pr

esen

t w

orth

of t

he in

com

e ge

nera

ted

by a

n in

vest

men

t and

the

amou

nted

inve

sted

. The

alte

rnat

ive

with

the

high

est n

et

savi

ng is

the

best

(Kis

hket

al.,

200

3).

Net

sav

ing

(NS)

Can

onl

y be

use

d if

the

inve

stm

ents

will

gene

rate

an

inco

me,

whi

ch is

not

al

way

s th

e ca

se in

the

cons

truct

ion

indu

stry

(Kis

hket

al.,

20

03).

Cal

cula

tions

nee

d a

trial

and

er

ror p

roce

dure

. IR

R c

an o

nly

be c

alcu

late

d if

the

inve

stm

ents

will

gen

erat

e an

in

com

e (F

lana

gan

et a

l.,

1989

).

Res

ult i

s pr

esen

ted

in p

erce

nt

whi

ch g

ives

an

obvi

ous

inte

rpre

tatio

n (F

lana

gan

et a

l.,

1989

).

The

IRR

is a

dis

coun

ted

cash

flow

crit

erio

n w

hich

det

erm

ines

an

ave

rage

rate

of r

etur

n by

refe

renc

e to

the

cond

ition

that

th

e va

lues

be

redu

ced

to z

ero

at th

e in

itial

poi

nt o

f tim

e (M

oles

and

Ter

ry, 1

997)

. It i

s po

ssib

le to

cal

cula

te th

e te

st

disc

ount

rate

that

will

gen

erat

e an

NP

V o

f zer

o. T

he

alte

rnat

ive

with

the

high

est I

RR

is th

e be

st a

ltern

ativ

e (IS

O,

2004

)

Inte

rnal

rate

of

retu

rn (I

RR

)

Com

parin

g di

ffere

nt

alte

rnat

ives

with

diff

eren

t lif

e-le

ngth

s (IS

O, 2

004)

.

Just

giv

es a

n av

erag

e nu

mbe

r. It

does

not

indi

cate

th

e ac

tual

cos

t dur

ing

each

ye

ar o

f the

LC

C (I

SO

, 200

4).

Diff

eren

t alte

rnat

ives

with

di

ffere

nt li

fe-le

ngth

s ca

n be

co

mpa

red

(ISO

, 200

4).

This

met

hod

expr

esse

s th

e on

e tim

e N

PV

of a

n al

tern

ativ

e as

a

unifo

rm e

quiv

alen

t ann

ual c

ost,

beca

use

it ta

kes

the

fact

or

of p

rese

nt w

orth

of a

nnui

ty in

to a

ccou

nt (K

ishk

et a

l., 2

003)

.

Equi

vale

nt a

nnua

l co

st (E

CA)

Mos

t LC

C m

odel

s ut

ilize

the

NP

V m

etho

d (K

ishk

et

al.,

2003

). N

ot u

sabl

e if

the

alte

rnat

ives

hav

e di

ffere

nt

life-

leng

ths

(Fla

naga

n et

al

., 19

89).

Not

usa

ble

whe

n th

e co

mpa

ring

alte

rnat

ives

hav

e di

ffere

nt li

fe-le

ngth

s.

Not

eas

y to

inte

rpre

t (K

ishk

et

al.,

2003

).

Take

s th

e tim

e va

lue

of m

oney

in

to a

ccou

nt.

Gen

erat

es th

e re

turn

equ

al to

th

e m

arke

t rat

e of

inte

rest

. It

uses

all

avai

labl

e da

ta

(Fla

naga

n et

al.,

198

9).

NP

V is

the

resu

lt of

the

appl

icat

ion

of d

isco

unt f

acto

rs, b

ased

on

a re

quire

d ra

te o

f ret

urn

to e

ach

year

’s p

roje

cted

cas

h flo

w, b

oth

in a

nd o

ut, s

o th

at th

e ca

sh fl

ows

are

disc

ount

ed to

pr

esen

t val

ue. I

n ge

nera

l, if

the

NP

V is

pos

itive

, it i

s w

orth

w

hile

inve

stin

g (S

mul

len

and

Han

d, 2

005)

. But

as

in L

CC

the

focu

s is

on

one

cost

rath

er th

an o

n in

com

e, th

e us

ual p

ract

ice

is to

trea

t cos

t as

posi

tive

and

inco

me

as n

egat

ive.

C

onse

quen

tly th

e be

st c

hoic

e be

twee

n tw

o co

mpe

ting

alte

rnat

ives

is th

e on

e w

ith m

inim

um N

PV

(Kis

hket

al.,

20

03).

Net

pre

sent

val

ue

(NPV

)

Sho

uld

only

be

used

as

a sc

reen

ing

devi

se, n

ot a

s a

deci

sion

adv

ice(

Flan

agan

et a

l., 1

989)

.

Igno

res

all c

ash

flow

out

side

th

e pa

ybac

k pe

riod(

Flan

agan

et a

l., 1

989)

Take

s th

e tim

e va

lue

of m

oney

in

to a

ccou

nt (F

lana

gan

et a

l.,

1989

).

Bas

ical

ly th

e sa

me

as th

e si

mpl

e pa

ybac

k m

etho

d, it

just

ta

kes

the

time

valu

e in

to a

ccou

nt (F

lana

gan

et a

l., 1

989)

.D

isco

unt p

ayba

ck

met

hod

(DPP

)

Rou

gh e

stim

atio

n of

w

heth

er th

e in

vest

men

t is

prof

itabl

e (F

lana

gan

et a

l.,

1989

).

Doe

s no

t tak

e in

flatio

n,

inte

rest

or c

ash

flow

into

ac

coun

t (Ö

berg

, 200

5,

Flan

agan

et a

l., 1

989)

.

Qui

ck a

nd e

asy

calc

ulat

ion.

R

esul

t eas

y to

inte

rpre

t (F

lana

gan

et a

l., 1

989)

.

Cal

cula

tes

the

time

requ

ired

to re

turn

the

initi

al in

vest

men

t. Th

e in

vest

men

t with

the

shor

test

pay

back

tim

e is

the

mos

t pr

ofita

ble

one

(Fla

naga

n et

al.,

198

9).

Sim

ple

payb

ack

Usa

ble

for

Dis

adva

ntag

eA

dvan

tage

Wha

t doe

s it

calc

ulat

eM

etho

d


21

3.2.3 Data required for life cycle cost calculation

The input required according to the reviewed literature for carrying out LCC analysis is categorised in Figure 3-4. These inputs influence the LCC outcome at different stages of the life cycle.

Cost DataAcquisition cost

Capital costTaxes

InflationDiscount rate

Management costReplacement costMaintenance cost

Operating costCleaning cost

Demolition costInsurance

Performance Data

Maintenance cyclesCleaning cycles

Thermal conductivityOccupancy time

ElectricityGas

Physical DataSuperficial floor area

Types of boiler/ heating systems

Window areaFunctional areas

Number of occupantsWalls and ceilings

No of sanitary fittings

Quality DataCondition of:

- sanitary fittings- pipe work- furnishing

- boiler-decorations

- fabric-road surfacing

Types of Life Cycle Data

Occupancy dataOccupancy profile

FunctionalityHours of use

Particular feature

Figure 3-4. The required data categories for a life cycle cost analysis

The occupancy and physical data could be seen as the key factors at the early design stage. LCC estimation at this stage depends on data such as floor area and the requirements for the building. Flanagan et al (1989) stressed the importance of occupancy data as other key factors, especially for public buildings. Performance and quality data are rather influenced by policy decisions such as how well it should be maintained and the degree of cleanliness demanded (Kishk et al., 2003). Quality data are highly subjective and less readily accountable than cost data (Flanagan et al., 1989). At the more detailed design stage, life cycle cost estimation is based more on performance


22

and cost data of a building (Bakis et al., 2003). Cost data are most essential for LCC research. However, cost data that are not supplemented by other data types would be almost meaningless (Flanagan et al., 1989). This data needs to be seen in the context of other data categories to obtain a correct interpretation of them (Kishk et al., 2003).

It should be considered that LCC is a decision making tool in the sense that it could be used to select among alternative projects, designs or building components. Consequently LCC data should be presented in a way that enables such comparison. For that reason the cost breakdown structure is an important concept for LCC (Bakis et al., 2003).

There are several different standards (ISO 15686-5/ NS3454/ ASTM/ Australian/ New Zealand-Standard) available to guide a LCC analysis. All have different cost categories and slightly different cost breakdown structures.

3.2.4 Problems and difficulties using LCC for buildings today

LCC needs time and effort. For that reason, there has to be a clear output motive to use LCC if it is to be worth the effort for the construction client (Raymond and Sterner, 2000). The availability of LCC input data are today rather limited. One reason is the lack of framework for collecting and storing input data (Bakis et al., 2003). Construction clients often give a low priority to LCC as they are not aware of the benefits (Raymond and Sterner, 2000).

The choice of the right calculation method for LCC is easy and obvious if the advantages and disadvantages are appreciated. The calculation of LCC is not difficult and for structuring the necessary input, help is available in the form of different standards such as ISO or the Norwegian standard. Nonetheless, the collection of required input causes difficulties. Data need to be predictable if the LCC analysis is to be reliable. Regional databases are seldom available or usable. To collect data by hand, takes much time and can be costly. This is worthwhile if the project is big enough. When historical data are collected and updated over time, the LCC analysis becomes more reliable and trustworthy.

Data should be shared to avoid the duplicated effort of collecting them. If more clients demand LCC information and a proper check of the information against performance is made in the future, improvement in accuracy and reliability can be expected. When LCC is used more frequently, the construction client can judge LCC in the same manner as they do with estimated capital costs today. The construction client, and the end-user, can save much money in the long


23

run, if LCC is adopted as a decision making tool. The lifetime quality and the cost effectiveness of operation would improve by using LCC at the early design stage.

According to the reviewed literature the collection of the data is the main difficulty in calculating the LCC for a building. Storing collected data in databases seems to be a good alternative, and would save time and offer easier access to input data. However, the databases would only be valid for the local setting in which the data were collected. Even so, building local databases would be a solution so long as there is regular updating.

3.3 CO2 emission

The International concern about CO2 emissions led to the Rio de Janeiro Convention of 1992 and to the Kyoto protocol of 1997. The European Union committed itself to reduce its emissions by 8% of the 1990 levels by 2008-2012. This 8% reduction of the 1990 CO2 levels is an average number. Each Member State sets its own emission reduction percentage depending on its degree of industrialisation and current per capita per annum emission level. This percentage varies from country to country, ranging from a 28% reduction in Luxembourg up till Sweden which was actually permitted to increase their emission during this period. The data from the European Environment Agency concerning the CO2 emission during the period from 1990-1999 in the 15 EU Member States shows an overall reduction of the emission. However, there are big gaps between states that have cut their emission levels considerably, like the UK and Germany, and others that are far from meeting the target levels set in the agreement. One reason that e.g. Germany has been successful can be the higher demands on the energy consumption for dwellings set by their national standards.

The building stock in Europe accounts for over 40% of the CO2 emission as well as the energy consumption in the European Union (Directive 2202/91/EC) of which dwellings represent 67% of the total energy consumption (EEA, 2006). This shows the potential for reducing the emissions by energy savings in the building sector.

3.4 Energy consumption in the building sector

The energy consumption of a building during its operating life affects both the operating cost and the environment. In European residential buildings, about


24

57% of the total final energy consumption is used for space heating, 25% for domestic hot water and 11% for electricity (Chwieduk, 2003). Research shows that more than one fifth of the present energy consumption could be saved by 2010 by applying tougher standards to new buildings and to buildings undergoing major refurbishment (European-Commission, 2003). Minimum standards for building energy performance will be determined by the member states and will be applied both to new buildings and to major refurbishments of the existing large buildings. However, the energy consumption is still increasing for the European stock of dwellings, mainly due to the increasing number of dwellings and the higher living standard along with the introduction of new equipment and appliances (Balaras et al., 2007).

The operating energy cost for a building is dependent on the energy consumption and the price for energy. Forecasts of energy prices for the future are difficult to make, but statistics from the past can be considered. The average prices for household electricity in the EU-15 increased significantly between 1997 and 2007. Especially, in Sweden the price for electricity has rapidly increased during the last 3 years. As a comparison, the electricity market price is higher in Germany compared with the average prices from the EU-15 countries, see Figure 3-5.

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

year

€EU (15 countries)

Germany

Sweden

Figure 3-5. Electricity prices for households (without tax) per kWh (Eurostat, 2008)

Note, the prices given in the figure are without tax.


25

The total energy consumption in the EU-15 Member States is 1 007 412 toe (tonnes of oil equivalent). The household and service sector energy consumption consists predominantly of space heating (EEA, 2006).

27%

33%

40%

IndustryTransportHoushold, trades,service, etc.

Figure 3-6. Share of final energy consumption by sectors in 2006, EU-15 (Eurostat, 2008)

The total electrical energy consumption in the EU-15 Member States is 2488741 GWh (Eurostat, 2008). The household and service sector’s electricity consumption is dominated by lighting, electrical appliances and a rapidly growing consumption of space cooling (EEA, 2008).

57%

3%

40%

Houshold and serviceTransportIndustry

Figure 3-7. Share of electrical energy consumption by sector in 2006, EU-15 (Eurostat, 2008)


26

3.5 Energy performance of buildings in the EU

3.5.1 The EU Directive 2002/91/EC - EPBD

The EU Directive 2002/91/EC from 2002 (European-Parliament, 2002) regarding the energy performance of buildings (“Energy Performance of Buildings Directive", EPBD), was introduced to ensure that the building standards in Europe have a high emphasis on minimising energy consumptions. The Member States had to incorporate the directive into their national legislation by January 2006. The directive is set to promote the improvement of energy performance of buildings with the following requirements to be implemented by the Member States:

- A common methodology for calculating the energy performance of a building on a national or regional level, taking into account local climatic conditions.

- Minimum standards for energy performance of new buildings and to major refurbishments of existing large buildings.

- Building certification will make energy consumption levels more visible to the owner/user.

- Regular inspection of boilers and air-conditioning systems in buildings and in addition an assessment of the heating installation in which the boilers are more than 15 years old;

- Requirements for experts and inspectors for the certification of buildings, the drafting of the accompanying recommendations and the inspection of boilers and air-conditioning systems.

Within these general principles and objectives, it is the responsibility of each EU Member State to specify the measures that correspond best to its particular situation (subsidiary principle). However, it is clear that collaboration and information exchange among the Member States can significantly facilitate the implementation. The implementation status of the EPBD in the different Member States is regularly reviewed.

3.5.2 Implementation of the EPBD in Sweden and Germany

Sweden has implemented the EPBD in the national regulations from 2006. The national regulations, from the National board of Building, Planning and


27

Housing (Boverket), came into force in March 2007. The energy performance will be measured by operational rating for all types of buildings. The country is divided into two climate zones (north and south) and the maximum energy consumption per m2 of tempered floor area is given. The requirements for new buildings are different for residential and non residential buildings. For dwellings the maximum specific energy consumption (excluding the use of household electricity) are 130 kWh/(m2 a) for the Northern Sweden and 110 kWh/(m2 a) for Southern Sweden.

In the case of space heating with direct electricity for a one- or two-story house, the specific energy consumption (excluding the household electricity) should not exceed 95 kWh/(m2 a) for the climate zone north and 75 kWh/(m2 a) for the climate zone south. Furthermore the U-value is limited to a maximum of 0.5 W/m2K. For premises the specific energy consumption (excluding the domestic electricity) is allowed up to 100 kWh/(m2 a) for the climate zone south and in the north 120 kWh/(m2 a) (BBR, 2006).The regulations also include some advice about comfort and indoor environment. In cases where no measured value is available, calculation may be used to estimate the energy consumption.

There is no general calculation method and software tool for energy calculations in Sweden. The proof of fulfilment must be made within 24 months after completion of the building. Control of the regulation is the responsibility of the municipality where the building is located. The certification of buildings is obligatory for new buildings from the year 2009. Certification is required for public buildings and multi-family houses from the end of 2008. Buildings which are let or sold must have an energy performance certification from the 1st of January 2009. The Information about boilers will be made by the Swedish Energy Agency. The inspection for air conditioning systems also started from the 1st of January 2009. The inspection estimates the efficiency of the system and help the owner to improve the energy efficiency. Sweden is also introducing an independent energy expert role with the right to issue certifications.

In Germany the federal office for building and regional planning is responsible for the implementation of the EPBD. The EPBD is implemented in the legal context of the Energy Saving Act which the Energy Saving Ordinance (EnEV) is based on. Germany has a general calculation method for residential buildings based on the two German pre-standards (DIN V 4108-6:2003-06 and DIN V 470110:2003-08). The German Standardisation Institute (DIN) has also published a new standard (DIN V 18599 (Part1-10)) for an overall calculation


28

method for energy performance of buildings including all aspects of the EPBD. The federal government initiated this standard, in order to have a universal method, covering the aspects primarily for non residential buildings. In the Energy Saving Ordinance the requirements for new buildings are dependent on the function and type of building and also on the surface/Volume ratio. The German regulator demands in the Energy Saving Ordinance (EnEV-Energieeinsparverordnung) from 2007 that the annual net heat demand is a function of the proportion between A/Ve [Envelope area/Volume of the heated area]. The main demand is on the maximum annual primary energy consumption as shown in Table 3-2 below.

Table 3-2. Maximal annual primary energy consumption for new buildings Germany (EnEV, 2007)

Annual primary energy requirements Specified

transmission losses

Qp in kWh/(m2 a) correlating to the net gross area Hτ W/(m2 K) Relation A/Ve

Dwellings Dwellings with mainly

electrical hot water systems

Dwellings

1 2 3 4 ≤ 0.2 66.00 + ΔQTW 83.80 1.05 0.3 73.53 + ΔQTW 91.33 0.80 0.4 81.06 + ΔQTW 98.89 0.68 0.5 88.58 + ΔQTW 106.39 0.60 0.6 96.11 + ΔQTW 113.91 0.55 0,7 103.64 + ΔQTW 121.44 0.51 0,8 111.17 + ΔQTW 128.97 0.49 0,9 118.70 + ΔQTW 136.50 0.47 1 126.23 + ΔQTW 144.03 0.45

≥1,05 130.00 + ΔQTW 147.79 0.44

where N

TW AmakWhQ

+=Δ 2100

/2600 and AN = 0.32 Ve

The main requirements are specified for maximum primary energy, maximum average U-value, maximal U-value for each element of the building’s fabric, requirements on quality of boilers, controls and pipe insulations, building air tightness and the prevention of thermal bridges. For existing buildings the requirements in case of refurbishment consist either of a maximum primary


29

energy demand and a maximum average U-value or a maximum U-value for each element of the refurbishment. The certification of buildings has been obligatory for new buildings since 2002. The Inspection of boilers is covered by the small and medium combustion plant Ordinance (1997). The inspection of air conditions is described in the revision of the Energy Saving Ordinance and will come into force in several steps. Germany is also planning an amendment of the Energy Saving Ordinance including several directives to simplify the inspection procedures to keep the cost of certification low.

The main difference between the two implementations of the EPBD is that Sweden specifies the requirement on the final energy consumption of a building and does not determine the specific regulation to calculate the specific energy consumption. Further it does not set any requirements on the heat and cooling systems except that they should be automatically controlled depending on the effect need in relation to the outside climate.

The Swedish regulations demand a control system for the energy consumption to be able to calculate the energy consumption for a certain time period.

Sweden and Germany are going to reform the laws regarding the energy consumption for buildings. The goal for Germany’s new Energy Saving Ordinance, planned for 2009 is to save approximately 30% of the energy consumption for space heating and hot water (EnEV, 2009), and to increase the required thermal isolation for the envelope area by about 30%. Also, the regulation of the implemented heat system should be stricter (EnEV, 2009). The main changes are implemented in the Swedish regulation since February 2009. Sweden is now divided into three climate zones instead of two. Climate zone one, in the north, is allowed to increase the specific energy consumption from 130kWh/(m2 a) up to 150 kWh/(m2 a), while the two other zones will keep the previous required consumptions of 130 and 110 kWh/(m2 a) respectively. The new Swedish regulation is implementing stricter regulations of the allowed used energy effect for buildings using electrical space heating. It also limits the average U-value for all three climate zone down to 0.4 W/m2K for dwellings (BBR, 2008).

3.5.3 Low energy houses

The meaning of the term “low energy houses” has changed over time and will probably change in the future. National standards vary and there is no general definition of low energy houses in Europe. One figure used for low energy house is the consumption of space heating up to 30 kWh/m2 per year


30

(Audenaert et al., 2008), but mostly the term “low energy houses” is used for buildings with energy consumptions below the standard demanded by the current national building code. Therefore the term low energy houses should be used carefully as national standards vary from country to country.

For example, according to the German Energy Agency (DENA, 2008) the standard norm in the German Energy Saving Ordinance (EnEV) from 2002 requires that new buildings are of a low energy house type. Until the Energy saving Ordinance came into force the specific heat energy consumption for new buildings was set in the thermal Insulation Ordinance in 2001 to 70 kWh/m2 per year. The Energy Saving Ordinance does not give an explicit number for the specific heat energy consumption. Instead the allowed total primary annual energy consumption is a function of the Envelope area/Volume of the heated area, see Table 3-2. Also, the maximum U-values for the different structural elements are given in this Energy Saving Ordinance, see Table 3-3 below.

Table 3-3. Maximal U-Value for structural elements after the German Energy Saving Ordinance from 2007

U-value for envelope walls ≤ 0.45 [W/m2K] U-value for envelope windows/doors ≤ 1.70 [W/m2K] U-value for sloped roofs ≤ 0.30 [W/m2K] U-value for flat roofs ≤ 0.25 [W/m2K] U-value for floors ≤ 0.50 [W/m2K]

In Germany the term “7 liter house” is used for low energy houses. A “7 liter house” is a building which uses no more than 7 liters of oil for space heating each square meter annually. Regarding the efficiency of the heating system, this it is equivalent to annual specific heat consumption from 50 kWh/m2.

Sweden has no standard for low energy houses, instead the Swedish Energy Agency (Energimyndigheten, 2007) has defined a “requirement specification” for low energy houses. The version from March 2008 has no significant variation of the requirement specification for passive houses. The only difference is that the space heating peak load can be additionally higher. Instead of max 10 W/m2 in the south and 14 W/m2 for the climate zone north, the space heating peak load can go up to 15 W/m2 in the south and 19 W/m2 in the north. The general description of the requirement specification for passive houses can be found in the next section.


31

3.5.4 Passive houses

According to Schnieders and Hermelinken (2006) a passive house is a building that manages to keep comfortable indoor climate even through the heating season without needing a conventional heat distribution system. Dr. Feist founder of the passive house Institute in Germany defined the term of Passive house in as:

“A Passive House is a building, for which thermal comfort (ISO 7730) can be achieved solely by post heating or post cooling of the fresh air mass, which is required to fulfil sufficient indoor air quality conditions (DIN 1946) - without a need for recalculated air" (Feist, 2006b).

According to (Feist et al., 2005), the Passive House concept can save more than 50% of the total primary energy consumption (for heating, domestic hot water, ventilation and all electrical appliances within the house). The space heating can even be reduced by a factor of five.

The idea of the passive house standard was developed from the low energy houses concept. The standard was proposed in 1988 by Professor Bo Adamson, Lund University, Sweden and Dr Wolfgang Feist Germany. The concept was developed by a number of research groups supported financially by the German state of Hessen and the final prototype was built in 1990 in Darmstadt (Feist, 2006a), Figure 3-8.

Figure 3-8. The first passive house in Kranichstein Darmstadt, Germany (Feist, 2006a)


32

Buildings with the Passive house standard have been spreading across Germany, Austria and Switzerland in the past years. In Sweden the first passive house was built in Lindås outside Gothenburg in 2001 (Wall, 2006).

The main idea behind the passive house concept is to create a building with a very good thermal performance of the envelope to a level that a heating system can be kept very simple. The main aspects which have to be considered when designing a passive house are:

- good thermal insulation,

- avoiding thermal bridges,

- good air tightness,

- ventilation with high heat recovery,

- comfort windows ( low U-values),

- innovative heat technology.

The Passive house Institute defined the passive house standard for Germany and it is also used for the central European climate. The space heating peak effect should not exceed 10 W/m2 in the living area in order to keep the annual heating requirement at less than 15 kWh/(m2 a). The Volume related air leakage at 50 Pa is limited to 0.6 times the house volume per hour. Furthermore, the combined primary energy consumption of the living area may not exceed 120 kWh/(m2 a) for heat, hot water and household electricity (Feist et al., 2005). This means that the total energy consumption of a passive house in central Europe is less than the average required energy consumption for space and hot water heating of a new Swedish building.

The Swedish Energy Agency defined in 2007 a “requirement specification” for passive houses in Sweden. This specification for passive houses is based on the German standard for passive houses but adapted to Sweden in a north and a south climate zone. The space heating peak load should not exceed 10 W/m2 in the south and 14 W/m2 for the climate zone north. If the building area is less than 200 m2, 2 W/m2 can be added to these figures. The annual heating requirement should be less than 45 kWh/(m2 a) for the southern zone and 55 kWh/(m2 a) for the northern zone. For buildings with less than 200 m2, 10kWh/m2 can be added to the above requirements. The air leakage should be less than 0.3 l/(sm2) and the windows should have a maximum U-value of 0.9


33

W/(m2K). The energy consumption of the building should be measured monthly.

To achieve the German passive house standard certain values of components and construction parts are recommended.

Table 3-4. Recommended values for components and construction parts to reach the passive house standard, after Feist at al. (2005)

Isolation of opaque envelope U value <0,15 [W/m2K] Thermal bridge free construction, i.e. Linear thermal transmittance, ψe

< 0.01 [W/(m K)]

Glazing with low U-value an high g-value, i.e. Thermal transmittance Ug Total solar energy transmittance, g

<0.8 [W/(m2K)] >50 [%]

Window, thermal bridge fee construction, insulated frame, Uw

<0.8 [W/(m2K]

Heat recovery with Net efficiency ηHe Heat loss through casing Internal external leakages

> 75 [%] < 5 [W/K] < 3 [%]

Electric energy demand for ventilation (including control), pel < 0,45 [ W/(m3/h)] Energy efficient electric appliances (e.g. highest EU appliance energy label class)

Class A

Recommended limit for primary energy use for household electricity, PE <55 [kWh/(m2a)]

Only high quality insulation materials can be used if the required U-values for the building envelope parts (walls, roof and floor) are to be fulfilled.

3.6 Energy calculations

Energy calculation programs that are used to estimate the energy consumptions can be divided in two main groups; steady state and dynamic programes.

3.6.1 Steady state calculations

A steady state energy calculation program works in principle like a hand calculation. All incoming and outgoing energy flows for the building are considered in this balance calculation.


34

Figure 3-9. Incoming and outgoing energy flows

The energy demand including space heating, domestic hot water and electricity for mechanical systems of a building can be formulated as:

hgehgelhwlvtenergy QQQQQQQQ −−++++= (3.1)

energyQ = energy demand including space heating demand, domestic hot water demand and electricity for mechanical systems (such as fans and pumps). Household electricity is excluded

tQ = transmission losses trough the building envelope including thermal bridges

vQ = ventilation losses

lQ = air leakage

hwQ = energy use for domestic hot water

elQ = energy used for mechanical systems (fans and pumps)

eheQ = energy gains from heat exchanger, heat pumps, solar collectors etc.

heQ = energy in the form of heat gains and losses from household appliances, persons, solar radiation, waste of warm water etc.

Transmission

Transmission to the ground

Solar radiation

Air leakage

Solar collectors

Waste water

Ventilation losses

Heat system

Transmission

Transmission to the ground

Solar radiation

Air leakage

Solar collectors

Waste water

Ventilation losses

Heat system


35

In steady state energy calculations it is assumed that the indoor and outdoor temperatures are constant over a certain time period. The time period can vary from 24 h up to a month if for example the average monthly temperature is used as input for the calculation. The degree day method is such a method. It does not take the temperature changes over a certain time period into account since the average temperature is used for the calculations, (Al-Homoud, 2001). Also all free energy gains such as solar radiation are usually not fully taken into consideration in the energy balance.

3.6.2 Dynamic calculation

Dynamic Programes for energy calculations make it possible to model with the help of a realistic thermodynamic model the energy use considering the requirements and indoor temperatures. Usually the heat balance is calculated in shorter time intervals like every hour but also minute interval simulations are possible (Sijpheer et al., 2005). Most dynamic calculation programes focus on the energy consumption for heating and cooling. The energy balance takes into account solar radiation, shading, heat exchange between structural material and internal environment, ventilation and air flow, heat recovery, process energy, transmission, air leakage, heat pump effects, etc. The accuracy of an energy calculation program depends on the accuracy of the input data and the assumption made by the model. Therefore, a major part of the inaccuracy of the results is due to the use of the energy calculation program rather than the analysis methods themselves (Al-Homoud, 2001).

If the purpose of the energy analysis is to study trends or compare construction alternatives then the simplified steady state method may be appropriate. But if a more detailed energy analysis of buildings is required a more comprehensive dynamic tool will be required (Al-Homoud, 2001).

3.6.3 Parameters which influence the energy consumption

The main parameters which influence the energy consumption of a building are the properties of the building envelope such as U-value, thermal bridges, air leakage and transmission, but also the solar radiation, shading, ventilation and air flow, process energy and indoor climate.

Other influencing factors are occupancy behaviour, lightning and equipment loads which are difficult to estimate and can cause variations of 10-30% between estimates and measured consumptions. The parameters in the previous paragraph can cause variations between 10-15% (Mason, 2003).


36

The window percentage influences the energy consumption strongly and today’s modern office buildings designed by architects are often highly glazed to be airy and transparent to give more access to daylight. According to Poirazis et al. (2008) is it likely that office buildings with fully glazed façades have a higher energy consumption for heating and cooling compared to buildings with conventional façades.

The energy used by air conditioning systems, including heating, cooling and distribution of the ventilated air, constitutes a significant part of the total energy consumption of a building (Heikkilä, 2007). The performance of essential building services and systems need to be optimized in order to improve energy conservation over the life cycle of a building.

The indoor climate is a further factor which influences energy consumption. The allowed range of variations in the air temperature affects the energy consumption since a small range requires more cooling in summer and more heating to reach the required temperature in winter. If the temperature range is wider i.e. the temperature is allowed to rise more during the cooling period and to drop more during the heating period, the building’s thermal inertia will thus utilised.

3.7 Conclusion

The combination of the integrated life cycle design aspects given by Sarja (2002), the sustainable aspects in Kohler (1999) including the life cycle cost aspects from ISO draft 15686-5 (2007) results in the picture shown in Figure 3-10.

The most dominant aspect with regard to cost and environmental issues is the operating energy. Junnila (2004) showed in his study that the operating electricity causes most of the environmental impact during the life cycle of an office building, but the operating heat and maintenance also have a significant impact on the environment. The statistics from the Swedish construction industry (2007) shows that one of the major components of the LCC during the operating time of a building is the energy consumption.

Today, data for performing complete life cycle costing is rather limited. All-Hajj and Horner (1998) proposed a Building Maintenance Cost Information Service (BMCIS) framework where only 11 cost elements are needed to predict the total running cost. Among the 11 selected significant cost items


37

were gas, electricity and fuel oil. This also shows that the operating energy cost is a significant factor in estimating the LCC of a building.

By optimising design parameters such as building shape, envelope and orientation on site, the heat consumption can be reduced up to 80% (Feist et al., 2005). According to Clarke (2001) energy simulations for buildings give a better and quicker design process, and the outcomes will better match society’s aspirations for sustainable practices, environmental protection and improvement regarding climate change. However, Neto and Fiorelli pointed out that occupant’s behaviour in a building where the air conditioning equipment are mainly unitary systems can significantly affect the energy consumption and make its forecasting more difficult or inaccurate. Nevertheless, using a calibrated energy simulation program forecasting the energy consumption can give opportunities for reducing the energy consumption of a building (Neto and Fiorelli, 2007).


38

Life Cycle Design(Sustainable Design)

Figure 3-10. Life cycle design aspects

The design process

39

4 THE DESIGN PROCESS

This Chapter describes the theoretical background and the results of the EU-Project InPro that has been used in the thesis.

4.1 Introduction

The gross volume of construction projects is based on outdoor craft based manufacturing and assembly process on the construction site, where different organisations plan and execute their work using document-based information, produced by functional-oriented organisations (Olofsson et al., 2007). The design process is mainly sequential (Harris and McCaffer, 2001), where the different phases are often separated in time and space.

The manufacturing industry changed radically in the past century, with a transition from craft production via mass production to lean production. During the 1970s, the product design phase in the manufacturing industry was followed by testing of physical prototypes. The introduction of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) systems in the 1980s increased the speed of the design and documentation work, but did not radically change the product development process. A major paradigm shift took place in the nineties when the simultaneous product development process, as introduced by the Japanese car industry, was spread over the world. In a benchmarking study of major automotive manufacturers this technique proved not only to be faster, but also appeared to require fewer engineering hours and to result in products better adapted to the production process, which in turn resulted in better quality of the end product(Womack, 1990). The change of the product development process from a sequential chain of activities to making things more concurrently also reduced the time for marketing new models. This transformation has taken place at the same time as the modelling methods have become increasingly sophisticated, Figure 4-1.

An Integrated Concurrent Engineering process (ICE) requires an even tighter integration among different design disciplines. Co-located multidisciplinary teams working on the same data have inspired the CAD developers to engineer product model servers, where designers work in a common model environment. Today, a product can be designed, tested and validated before the first physical prototype is built (Olofsson et al, 2006).


40

Process

70 80 90 00 10

ICT

ComputerAidedDrawing

SequentialEngineering

2Ddrawing

3Dmodels

DigitalMock-up

ProductModels

SemanticModels?

SemiparallellEngineering

ConcurrentEngineering

IntegratedConcurrentEngineering

KnowledgeEngineering?

Figure 4-1. Schematic picture of the evolution of the product development process and modelling methods in the manufacturing industry, (from Olofsson et al.,2007).

4.1.1 Concurrent engineering

Concurrent engineering (CE) or simultaneous development is primarily known from the manufacturing industry. Toyota’s product developing process during the 1980s is recognized as the origin of concurrent engineering (Womack, 1990). The main idea behind concurrent engineering is that a number of sequential activities are coordinated and performed in the same time by interdisciplinary teams which bring multidimensional knowledge to the project.

CE requires information sharing and tight coordination of the different design teams. The first practical use of virtual reality (VR) was introduced when “digital mock-ups” started to be used in the design process. Digital mock-ups are VR models of the product, assembled from the different design teams’ 3D CAD models (mostly sharing of geometrical data). Digital mock-ups are useful for coordinating design and for communication of design intents to stakeholders (Woksepp, 2005). According to Woksepp (2007), individual

The design process

41

stakes and risks for each stakeholder need to be replaced by common project goals and sharing of benefits and risks if CE is going to be successful.

There are many definitions of the meaning of concurrent engineering. ESA, the European Space Agency, defines CE as (ESA-CDF, 2004):

“Concurrent Engineering is a systematic approach to integrated product development that emphasises the response to customer expectations. It embodies team values of co-operation, trust and sharing in such a manner that decision-making is by consensus, involving all perspectives in parallel, from the beginning of the product life-cycle”.

CE is based on five key elements, (1) a concurrent process where (2) multidisciplinary teams develop (3) an integrated design model supported by (4) a software infrastructure using (5) environments for collaborative work.

In construction the software infrastructure is believed to be constituted by Building Information Models, (BIM).

4.1.2 Building Information Models and Virtual Design and Construction

Building Information Modelling, BIM, describes the process of creating, storing and using building information in a common Building Information Model, (BIM). The noun BIM stands for a static representation of a building that contains multidisciplinary data that defines the building from the point of view of more than one discipline.

BIM operates on digital databases which enable capturing, managing, and presenting data in an appropriate way for each discipline. Such downstream applications start with capturing and managing the required information, and present that information back in appropriate way, and make it available for use and reuse during the project (Ibrahim and Krawczyk, 2003). Laiserin (2007) defines BIM as a process of support communication (sharing data), collaboration (acting on shared data), simulation (using data for prediction), and optimisation (using feedback to improve design, documentation and delivery). This definition makes no reference to any software at all, but software can automate and improve that process.

Virtual Design and Construction, VDC, is another acronym used to describe the model-based technology and working methods (Kunz and Fischer, 2008). According to Kunz and Fisher (2008) three levels of implementation of VDC methods in the building sector can be recognized:


42

- Visualisation: 3D models are routinely created and used to predict performance metrics. Especially, gains in clarification of project objectives for stakeholders and resolving of coordination issues among different design disciplines can justify the relative inexpensive investments made in the project.

- Integration: Projects develop computer based methods to exchange data among different modelling and analysis applications either using standard formats such as IFC (International Foundation Classes) or propriety formats. For integration to work well vendors need to agree on exchange formats. The implementation costs in the integration phase are more expensive compared to the visualisation phase and cannot be justified on project level. Therefore the benefits need to be derived on company level over several projects.

- Automation: Routine design tasks or manufacturing of assemblies (CNC -Computer Numeric Control) for on-site installation are automated. Enables a dramatic increase in design efficiency and effectiveness and dramatic decrease in construction duration. The automation phase needs more long term strategic partnership since the implementation costs are high and need to be depreciated over several projects.

For VDC to work well contracts need incentives to encourage sharing of information between stakeholders in the projects (ibid).

4.1.3 Model based design

Several research projects and national programs have been launched in the past years in Europe in order to develop model based design guidelines and exchange strategies of BIM data (InPro 2009, BIPS 2007, Senate 2007). A common approach is that the degree of detailing, the information level or model maturity is increasing through a number of phases from the early stages to detailed design and construction where the required functionalities are gradually are mapped onto technical solutions.

In the InPro approach the information level is synchronized with the decision making process over a building’s life cycle. The decisions to be made at a certain gate, called quality gate, require a specific level of information so that the performance of the proposed design solution can be evaluated. InPro defines the collected project information as open information environment (OIE) and the software infrastructure for collaborative work as the open

The design process

43

information platform, (OIP). The OIP contains in general a mix of documents and models. The content of the OIP is project specific. Change management procedures are applied on approved levels of information maturity. InPro defines by default 8 lifecycle maturity levels to guide the project management using the InPro life cycle design framework where levels 0 – 3 are part of the InPro early design phase.

The default InPro OIP maturity levels are shown in Table 4-1.

Table 4-1. InPro default maturity levels, (InPro 2008)

Level Caption Information 0 Goals The business case is identified and the overall goals,

timeframe, budget and possible locations of the building project are formulated. Also, space program and specific requirements from authorities, client and end-users can be stipulated. Normally, no building information model is available at this level

1 Conceptual Alternatives regarding building envelope and placement on the premises are selected with regard to constraints given by authorities, the client and geotechnical conditions. Also, gross areas for different functions, room types and relations between functions and scenarios of alternative uses of the building in the future are developed

2 Functional Gross areas are further detailed into functional spaces in the building (e.g. location of office rooms, meeting rooms, fire compartments, etc.). Location of installation shafts and main structural parts are defined.

3 System Design of main structural and installation systems. The building program for the approval process from local authorities is developed.

4 Detailed Detailing of structural, installation and finishing design and planning information needed for the realisation stage.

5 As built The OIP level 4 is updated with information from the realisation phase of the building.

6 Operation Operational stage where the OIP is updated for use with information from facility management in Computer Aided Facility management, CAFM, systems. If during operation the facility is rebuilt, the OIP maturity levels are repeated starting with as built condition.

7 Demolition Planning for the demolition and recycling of building parts The default mapping of OIP maturity levels versus the building lifecycle phases and quality gates is shown in Figure 4-2.


44

Different types of discipline models will be developed during the design phase and aggregated. The approved aggregate model in a specific maturity level can also act as a master model at the next level of maturity, see Figure 4-3. The master model contains mainly the geometrical requirements for each discipline (design space) e.g. rooms, structures and ducts and can improve the consistency among the different design disciplines and reduce clashes during each stage/gate concurrent engineering session.

Feasibilitydesign

Building design

Businessplanning OperationDetailed design

and realisation RIP

Phas

es

1st Level

OIP

mat

urity 3rd Level 4th Level 5th Level

Maturitylevels

Strategic

Buildingapproval

First contactwith client

Early design

2nd Level

Tactical Operational briefing

Act

ors

Brie

fing

Contractsignature

0th Level

Handover

ClientArchitectEngineer

ContractorFM spec.

ClientArchitectEngineer

ContractorFM spec.

Conceptual Functional System Detailed As built

6th Level

for support of CAFM systems

OperationalGoals

Figure 4-2. Default mapping of OIP maturity levels versus building life cycle phases, from (InPro-D17, 2009).

Each design discipline will be responsible for its model. The aggregated model is the responsibility of the project management as data sharing and model coordination are the most important aspects of concurrent engineering. The model aggregation is proposed to be handled by an appointed project information manager who is also responsible for the quality assurance of the OIP.

The design workflow is coordinated among the involved speciality design/analysis disciplines using OIP maturity levels and quality gates for decision making. The next figure, Figure 4-4, shows the proposed workflow for the design process between two quality gates. It starts from an approved level of maturity set by the previous quality gate m and ends with the decision making at quality gate n. The workflow consists of requirement management, developing a design strategy, the design and analysis work, model coordination and model quality assurance and a decision making gate at which the best alternative is selected.

The design process

45

QG QG

Architectural model Structural model HVAC model Discipline model n

Aggregated model

Discipline models

Master modelfor next stage/gate

Maturity level n

Maturity level m

Figure 4-3. The principal use of different types of models, adapted after BIPS(2007), from InPro (2009).

Info

rmat

ion

man

agem

ent

Ope

n In

form

atio

n P

latfo

rm

Spe

cial

ity

desi

gner

/ana

lyst

Dev

elop

men

t tea

mC

lient

Mat

urity

le

vel m

Mat

urity

le

vel nAl

t 1

Alt

2

Alt

nn

Figure 4-4. Generic workflow process between two quality gates, InPro (2008)


46

4.1.4 Early design

Studies by the National Research Council (1991) of large scale projects estimate that 80-90% of the life cycle design costs are determined in the first 10-20% of the design phase. The idea behind concurrent design is to incorporate the consideration in the downstream product development phases, including manual fracturing, assembly, and maintenance, into the early design phase for achieving better product life cycle performance.

According to Ryd (2008), the stakeholders in the building process have different interpretations of the term “early phase”. For a client the “early phase” starts when a business opportunity or a societal demand arises. The initiation of a building project often includes a business planning phase for the client where goals, budget, timeframe and organisation are determined before other stakeholders from the AEC (Architectural, Engineering and Construction) sector are involved. Since the business planning phase often constitutes the boundaries or the framework for the construction project this phase has been seen as the starting point of the early design.

The InPro definition of the early design phase contains besides business planning, a feasibility design and a building design phase, see Figure 4-2. The early design phase ends when the detailed design for the realisation of the building begins. During the feasibility design phase, the client and the design team translate the client’s project goals into values and design requirements expressed in performance language. In this phase it often becomes clear that different design requirements can influence one another or even be conflicting. During the building design phase the functional design is further detailed and the main structural and installation systems are selected. Often the building design phase ends by formulating a building program for the approval of the project from local authorities.

Experimental study of Senate Properties Headquarters

47

5 EXPERIMENTAL STUDY OF SENATE PROPERTIES HEADQUARTERS

This Chapter describes the experimental design for the energy and LCC analyses of the Senate Properties Headquarters. It gives also a brief motivation of and introduction to the studied building and the used analysing tool. Further, how the LCC experiment is set up is explained in this Chapter.

5.1 Energy analysis at different levels of maturity

InPro (2009) has proposed a strategy for energy performance analyses regarding energy consumption and indoor climate simulation to guide the design in a more energy efficient direction in the early design phase. The proposal was related to the different stages of design and the level of information maturity available, see Figure 5-1.

Energy and indoor climate simulation for verification of final designDetailedDynamic

Energy and indoor climate simulation for alternative space layouts and HVAC systems solutions and calculation of energy and indoor climate KPISystemDynamic

Energy simulation of alternative building envelope, orientation and energy supplyFunctionalDynamic

Energy estimation for determination of energy supply and energy performance requirementsConceptStatic

PurposeLevelMethod

OIP maturity4

3

2

1

0

Early Design

Feasibility design phase

Energy and indoor climate simulation for verification of final designDetailedDynamic

Energy and indoor climate simulation for alternative space layouts and HVAC systems solutions and calculation of energy and indoor climate KPISystemDynamic

Energy simulation of alternative building envelope, orientation and energy supplyFunctionalDynamic

Energy estimation for determination of energy supply and energy performance requirementsConceptStatic

PurposeLevelMethod

OIP maturity4

3

2

1

0

Early Design

Feasibility design phase

Building design phase

Detailed design and realisationphase

Figure 5-1. Strategy for energy performance analysis in the early design phase, after InPro (2009).


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At the conceptual maturity level, design alternatives regarding building envelope and placement on the premises are studied with regard to constraints given by authorities and geotechnical conditions. Also, gross areas for different functions, room types and relations among functions and alternative uses of the building in the future are developed. Since only information regarding gross areas, number of floors, location and building type is known; simple estimates based on static methods are proposed for the purpose of checking of requirements and decision support for the selection of energy supply.

At the next level of maturity, the functional level, energy analysis can be performed to evaluate design alternatives regarding building structure, heat transfer coefficients of window and wall areas and the effect of the selected indoor climate.

At the system maturity level, more detailed design alternatives concern structural design and installations. At this stage the main structural design and installation systems are determined. Also, the indoor climate on room level can be simulated and design values for the detailing of structural and installation system can be determined.

In the detailed design and realisation phase the analyses of energy performance and indoor climate simulation are made for verification of the final design.

This study focuses on the early design phase. The three first levels of maturity; conceptual, functional and system levels are included, see Figure 5-2

10% window area

Conceptual level Functional level System level

Space distribution and use

Figure 5-2. The different maturity levels of the early design phase


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5.2 The Senate Properties Headquarters

The aim of this research is to investigate the use of energy simulations at different levels of maturity in the early design phase. Therefore an existing building with known energy consumption was selected in the study. The choice of using the Senate Properties Headquarters in this study was supported by the fact that a 3D model of this building exists, which would facilitate the energy simulation with programs where a 3D model is needed. Further the fact that the Senate Headquarters building has a building automation system (BAS) installed made it possible to follow up the actual use of energy.

Figure 5-3. Senate Properties Headquarters, inner courtyard to the left and the Senate Properties Headquarters from the street side with the former grain silos to the right, Jussi Tiainen, (2003)

The case study object shown in Figure 5-3 is the Senate Properties Headquarters in Helsinki. Senate Properties is a government owned enterprise under the aegis of the Finnish Ministry of Finance and is responsible for managing the Finnish State's property assets and for letting premises. Senate Properties provides services related to premises, primarily to customers which form part of the state administration. The services include leasing premises, investments, and the administration and development of the property portfolio. As a business enterprise, Senate Properties finances its own operations and is not dependent on the state budget. The building stock comprises universities, offices, research, culture and other facilities.

The Senate Properties Headquarters are used as an office building where the spaces are mostly open offices including conference rooms and a canteen. The building is a former grain mill from 1934 with huge storage silos. The building


50

is L- shaped and consists of 9 floors, ventilation rooms and basement. The building was renovated in 2002 and the old structures are maintained as much as possible. For example: elevators are placed in the old grain silos. The building has a gross area of 11 082 m2 and net floor area of 10 263 m2, the floors are mainly open office. The building has a window area of 15% to the gross net area. A new steel structure was made for the double glass façade of the open offices. District heating is used as the energy-heating source. The building’s HVAC equipment is a heating and cooling system with air conditioning and building automation systems. In the energy certification for the building the energy consumption is declared, see Table 5-1.

Table 5-1. Energy consumption for the Senate Properties Headquarters

Total energy consumption 2 613 180kWh per year (224 kWh/(m2 a)) Heating energy consumption 1 400 000 kWh per year (123 kWh/(m2 a)) Cooling energy consumption 721 100 kWh per year (61 kWh/(m2 a)) Heating and cooling 2121100 kWh per year (2061) kWh/(m2 a)) 1) This value is based on net floor area according to the Swedish standard BBR Since the Swedish Standard (BBR, 2008) is used in the study only the heating and cooling energy consumption is considered. This gives a total energy consumption of 2 121 100 kWh per year or 206 kWh/(m2 a).

In the Finnish energy certification the energy performance rating is based on the gross building area not the net gross area as in Sweden. The energy certification of the Senate Properties Headquarters is E, according to the Finnish certification system, which means that the energy consumption is between 177 kWh/(m2 a) and 230 kWh/(m2 a). The certification is based on the consumption measured in 2007. The consumption is rather high and it has been suggested that the operating and ventilation schedule could be optimised in order to reduce the total energy consumption. The building has a building automation system (BAS) installed which makes it possible to follow up the actual use of energy.


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Figure 5-4. To the left, the Senate Properties Headquarters from the street side with the former grain silos as a 3D model; to the right, a 3D model of the building from the inner courtyard.

5.3 Design of the Senate Properties Headquarters simulation study

The InPro life cycle design process proposes a framework where design alternatives at specific stages in the design and maturity level are evaluated. For that reason it was decided to investigate the influence on the energy consumption of different options or alternative designs of the same building. Alternatives requirements on “indoor climate”, “building envelope” and “window area” are major design parameters which can influence the energy consumption significantly. Therefore, a study with these three design parameters was performed. The idea is to investigate how energy performance analysis can be used in the early design phase to estimate the effect of these design parameters on the energy consumption.

Each of the three design parameters “indoor climate”, “window area” and “building envelope” was divided into three alternatives. The combination of all design parameters gives a total of 27 cases to analyse, plus the analysis of the original building. These 28 cases were analysed at the different maturity levels of the early design phase using three different energy analysis programs, see Figure 5-5.


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Figure 5-5. Design alternatives by combining the different parameters

Indoor climate

The indoor climate is divided into three categories according to the ISO 7730 – Ergonomics of thermal environment (ISO, 2005). Only the indoor temperature for the different categories is considered and other factors like air velocity are excluded. For an office building, the three categories A to C divide the indoor temperatures and air velocities into a summer and a winter season. All three categories require an average room temperature during the summer of 24,5°C and during the winter 22 °C. The temperature is allowed to vary from the average more in class B and C compared to class A. Table 5-2 summarizes the ISO 7730 requirements.

Table 5-2. Indoor climate requirements for office building, after ISO 7730 (2005)

Operative Temperature °C

Max. middle air velocity m/s Building-/room

types CategoriesSummer Winter Summer Winter

A 24.5 ±1.0 22 ±1.0 0.12 0.1 B 24.5 ±1.5 22 ±2.0 0.19 0.16 Office, conference-

rooms, classrooms C 24.5 ±2.5 22 ±3.0 0.24 0.21

Building envelope

Alternative building envelopes are used according to recommended U-values for passive houses, low energy houses and standard houses. Only the U-value


53

for windows, walls, roof and base plate for the different building envelopes are considered in the study and not all the requirements such as ventilation system, total energy consumption and heat recovery. The passive house building envelope’s U-values were chosen after the German Passive house standard recommendation which is suitable for central Europe and could therefore be used for the south of Finland. The U-values for the low energy envelope were selected according to the maximal U-value for structural elements after the German Energy Saving Ordinance from 2007, Table 3-3.The chosen standard house building envelope fulfils the required minimum standard for buildings in the Southern zone of Sweden.

Table 5-3: U-values for the different building envelopes

Type Property Wall Silo Window Door Ground Roof Standard building

U-value [W/m2 °C] 0.28 0.3 1.8 1 0.32 0.18

Low energy building

U-value [W/m2 °C] 0.24 0.23 1.2 0,63 0.17 0.12

Passive house building

U-value [W/m2 °C] 0.098 0.12 1.0 0,64 0.1 0.06

Window area

The window area was chosen in relation to the gross net floor area in percentage. That means a gross net floor area of 10 263 m2 a 10% window area is 1026 m2, at 20% the window area is 2052 m2 and at 40% the window area is 4105 m2. As shown in the Figure 5-7 a 40% window in relation to the gross net floor area for the Senate properties is almost equal to glass façades, only the silos are not affected.

Figure 5-6. The left picture shows the original Senate Properties Headquarters

and the right shows the Senate Properties Headquarters with a 10% window area.


54

Figure 5-7. The left picture shows the Senate Properties Headquarters with 20% window area and the right shows the Senate Properties Headquarters with a 40% window area.

5.4 Energy analysis software

Today different energy analysis softwares are commercially available on the market. This study is focused on making energy analysis depending on the information maturity level. The table below, Table 5-4, shows the different programs used in the study categorized after maturity level where they can be applied. The choice of the programs at the first maturity level was due to the possibility of the programs to make energy estimations with the provided limited input data. The calculation programs RIUSKA and VIP+ can be used at different maturity levels depending on how defined the input data is. The choice of using RIUSKA at the system level was due to the fact that RIUSKA is able to dimension the HVAC systems on the room level. However VIP+ is not intended to be used for dimensioning heating- or cooling systems. But the purpose of this study was to explore and understand the different programs, and that’s why VIP+ was chosen for the functional level.

Table 5-4. Used Programs at the different Maturity levels

Maturity Level

Level I conceptual

Level II functional

Level III system

Method Static (steady state) Dynamic Dynamic Dynamic Dynamic Dynamic

Possible programs

Excel sheet (Hand

Calculation) VIPWEB VIP+ RIUSKA VIP+ RIUSKA

Used in the study

Excel sheet (Hand calculation) VIPWEB VIP+ RIUSKA


55

Excel “hand calculation”

The “hand calculation” is represented by a self-made Excel estimation sheet which is based on a steady state analysis of the energy balance. This type of analysis does not consider, e.g. sun and wind factors and energy consumption for active cooling during the summer period. The in and outgoing energy flows that are accounted for in the energy balance are listed in Table 5-5.

Table 5-5: Accounted in- and outgoing energy flows in the energy balance.

Outgoing energy flows In going energy flows Transmission Person energy Air leakage Heat gains from hot water Ventilation losses Heat gains from process energy Passive cooling Heat gains from electricity use Energy demand for domestic hot water Energy demand for heating

Some factors are accounted for or assumed in the Excel calculation sheet; U-value, cold bridges, energy demand for domestic hot water per person, heat grains from process, electricity and hot water and person energy. Others need to be filled in by the user. Climate data from one region is implemented (Helsinki region) and the energy balance is based on monthly average temperatures.

The input data which has to be specified in the Excel calculations is shown for the case of the Senate Properties Headquarters (Table 5-6).


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Table 5-6. Input data for the Excel analysis of the Senate Properties Headquarters building in Helsinki

Input data Options

Net gross area [m2] Ceiling high [m]

Foot point shape chose Square / rectangular / L-shape Window area

(to the net gross area) chose 10% / 15% / 20% /40%

Number of floors number Building type chose Passive house / Low energy / Standard

Indoor temperature max chose 25.5C° / 26C° / 27C° Indoor temperature min chose 21C°/ 20C° / 19C° Number of occupancies number

Building use chose Office / dwelling Heat recovery chose 50% / 70% / 80%

VIP-Energy analysis software

The VIP-Energy from StruSoft AB (2009) is intended for calculating the energy consumption in a building during a period of one year but shorter periods can also be analysed. The program is built around a dynamic calculating model, with a time resolution of one hour. The energy flow is calculated with consideration taken to climate factors such as temperature, sun, humidity and wind. Varying demands on room temperature and air changes control the calculation.

VIP-Energy is constructed for calculating yearly energy consumptions. The program is not intended to be used for dimensioning heating- or cooling systems (Strusoft, 2008).

Figure 5-8 shows the various energy flows that are accounted for in the VIP energy calculation engine. VIP-Energy is distributed in two software packages, VIPWEB and VIP+. Both VIPWEB and VIP+ use the VIP-Energy calculation engine.

VIPWEB is web-based and intended to be used in the early design phase. The user interface can be tailor-made for almost any kind of use, but is normally built for the non expert reducing the amount of input data needed.


57

Figure 5-8. Energy flows in the VIP-Energy calculation engine, StruSoft (2009).

The version used in the case study requires climate data given by the location of cities, wind exposure of the building and according to what standard the calculation should be made. The building data are the type of envelope, main shape, one or multi-storey house. The window area has to be defined as normal, big or glass façade. Geometrical data include net gross area, number of floors and ceiling height, basement or foundation slab. New windows and extra isolation have to be filled in. The installation data are the type of ventilation and heat recovery system, indoor maximum and minimum temperature and what kind of activity the building is used for. An example of input data is given in Appendix A.


58

For more detailed energy analyses VIP+ is used, giving the user more control of the input data. Input data such as geographic location and building orientation, building materials and building parts, operating schedules, HVAC equipment and controls, utility rate schedule, energy costs etc, are given in dialogs. Multiple zones can be calculated. Climate files can be imported from Meteonorm and other sources. Input data can also be imported from Archicad files. Examples of input needed for VIP+ can be found in Appendix A.

RIUSKA

RIUSKA is an energy simulation program developed by Granlund (2003) which uses the DOE-2.1E as a calculation engine. The DOE- 2.1 engine was developed by the Lawrence Berkeley National Laboratory in the US and is often used by thermal simulation programs (Maile et al., 2007). DOE2.1E uses a weighting factor method for solving the thermal balance equation of the building on an hourly basis. RIUSKA imports the building geometry through IFC import. RIUSKA has been officially certified by the IAI to comply with the IFC 2x standards. The 3D model of the building needs to be created in IFC compatible modelling software or in SMOG, an object-oriented 3D modelling software developed by Granlund (Jokela et al., 1997). It is important to define construction and space types in the CAD program to facilitate the work in RIUSKA. Geometrical changes of window areas etc, need to made in the CAD model since there is no possibility to change that in RIUSKA.

Errors in the import of IFC files to RIUSKA occur if defined spaces are under one m2 or if spaces are doubly defined. In the latter case the outside wall disappears. Further, spaces that span over two floors can be problematic and overlapping spaces within a building storey that are on top of each other can cause errors that must be detected by the user. These and other problems in the import of IFC files into RIUSKA have also been reported in the literature (Maile et al., 2007).


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Figure 5-9. Link between IFC compliant CAD programs and RIUSKA.

After importing the building geometry into RIUSKA material types and thickness, U-value, window types can be and often must be adjusted in RIUSKA. The program does not calculate cold bridges, this should be included by the user in the U-value. RIUSKA provides a database for thermal space parameters and these can be modified by the user. RIUSKA provides four different types of HVAC systems, other types can not be modelled in this program.

RIUSKA can be used for:

- Building simulation of annual energy consumption for the whole building or for groups of individual spaces.

- System simulations when comparing and dimensioning HVAC systems.

RIUSKA is used both in the preliminary design in comparison between different alternatives, the system design for dimensioning of HVAC systems, but also in the operational phase comparing monthly energy consumption figures from building automation systems are simulated.

RIUSKA has the advantage that the geometry can be imported via IFC files. Still construction types, HVAC system, occupancy hours and thermal spaces need to be adjusted or selected by the user. Input data for RIUSKA simulations can be found in Appendix A.

MagiCAD-3D model

IFC

RIUSKA


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5.5 LCC analysis

As mentioned in Chapter 3.2, the most suitable economic evaluation method for LCC in the construction industry is the net present value method. This method takes the discount factor into account and is usable if the different alternatives have the same life span. The net present value is the projected result, based on a required rate of return of each annual projected cash flow, discounted to the present value. As LCC focuses on cost rather than income, the usual practice is to treat cost as positive and incomes as negative. The best choice between competing alternatives will then be the one with the minimum net present value.

The equation for the net present value (NPV) is:

∑= +

=N

tt

t

rCNPV

0 )1( (4.1)

where

Ct = the net cash flow at time t

r = discount rate

N = years

t = time

This study focuses on the economic result of the investment alternatives regarding building envelope and energy consumption. Consequently, the net present value calculation only includes the initial construction cost of the building envelope alternatives and the annual cost for energy consumption. Equation 4.1 reduces to:

∑=

+=T

tit

dii ECNPV

10 (4.2)

C0i = initial construction cost for the building envelope

∑ dEit = the sum of discounted Energy cost at time t


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Time period

For the net present value analysis a time period of 30 years is chosen. The life cycle of e.g windows is often selected between 30-50 years (Baumarkt, 2009).

Discount rate

The discount rate for the annual energy cost should take into account the effects of the estimated inflation. The real rate of interest is calculated according to the Norwegian Standard (NS, 2000).

iir

r n

+−

=1

(4.3)

r = real rate of interest (in decimals)

i = rate of inflation (in decimals)

rn = nominal rate of interest (given as decimal)

According to the statistics from the European Central Bank, the interest rate for the Euro area has varied from 3% to 5.5% between 1998 and 2008, giving an average of 4.3%. A nominal interest rate of 4% has been adopted in the calculations.

According to Eurostat (2008), the inflation rate has varied between 1.2% to 3.7% over the last decade. The International Monetary Fund (2009) reports that the inflation rate has varied from 28.6% to 2.2% in the past 20 years in the European Union. An inflation rate of 3.0% seems reasonable to select. However, a prognosis over 20 years is very insecure. Inserting these figures into the equation 4.3 for the real rate of interest gives 0.98%.

%98.00098.003.01

03.004.0==

+−

=r

An alternative scenario of 5.5% interest rate and an inflation of 1.2% gives a real rate of interest of 4.2%. Considering the uncertainty of forecasting the future two discount rates are used in the study, 0.98% and 4.2%.

%2.40424.0012.01

012.0055.0==

+−

=r


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Energy prices

The energy prices in the European Union countries in 2008 are shown in Figure 5-10. The prices varied from 0.5 SEK to 2.5 SEK per kWh. An initial price of 1.2 SEK per kWh is used in this study.

Figure 5-10. Energy prices in the EU in 2007 (Goerten and Clement)

To forecast energy prices over a time period of 20 years is difficult or maybe even impossible. However, some trends can be recognised in the construction of a plausible future scenario considering the energy prices over the last years for electricity, gas and oil. According to Eurostat (2008) the price for electricity without tax in the EU 15 countries (Chapter Energy) has been increasing by approximately 10% the last decade. The price for gas including tax has increased during the same time period by 68%. The Eurostat (2008) statistics for oil within the EU15 countries for a time period from 2002-2007 shows that the prices including tax increased by 53%. In Germany, the energy cost for households has increased by 28.6% between 2004 and 2006. Hence, the energy prices are dependent on a variety of factors not easily forecast. But two scenarios, a moderate case of a 4% increase per year and a case of a high energy price increase of 12% per year can give some indication of the effect on the net present value estimations.

Investment cost

The investment costs for the different building elements in the alternative building envelopes are shown in Table 5-7 and 5-8. The figures in the tables


63

are based on Wikell’s cost recipes for different building elements and interviews with suppliers of windows and glass façades

Table 5-7. Prices and U-values for the different building envelope parts

Type Property Wall Silo Window

Standard building U-value [W/m2 °C] 0.28 0.3 1.8

Cost per m2 1841.42 SEK 2496,85 SEK 4400 SEK Low energy building U-value [W/m2 °C] 0.24 0.23 1.2

Cost per m2 1869.47 SEK 2513.65 SEK 5400 SEK Passive house building

U-value [W/m2 °C] 0.098 0.12 1.0

Cost per m2 2107.87 SEK 2614.65SEK 6400 SEK

Table 5-8. Square meter of the different envelope parts depending one the percentage of the window area

Window area in percent 10% 20% 40% Façade [m2 ] 3581.93 2555.63 502.945 Silo[m2 ] 1294.1 1294.1 1294.1 Window [m2 ] 1026.22 2052.52 4105.2

Results and analysis

65

6 RESULTS AND ANALYSIS

This Chapter presents the results and analysis of the energy performance analysis followed by the results and analysis of the LCC calculations.

6.1 Energy performance analysis

The results of the energy analysis of the different buildings are presented using the abbreviations of building envelopes and window areas as shown in Table 6-1. The different climate classes used for selecting the indoor temperatures are denoted Class A, B and C.

Table 6-1. Abbreviations used of building envelope and window area

SH-10 Standard building envelope with 10% window area

SH-20 Standard building envelope with 20% window area SH

Standard building envelope

SH-40 Standard building envelope with 40% window area

LH-10 Low energy building envelope with 10% window area

LH-20 Low energy building envelope with 20% window area LH

Low energy building envelope

LH-40 Low energy building envelope with 40% window area

PH-10 Passive house building envelope with 10% window area

PH-20 Passive house building envelope with 20% window area PH

Passive house building envelope

PH-40 Passive house building envelope with 40% window area


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6.1.1 Energy analysis at conceptual maturity level

Result from the Excel tool

The estimation at the conceptual level is based on a limited amount of input data and is meant to give an indication of the relative energy performance of the different design options, see Figure 6-1.

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

SH-10 SH-20 SH-40 LH-10 LH-20 LH-40 PH-10 PH-20 PH-40 Originalbuilding

building envelope

kWh/

(m2 a

)

Class AClass BClass C

*The actual energy consumption for the original building is 206 kWh/(m² a)

*

Figure 6-1. Results of the energy estimates using the Excel tool

The energy consumption ranges from 45 kWh/(m2 a) for a passive house envelope with a 10% window area up to 122 kWh/(m2 a) for the standard building envelope with a 40% window area. The difference between the standard building envelope and the other two types of building envelopes is significant. The effect on the energy consumption between indoor climates classes A-C is clearly seen for the standard building envelope but not as clearly for the other types of building envelopes. The original Senate Properties Headquarters shows in this calculation an energy consumption of 73 kWh/ (m2 a) which in relation to the actual energy consumption is only 36%, (207 kWh/m2 a).


67

Result from the VIPWEB tool

It is not possible to use the same input data in the Excel script and the VIPWEB tool. However, the input in the VIPWEB estimations was chosen to mimic as much as possible the input in the Excel analysis. The energy estimates from the VIPWEB analysis at the conceptual maturity level are shown in Figure 6-2.

0

10

20

30

40

50

60

70

80

90

100

SH-10 SH-20 SH-40 LH-10 LH-20 LH-40 PH-10 PH-20 PH-40 Originalbuildingbuilding envelope

kWh/

(m2 a

)



*

Figure 6-2. Results of the energy estimates using the VIPWEB

The energy consumption varies from 74 kWh/(m a) for the passive house envelope with a 10% window area and climate class C to 89 kWh/(m2 a) for the standard house envelope with a 40% window area and indoor climate class A. The energy consumption for the different envelope types varies not so much as in the Excel analysis, but the difference between the indoor climate classes is clearly recognisable in all estimates. The original Senate Properties Headquarters building shows an energy consumption of 82 kWh/(m2 a) compared with the steady state Excel calculation of 73 kWh/(m2 a). Compared with the actual energy consumption the result is only about 40% of the actual consumption for the Senate headquarter. The difference between the highest and lowest values is 15 kWh/(m2 a), a difference that might seem small. However, the difference calculated for the whole building would be 153 945 kWh per year.


68

Analysis

The results from the steady state analysis illustrated in Figure 6-1 show a significant difference in the energy consumption between the standard building envelope and the low and passive house building envelopes. Since this calculation method mainly takes into account heat losses via air leakage and transmission, the energy consumption is very dependent on the U-value for the building. The U-value is much higher for the standard building envelope than the other types of building envelopes. This effect is even more pronounced for the standard building envelope with a 40% window area since the U-value for the windows is six times higher than for the standard wall. The results also show that the steady state method does not properly take into account the different climate classes since the method is based on monthly average indoor temperatures and does not consider daily temperature variations. Also, the method does not include cooling energy consumption or gains from solar radiation.

The results from the dynamic estimation program VIPWEB illustrated in Figure 6-2 show minor effects on the energy consumption for the different building envelopes and the window area. However, the program selects the U-value for the analysis based on the chosen building type resulting in U-values very close to each other for the different cases. The U-value only varies with 0.05 between the standard building and the passive house building for the same window area. For example, the average U-value ranges from 0.33 for SH-20 to 0.29 for PH-20. Therefore the variation in the energy consumption between the different building envelopes becomes small. Also, the windows for all building envelopes had the highest standard with a U-value of one. This explains why the influence of windows is less in the WEBVIP estimations compared with the steady state method used in the Excel script.

In the estimations of the energy consumption of the different alternatives the orientation of the building was not considered. For example south facing glass façades get more heat gains from solar radiation, but might also require more cooling in the summer, depending on the existing shading systems and indoor climate requirements. Also, a schematic outline for the building shape was used in the calculations at the conceptual maturity level. In the VIPWEB calculation a cubical shape was assumed whereas in the steady state method a L-shaped building envelope was used. These assumptions lead to different estimations of the building surface area, which affects factors like air leakage, transmission and cold bridges.


69

Both methods give an indication of the relative energy consumption between different design alternatives, and can guide the design regarding the building envelope. However, the estimated values, compared with the actual consumption in the studied case are significantly lower indicating that the level of information maturity is too low to make any prediction of the final energy consumption.

6.1.2 Energy analysis at functional maturity level

Results from the VIP+ tool

Figure 6-3 shows the result of the energy analysis using VIP+ at the functional maturity level.

0

20

40

60

80

100

120

140

SH-10 SH-20 SH-40 LH-10 LH-20 LH-40 PH-10 PH-20 PH-40 Originalbuilding

building envelope

kWh/

(m2 a

)



*

Figure 6-3. Results of the energy calculation with VIP+

The energy consumption ranges from 78 kWh/(m2 a) for the passive house envelope with a 10% window area and indoor climate class C up to about 115 kWh/(m2 a) for the standard house envelop with a 40% window area and indoor climate class A. The Senate Properties Headquarters is estimated to consume 123 kWh/(m2 a) which is 83 kWh/(m2 a) less than the actual consumption (~60%).


70

Analysis

The energy consumption is moderately affected by the different building envelopes and window area. A standard building with a 40% window area only uses about 12% more energy than the same building with a 10% window area. However, the transmission losses and air leakage increase by 80% and 29% respectively for the 40% window area compared with the 10% and window the heat gains from solar radiation also increase by 137%. This can be one explanation why the energy consumption increases only moderately with a lager window area.

Comparing the results from VIP+ with VIPWEB, as illustrated in Figure 6-4, the VIP+ results are more affected by the different window areas, the different indoor climates and the different building envelopes. The VIP+ results also show a slightly higher energy consumption compared to VIPWEB.

SH-10 SH-20 SH-40 LH-10 LH-20 LH-40 PH-10 PH-20 PH-40

0

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120

Class A

VIP+

Class B

Class C

Class A

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Figure 6-4. Comparison of VIP+ and VIPWEB calculation results

As both programs use the same calculation engine, the differences must be explained by the simplified user interface and default values used in VIPWEB resulting in different input data in the two analyses. One significant difference in the input data is the geometry and the placement of the building in VIP+ compared to VIPWEB effecting transmission and air leakage. Furthermore, the U-values for the different building types were set in the VIP+ calculation. These U-values varied much more than the U-values used in the VIPWEB calculation for the different building types. However, the results from the calculations at the functional level using VIP+ are in line with the tendencies from the conceptual level and more pronounced. Still, the actual consumption


71

of the original building compared with the estimated value is significantly higher.

A suitable ventilation system with no heat recovery for the building, (0.5 ACH according to BBR), was used for both the VIPWEB and the VIP+ calculations. The ventilation system accounted for approximately 50% of the energy consumption showing the significance of ventilation in offices in the final result. The ventilation system used in the original building has a different dimension compared with the assumptions made in the calculations. This might account for some of the differences between the actual consumption and the estimated values from the analyses. See also the discussion in the summary of the energy analyses in section 6.1.4.

6.1.3 Energy analysis at system maturity level

Results from RIUSKA tool

The energy simulation at the system maturity level was performed with the RIUSKA analysis software, importing the IFC file of the CAD model of the Senate Properties Headquarters from MagicCad, see Figure 6-5. The results of the analyses are shown below.

0

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SH-10 SH-20 SH-40 LH-10 LH-20 LH-40 PH-10 PH-20 PH-40 Originalbuildingbuilding envelope

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Figure 6-5. Results of the energy calculations with RIUSKA


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The results show a clear difference in the energy consumption depending on building envelope type, window area and indoor climate class. The energy consumption varies from 161 kWh/(m2 a) for the standard building envelope with a 40% window area and a indoor class A to 61 kWh/(m2 a) for a passive house building envelope with a 10% window area and a indoor class C. The energy consumption for the original building calculated with RIUSKA is 136 kWh/(m2 a) which is 70 kWh/(m2 a) less then the original building is using today, about 30% lower compared with the actual value.

Analysis

The energy consumption calculated with RIUSKA shows a distinct difference between the different building envelopes and window areas. Especially the window area affects the energy consumption strongly. Depending on the type of building envelope the energy consumption increases from 76% to 40% respectively, when the area of windows increases from 10% to 40% of the façade for the same indoor climate class. The explanation of the stronger variation in the results is probably that the calculation was performed using a more detailed information maturity level. The RIUSKA analysis was performed using a detailed definition of spaces and indoor temperatures. The ventilation system is also optimised in RIUSKA, e.g. using night cooling during the summer period and taking into account the indoor climate requirements on room level. This might explain why the different parameters affect the energy consumption more distinctly in the RIUSKA estimation compared to, e.g., VIP+. The detailed definition of spaces and requirements on the ventilation system also results in a higher estimated energy consumption compared with VIP+ since kitchens, restaurants and machine rooms require higher ventilation compared with office spaces. Also, the RIUSKA calculations were based on climate data from the Helsinki area while the VIP+ used climate data from the Stockholm area. Hence, this can have some effect on the resulting energy consumptions.

Comparing the results from RIUSKA with VIP+ it can be stated that the RIUSKA results of the different alternatives are consistent with the overall tendencies but more pronounced and somewhat higher in absolute values. However, the result from the RIUSKA estimation of the original building is still significantly lower than the actual consumption.


73


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RIUSKA

Class B

Class C

Class A

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Class B

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Originalbuilding

Figure 6-6. Comparison of RIUSKA and VIP+ energy calculation results

6.1.4 Summary of the energy calculations

The results from the different maturity levels vary a lot. The calculations from the conceptual maturity level show in general too low energy consumption values. The consumption is approximately 60% lower compared with the original building. Lack of information of building geometry, placement of the building on site and space layout makes it impossible to take into account factors such as sun radiation, cooling demands and indoor climate requirements properly. Also, the steady state calculations use average temperatures as a base which makes the method unsuitable for estimation of the effect of climate classes. Therefore, the estimation at the conceptual maturity level can only give tendencies of the energy consumption in relation to the different design alternatives studied in the thesis.

The calculated results from the functional maturity level are higher compared to the conceptual maturity level. Still the estimation is 40% lower than the actual consumption. A suitable ventilation system for the building needs to be assumed since, at this stage the ventilation cannot be dimensioned from indoor climate simulations on room level. Also, the different U-values and window areas do not seem to have such a strong influence on the energy consumption, but the effects of climate classes are more pronounced compared with the conceptual level estimations. E.g. the standard building type with a 40% window area uses only 13 kWh/(m2 a) more than the same type with a 10% window area.

In VIP+ calculation the ventilation system is assumed using one aggregate. The original building uses today eight units. Using several ventilation units


74

increases the energy consumption even if eight units fulfil the same requirements as the assumed one unit.

The VIP+ estimation is calculated using passive cooling and not air conditioning, which reduces the energy consumption during the cooling period, in particular for a building with a large window area in the façade. This might also influence the indoor temperatures not being able to keep within the limits of the required range.

The results from the system maturity level calculated with RIUSKA show a clear picture of the variation for the different design alternatives such as indoor climates, building envelops and window areas.

The overall picture of the energy estimation from all maturity levels for indoor class A is presented in Figure 6-7. The estimations from the different levels show the same tendencies with regard to the different design alternatives. The values from the calculations, especially the conceptual level should be not seen as estimates of the real energy consumption, but more as guidelines for selecting the most energy efficient alternative.

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SH-10 SH-20 SH-40 LH-10 LH-20 LH-40 PH-10 PH-20 PH-40 Original building

Figure 6-7. Energy estimation from all maturity levels for indoor class A.


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The estimate of the original building at the system level using RIUSKA is approximately 30% lower than the actual consumption. The difference can be explained to some extent by the fact that RIUSKA optimises the ventilation system, which is not comparable with the existing system installed in the Senate Properties Headquarters. Also, the occupancy behaviour affects the resulting consumption of a building. Mason (2003) showed that the occupancy behaviour can cause a variation of 10-30% between estimated and measured energy consumption.

6.2 LCC calculation at the system maturity level

Investment cost

Figure 6-8 shows that the alternative investment costs are strongly dominated by the window area and the thermal conductivity of the window glass. The price difference between the different building envelopes ranges between 12–27%, whereas the price difference between the different window areas are between 10–40% higher for the building envelope with a 40% window area. The investment cost (taking into account only the outer walls and windows) for the standard building is significantly lower compared to a low energy or passive house building.

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SH-10% SH-20% SH-40% LH-10% LH-20% LH-40% PH-10% PH-20% PH-40%

building envelope

SEK

Figure 6-8. Investment cost for the different building envelopes


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LCC calculation

For the LCC calculation the resulting energy consumption of the RIUSKA simulation was used. Only the energy cost is included in the operational cost over the discounted period. All LCC scenarios are calculated with the energy price of 1.2 SEK and discounted over a period of 30 years. Figure 6-9 shows the LCC over a period of 30 years with an energy price of 1.2 SEK using an inflation rate of the energy cost of 4% and a discount rate of 4.2%.

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SEK


Figure 6-9. LCC RIUSKA with 4.2% discount rate and a 4% increase of the energy prices

This figure shows that most dominant factor is the window area, as the investment cost of windows is much higher than for the other parts of the building envelope. It is interesting that the standard building with a 10% window area has a somewhat higher LCC than the passive house building with the same size of window area. Also, the low energy building with a 10% window area has a slightly higher LCC than the standard building.

The low energy house with a 40% window area has a lower LCC compared to the standard building with the same window area. Although the investment cost for the low energy building is approximately 400 000 SEK more than for the standard building, the energy of 38 kWh/(m2 a) make it worthwhile to invest.


77

By assuming that the energy prices will increase by 12% instead of 4% the differences in LCC will increase as shown in Figure 6-10. The cost of energy dominates over the investment cost. The evidently more favourable investment solution gets more expensive to operate.

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Figure 6-10. LCC with 4.2% discount rate and 12% increase of the energy prices

The investment cost of the standard building envelope is approximately 8.5 million SEK less than the passive house envelope with a 40% window area. But by discounting the energy consumption over a period of 30 years the LCC cost for the standard building envelope would be about 15 million SEK more than for the passive house envelope. It can be noted that the passive house envelope with a 40% window area is still a more expensive alternative than the low energy envelope with the same window area. However, the calculated energy consumptions for these two alternatives differ only by approximately 2 kWh/(m2 a) and since the investment cost for the passive alternative is 4.3 million SEK higher the savings in energy cannot make up for this difference in investment.

The next scenario represents a low level discount rate of 0.98% and energy price rate of 4%, see Figure 6-11. It is noticeable that the total LCC is substantially lower than for the previous scenario. Also in this scenario the


78

building alternatives also result in lower energy consumption with the same window area (i.e. SH, LH and PH alternatives) and will be more favourable economically to select over a 30-year life span, regardless of the higher investment cost with the exception of the LH envelope compared to the PH alternatives with a 40% window area.

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120 000 000


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SEK


Figure 6-11. LCC discount rate of 0.98% and an energy price increase by 4%

In the last scenario where the discount rate is 0.98% and the energy prices rise 12% the LCC increases in average with 350%, see Figure 6-12.


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SEK


Figure 6-12. LCC discount rate of 0.98% and an energy price increase by 12%

The last scenario shows in general the same tendencies as the previous LCC scenarios. The difference in LCC is more extreme in absolute numbers.

6.2.1 Summary of LCC

It is not possible to predict energy prices or the discount rates over a time period of 30 years. Therefore different scenarios with varying discount rates and price rates of energy are necessary to construct to evaluate the sensitivity in the LCC calculations.

All tested scenarios show basically the same tendencies. A scenario with higher energy prices will of course have a stronger effect on the operating cost of energy in the LCC estimations. In all scenarios the window area is a dominant factor, as the investment cost of windows are much higher than for the other parts of the building envelope. Still the higher investment cost for a window with better thermal conductivity will reduce the LCC in the long run, as the reduced energy consumption interacts stronger with the LCC than the investment. Only in the LH and PH cases, where the energy consumption differs only slightly among the alternatives, the higher investment cost of the better isolated window in the PH case do not paid off in the LCC calculation.


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Overall it can be stated that the energy consumption has an effect on the LCC. Also, the life time of the building envelope and windows can be longer than 30 years with an even higher impact on the operating cost part in the LCC calculation.

Discussion and conclusion

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7 DISCUSSION AND CONCLUSION

This Chapter discusses and concludes the research work. The research questions and contribution are addressed and discussed. Also, suggestions for further research directions are proposed in this Chapter.

7.1 Addressing the research questions

The three research questions, addressed in the beginning of this thesis will be answered one by one in this Chapter.

Research question I

What types of energy simulations are possible to make in the early design phase?

To answer this question the InPro definition of the early design phase was used. The InPro framework divides the early design phase into three information maturity levels, as the amount of design information grows as the design process proceeds. The three maturity levels in the early design phase are the conceptual, the functional and the system level.

At the first level, the conceptual maturity level, only rough energy estimations can be made. These estimations are based on general design information known at this stage such as basic requirements on the volume needed, building type, building use and location. These energy estimations can be made using simple tools based on the steady state method. The use of dynamic simulation is also possible, if the program, such as the VIPWEB tool, has a simplified user


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interface to a dynamic calculation engine where default input values are associated to e.g. type of building.

At the functional maturity level, more detailed dynamic energy simulation can be performed. More details about the building geometry, placement on site and the thermal properties of the building envelope can be examined.

At system maturity level before the detailed design starts, more detailed energy and indoor climate simulations are possible to perform. Impacts of space layout, space distribution, requirements of indoor climate are possible to evaluate. Also, the indoor climate simulation on room level provides the dimensioning parameters of the HVAC system in the detailed design phase.

The type of energy simulations/estimation possible to perform depends on the available information, i.e. the maturity level. At the conceptual level, estimations are rough and need no expert knowledge in the calculation of the energy consumption. A simplified user interface can provide fast access to these estimations. The more detailed energy simulations require expert knowledge and are time consuming to perform since geometrical data, construction information for the building, HVAC system performance, occupancy hours, climate data, etc need to be input into the program. In the case of a model based design process the geometrical data can be imported via, e.g. IFC import. However, data explicit for the energy estimations and indoor climate simulations such as material properties of building parts, temperature zones etc still need to be defined. Errors in the import of the IFC exchange file can also occur and these errors must be detected and fixed before the user can continue to calculate the performance. All of this can make the analysis very time consuming. Nevertheless, the more detailed the energy simulation model is, the more reliable results it gives, if the input data is correctly defined.

Research question II

How reliable are early energy estimations compared to results when detailed models are available?

The energy estimations made at the three different maturity levels all showed the same tendencies. The first estimations at the conceptual level, however, showed a significantly lower energy consumption compared with the analyses made later. This result seems to indicate that it is difficult to estimate the consumptions at this point of time where orientation, space distribution and space usage are not known. The conclusion is that the estimation at the

Discussion and conclusion

83

conceptual maturity level can only show tendencies between different design alternatives and can guide the design in a more energy efficient direction. Therefore estimations when the information maturity is low should only be used to compare different design alternatives at the same design stage. The absolute values cannot be taken as a prognosis of future energy consumptions. Also, the steady state method poses some restriction on the use in comparing, e.g. different alternative indoor climate classes since the prescribed daily temperature variation cannot be modelled.

The energy calculations when the information maturity level is higher give reasonable results and were higher than at the conceptual stage. In this study the calculated energy consumption in still underestimated by approximately 30% compared to the measured values of the original building.

The difference between the estimated and the measured energy consumption can depend on the occupancy behaviour. Discrepancies of ±30% between simulated and measured annual energy consumption have been reported in the literature. Ahmand and Cuple (2006) claim that un-calibrated simulation models may not adequately represent the real operation of the building. Also, in the case of the Senate Properties Headquarters, the actual ventilation system was not modelled correctly.

The different programs used in the study were developed for somewhat different usage and therefore also require different sets of input data even at the same maturity level. Therefore it is recommended, if possible, to use the same program throughout the early design phase to get comparable results. Otherwise, the programs should be benchmarked with each other using the same input data if they are going to be used in the same project.

Research question III

How does energy consumption affect the life cycle cost when considering the investment cost for the building envelope?

In addressing the third research question LCC calculation for all 28 different cases was performed. The LCC calculation only included the investment cost of the building envelope and the RIUSKA estimation of the energy consumption for the different cases. The two main factors, the rate of interest and the rate of energy prices over the discount time of 30 years, were varied to see the influence of the LCC comparison of the different alternatives.


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The result of the LCC calculations shows a strong effect of the estimated energy consumption. All tested scenarios show basically the same tendencies. In all scenarios the window area is a dominant factor, as the investment cost of windows is much higher than for the other parts of the building envelope. Still the higher investment cost for a window with better thermal conductivity reduces the LCC in the long run, as the reduced energy consumption has a strong impact on the LCC than the investment.

7.2 Contribution of the research

The theoretical contribution of the research is that the proposed process model for energy estimation in the early design stage developed in the EU Project InPro was evaluated. A further theoretical contribution is the identification and mapping out the different aspects of the life cycle design by combining the sustainable building framework with the integrated life cycle design aspects.

The practical contribution of this research is the validation and use of the different energy analysing tools to see how accurate the simulated results are compared with the measured ones for the specific case used in the study. Also, recommendations are given for how the different energy analysis can be used in the design process to guide designers and decision makers in a more energy efficient direction. The study also investigated how different design parameters such as window area, indoor temperature and u-value for the building envelope affected the energy consumption and the life cycle cost for the specific case investigated. A similar approach can easily be implemented in the design of real projects to guide decision makers in a more sustainable direction.

7.3 Suggestion for further research

The results of this research provide several directions for further research.

- Identify and quantify the main factors responsible for the difference between measured and estimated energy consumption for different types of buildings, offices, residential buildings etc.

- Investigate how energy estimation in the early design phase can lead to a more energy efficient design.

- Investigate how the proposed design process can be implemented in a BIM supported design environment.

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- Ventilation systems have a strong influence on the energy consumption especially in office buildings. How ventilation systems can be properly modelled in the energy analysis.

Practical development

- Energy simulation tools which can be used for different maturity levels. Some steps have already been taken in this direction, see e.g. Energilotsen, www.energilotsen.nu, but more work is needed especially when using a model based design process.

- Develop tools which are more user friendly, support a model based design and provide better support for checking input data.

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Appendix A. input data

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APPENDIX A. INPUT DATA

Table A-1. Input data for the different energy calculation tool

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Table A-2. Wall and Window area used in VIP+

[m²] Original building

with10% window area

With 20% window area

With 40% window area

South façade 558.7 709.2 506.0 99.5 South Silo 130.4 130.4 130.4 130.4 South window 782.8 676.2 879.4 1285.9 West façade 914.7 159.1 755.69 148.7 West window 326.8 182.2 485.8 1092.8 North façade 656.3 698.4 498.3 98.0 North Silo 1098.6 1098.6 1098.6 1098.6 North window 51 20.82 220.9 621.2 East façade 962.6 1115.0 795.5 156.56 East Silo 65.1 65.1 65.1 65.1 East window 295.2 146.7 466.2 1105.2

Table A-3. U-value for all building elements

Type Property Wall Silo Window Door Ground Roof Standard building

U-value [W/m2 °C] 0.28 0.3 1.8 1 0.32 0.18

Low energy building

U-value [W/m2 °C] 0.24 0.23 1.2 0,63 0.17 0.12

Passive house building

U-value [W/m2 °C] 0.098 0.12 1.0 0,64 0.1 0.06

Table A-4. Indoor temperature

Operative Temperature

°C

Max. middle air velocity

m/s Building-/room

types Categorie

s Summer Winter Summer Winter

A 24.5 ±1.0 22 ±1.0 0.12 0.1 B 24.5 ±1.5 22 ±2.0 0.19 0.16

Office, conference-

rooms, classrooms C 24.5 ±2.5 22 ±3.0 0.24 0.21

Appendix A. input data

95

Table A-5. Ventilations data for RIUSKA

Table A-6. Ventilations data for VIP+

Documents

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