ENERGY USE IN THE EU BUILDING STOCK

CASE STUDY: UK

Examinr : Bahram Moshfegh (Linkoping University)

Supervisor : Erika Mata Las Heras (Chalmers University of Technology)

Reza Arababadi

2012

ISRN: LIU-IEI-TEK-A--12/01526—SE

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Abstract Previous studies in building energy assessmnet have made it clear that the largest potential energy efficiency improvements are conected to the retrofitting of existing buildings. But, lack of information about the building stock and associated modelling tools is one of the barriers to assessment of energy efficiency strategies in the building stocks. Therefore, a methodology has been developed to describe any building stock by the means of archetype buildings. The aim has been to assess the effects of energy saving measures. The model which is used for the building energy simulation is called: Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS). This model calculated the net energy demand aggregated in heating, cooling, lighting, hotwater and appliances. This model has already been validated using the Swedish residential stock as a test case. The present work continues the development of the methodology by focusing on the UK building stock by discribing the UK building stock trough archetype buildings and their physical properties which are used as inputs to the ECCABS. In addition, this work seekes to check the adequacy of applying the ECCABS model to the UK building stock. The outputs which are the final energy use of the entire building stock are compared to data available in national and international sources. The UK building stoch is described by a total of 252 archetype buildings. It is determined by considering nine building typologies, four climate zones, six periods of construction and two types of heating systems. The total final energy demand calculated by ECCABS for the residential sector is 578.83 TWh for the year 2010, which is 2.6 % higher than the statistics provided by the Department of Energy and Climate Change(DECC). In the non-residential sector the total final energy demand is 77.28 TWh for the year 2009, which is about 3.2% lower than the energy demand given by DECC. Potential reasons which could have affected the acuracy of the final resualts are discussed in this master thesis. Keywords: archetype buildings, UK building stock, energy demand, bottom-up modelling, energy simulation

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Acknowledgements

I would like to express my gratitude to all those who gave me the possibility to complete this report. I am grateful to my examiner Prof. Bahram Moshfegh for his vital encouragement and guidence. This research project would not have been possible without his support. I am deeply indebted to my supervisor Erika Mata Las Heras whose help, stimulating suggestions, knowledge and encouragement helped me in all the times of study and analysis of the project. My special thanks to my family to whom this thesis is dedicated to. I have no suitable word that can fully describe their everlasting love for me. I cannot ask for more from my love ‘Atefeh’ who has been a constant source of love, concern, support and strength.

Reza Arababadi Nov. 2012

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Table of Contents

FIGURES ........................................................................................................................................................ V

TABLES ......................................................................................................................................................... VI

TECHNICAL ABBREVIATIONS ............................................................................................................... VII

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

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

1.2 CONTEXT OF THE REPORT ............................................................................................................................. 1

1.3 AIM OF THIS MASTER THESIS ......................................................................................................................... 2

1.4 STRUCTURE OF THE REPORT .......................................................................................................................... 2

2. DATA SOURCES ........................................................................................................................................ 3

2.1 NATIONAL DATABASES ................................................................................................................................. 3

2.1.1. DEPARTMENT OF ENERGY AND CLIMATE CHANGE .......................................................................................... 3

2.1.2 BUILDING RESEARCH ESTABLISHMENT ............................................................................................................ 3

2.1.3 CHARTERED INSTITUTION OF BUILDING SERVICES ENGINEERS .............................................................. 4

2.1.4 ENVIRONMENTAL CHANGE INSTITUTE ............................................................................................................. 4

2.1.5 THE GOVERNMENT’S BOILER EFFICIENCY DATABASE .......................................................................................... 5

2.2 INTERNATIONAL DATABASES ......................................................................................................................... 5

2.3 LEGISLATIONS ............................................................................................................................................ 5

3. EXISTING MODELING TOOLS FOR THE UK ................................................................................................ 6

4. METHODOLOGY ....................................................................................................................................... 7

4.1 ABOUT THE ECCABS MODEL ........................................................................................................................ 8

4.2 SEGMENTATION METHODOLOGY ................................................................................................................... 9

4.2.1 BUILDING TYPE ........................................................................................................................................ 10

4.2.2 CONSTRUCTION PERIOD ............................................................................................................................. 12

4.2.3 CLIMATE ZONE ......................................................................................................................................... 13

DIFFUSE RADIATION ON HORIZONTAL SURFACE ........................................................................... 13

4.2.4 TYPE OF HEATING SYSTEM........................................................................................................................... 15

4.2.5 TOTAL NUMBER OF ARCHETYPES BASED ON THE DEVELOPED METHODOLOGY ........................................................ 15

4.3 CHARACTERIZATION OF THE UK BUILDING STOCK ............................................................................. 16

4.3.1 Average heated floor area ................................................................................................................... 16

4.3.1 TOTAL WINDOWS AREA .............................................................................................................................. 18

4.3.2 TOTAL EXTERNAL SURFACE .......................................................................................................................... 20

4.3.3 AVERAGE U-VALUE OF BUILDINGS ............................................................................................................... 22

4.3.4 AVERAGE CONSTANT LIGHTING LOAD ............................................................................................................ 23

4.3.5 AVERAGE CONSTANT GAIN DUE TO PEOPLE IN THE BUILDING .............................................................................. 23

4.3.6 AVERAGE CONSTANT CONSUMPTION OF APPLIANCES ....................................................................................... 24

4.3.7 HOT WATER DEMAND ................................................................................................................................ 25

4.3.8 INDOOR TEMPERATURE .............................................................................................................................. 26

4.3.9 SANITARY VENTILATION FLOW RATE .............................................................................................................. 27

4.3.10 NATURAL VENTILATION RATES ................................................................................................................. 27

4.3.11 RESPONSE CAPACITY AND MAXIMUM HOURLY CAPACITY OF THE HEATING SYSTEM ............................................. 28

4.3.12 EFFECTIVE HEAT CAPACITY OF WHOLE BUILDING .......................................................................................... 28

4.4 QUANTIFICATION OF THE UK BUILDING STOCK ................................................................................. 29

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4.5 FINAL ENERGY DEMAND .................................................................................................................... 30

4.5.1 FUEL USE IN BUILDINGS WITHOUT CENTRAL HEATING........................................................................................ 30

4.5.2 FUEL USE IN BUILDINGS WITH CENTRAL HEATING ............................................................................................. 31

4.5.3 FUEL USE IN NON-DOMESTIC BUILDINGS ........................................................................................................ 31

4.5.4 HEATING SYSTEM EFFICIENCY ...................................................................................................................... 32

5 RESULTS ................................................................................................................................................. 33

5.1 DESCRIPTION OF THE UK BUILDING STOCK THROUGH ARCHETYPE BUILDINGS ........................................................ 33

5.1.1 SEGMENTATION ....................................................................................................................................... 33

5.1.2 CHARACTERISATION .................................................................................................................................. 35

5.1.3 QUANTIFICATION ...................................................................................................................................... 37

5.2 NET ENERGY DEMAND OF THE UK BUILDING STOCK ......................................................................................... 38

5.3 FINAL ENERGY DEMAND OF THE UK BUILDING STOCK ....................................................................................... 39

6 SENSITIVITY ANALYSIS ........................................................................................................................... 41

7 DISCUSSION ........................................................................................................................................... 47

7.1 ON THE DESCRIPTION OF THE UK BUILDING STOCK .......................................................................................... 47

7.1.1 SEGMENTATION ....................................................................................................................................... 47

7.1.2 CHARACTERIZATION .................................................................................................................................. 47

7.1.3 QUANTIFICATION ...................................................................................................................................... 47

7.1.4 FINAL ENERGY DEMAN ............................................................................................................................... 47

7.2 ON THE METHODOLOGY AND MODEL ............................................................................................................ 48

7.3 COMPARISON BETWEEN THIS WORK AND PREVIOUS WORK WITHIN THE PATHWAYS PROJECT .................................... 48

8 CONCLUSION ......................................................................................................................................... 49

9 FURTHER WORK ..................................................................................................................................... 50

10 REFERENCES ........................................................................................................................................... 50

11 APPENDIX1. STATISTICS ......................................................................................................................... 55

12 APPENDIX 2. DATA USED TO CALCULATE THE EFFECTIVE HEAT CAPACITY .............................................. 61

13 APPENDIX 3. U-VALUES .......................................................................................................................... 75

14 APPENDIX.4 FINAL ENERGY USE ............................................................................................................ 76

15 APPENDIX 5. DECC TABELES ................................................................................................................... 80

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FIGURES FIGURE 1. PROCESSES UNDERTAKEN IN THIS WORK TO CALCULATE THE ENERGY DEMAND OF THE UK BUILDING

STOCK IN ORDER TO CHECK THE SUITABILITY OF THE ECCABS MODEL TO BE APPLIED TO THE UK BUILDING

STOCK. ............................................................................................................................................................ 8

FIGURE 2. CLIMATE ZONES COSIDERED IN THIS WORK. SOURCE: (METOFFICE, 2000) ........................................... 14

FIGURE 3. SURFACE AREA OF DIFFERENT DWELLING TYPES OVER TIME. SOURCE: (ROYS, 2008) ........................... 17

FIGURE 4. FUEL SHARE IN NON-CENTRALLY HEATED DWELLINGS. SOURCE: (PALMER & COOPER, 2011) .............. 30

FIGURE 5. FUEL SHARE IN CENTRALLY HEATED DWELLINGS. SOURCE: (PALMER & COOPER, 2011) ...................... 31

FIGURE 6. FUEL SHARE IN NON-DOMESTIC BUILDINGS (DECC, 2011)(REF) ......................................................... 32

FIGURE 7. DISTRIBUTION OF THE NUMBER AND SURFACE AREA THE OF EXISTING BUILDINGS BY TYPE OBTAINED IN

THIS THESIS WORK ......................................................................................................................................... 34

FIGURE 8. DISTRIBUTION OF THE NUMBER AND SURFACE AREA OF EXISTING BUILDINGS BY CLIMATE ZONE

OBTAINED IN THIS THESIS WORK ................................................................................................................... 34

FIGURE 9. DISTRIBUTION OF THE NUMBER AND SURFACE AREA OF EXISTING BUILDINGS BY TIME OF CONSTRUCTION

OBTAINED IN THIS THESIS WORK ................................................................................................................... 35

FIGURE 10. DISTRIBUTION OF THE NUMBER OF EXISTING BUILDINGS BY TYPE OF HEATING SYSTEM OBTAINED IN

THIS THESIS WORK ......................................................................................................................................... 35

FIGURE 11. ENERGY DEMAND IN DOMESTIC BUILDINGS BY FUEL BASED ON ECCABS MODEL AND DECC TABLES

...................................................................................................................................................................... 40

FIGURE 12. ENERGY DEMAND IN NON-DOMESTIC BUILDINGS BY FUEL BASED ON EABS MODEL AND DECC TABLES

...................................................................................................................................................................... 40

FIGURE 13. COMPARISON OF ENERGY DEMAND BY SUB-SECTORS IN NON-DOMESTIC BUILDINGS ........................... 41

FIGURE 14. BEHAVIOUR OF THE INPUT PARAMETER WITH THE HIGHEST NORMALIZED SENSITIITY COEFFICIENT IN

RESIDENTIAL SECTOR OBTAINED IN THIS WORK ............................................................................................. 43

FIGURE 15. BEHAVIOUR OF THE INPUT PARAMETER WITH THE HIGHEST NORMALIZED SENSITIITY COEFFICIENT IN

NON-RESIDENTIAL SECTOR OBTAINED IN THIS WORK .................................................................................... 44

FIGURE 16. NORMALIZED SENSITIVITY COEFFICIENTS BY PREMISES TYPE AND AGE BAND FOR FOUR SELECTED

INPUT PARAMETERS ....................................................................................................................................... 45

FIGURE 17. NORMALIZED SENSITIVITY COEFFICIENT IN DOMESTIC BUILDINGS ...................................................... 46

FIGURE 18. NORMALIZED SENSITIVITY COEFFICIENT FOR THE U-VALUE IN DIFFERENT CLIMATE ZONES................ 46

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Tables TABLE 1. BUILDING REGULATIONS USED MOST IN THIS MASTER THESIS WORK ........................................................ 6

TABLE 2. COMPARATIVE ANALYSIS OF PREVIOUSLY DEVELOPED MODELS. SOURCE: (KAVGIC, ET AL., 2010) ......... 7

TABLE 3. EXAMPLES OF CLASSIFICATION METHODOLOGY IN THE UK ................................................................... 10

TABLE 4. BUILDING TYPE CLASSIFICATION USED IN THIS WORK. ........................................................................... 11

TABLE 5. DWELLING TYPES IN PREVIOUS STUDIES IN THE UK ............................................................................... 12

TABLE 6. WEATHER DATA FILE INPUTS ................................................................................................................. 13

TABLE 7. CITIES CHOSEN IN DIFFERENT CLIMATE ZONES ....................................................................................... 14

TABLE 8. TOTAL NUMBER OF ARCHETYPE BUILDINGS ............................................................................................ 16

TABLE 9. DWELLING FLOOR AREA ......................................................................................................................... 17

TABLE 10. FLOOR AREA OF NON-DWELLINGS (M2) CONSIDERED IN THIS WORK FOR THE DIFFERENT BUILDING TYPES

AND CONSTRUCTION PERIODS ....................................................................................................................... 18

TABLE 11. Λ AND µ FOR THE DWELLINGS BUILT BEFORE 1985................................................................................ 18

TABLE 12. Λ AND µ FOR THE DWELLINGS BUILT AFTER 1985 .................................................................................. 18

TABLE 13. WINDOW SURFACE AREA OF DWELLINGS (G(GARSTON, 2009)ARSTON, 2009) ..................................... 19

TABLE 14. WINDOW WALL RATIO IN ALL TYPES OF NON-DOMESTIC BUILDINGS FOR ............................................. 19

TABLE 15. METHODS USED TO CALCULATE THW WINDOWS SURFACE AREA FOR ................................................... 20

TABLE 16. DETACHED FACTORS ............................................................................................................................ 21

TABLE 17. COMPARISON OF CHAPMAN AND 3DL .................................................................................................. 21

TABLE 18. EXTERNAL WALL SURFACE OF DWELLINGS OBTAINED IN THIS WORK. .................................................. 21

TABLE 19. U-VALUE OF DWELLINGS BUILT BEFOR 1985 ........................................................................................ 22

TABLE 20. AVERAGE CONSTANT LIGHTING LOAD IN DOMESTIC AND NON-DOMESTIC SECTOR USED IN THIS WORK 23

TABLE 21. AVERAGE METABOLIC RATE BASED ON ACTIVITIES. SOURCE: (ETB, 2011) ......................................... 23

TABLE 22. AVERAGE CONSTANT GAIN DUE TO PEOPLE BY DIFFERENT DWELLING TYPES. ...................................... 24

TABLE 23 CONSTANT CONSUMPTION OF APPLIANCES CONSIDERED IN THIS WORK ................................................. 25

TABLE 24. HOT WATER ENERGY USE OBTAINED IN THIS WORK .............................................................................. 26

TABLE 25. INDOOR TEMPRATURE IN DIFFERNT BUILDING TYPE APPLIED IN THIS MASTER THESIS ........................... 27

TABLE 26. VENTILATION RATES CONSIDERED IN THIS WORK FOR THE DIFFERENT BUILDING ................................. 27

TABLE 27. NUMBER OF BUILDINGS BY TYPE AND TIME PERIODS OBTAINED IN THIS WORK. ................................... 29

TABLE 28. FUEL SHARES FOR NON-CENTRALLY HEATED DWELLINGS (BUILT BEFORE 1985) ................................. 30

TABLE 29. FUEL SHARES FOR CENTRALLY HEATED DWELLING .............................................................................. 31

TABLE 30. FUEL SHARES IN NON-DOMESTIC BUILDINGS FOR ALL CONSTRUCTION PERIODS USED IN THIS WORK ... 32

TABLE 31. HEATING SYSTEM EFFICIENCIES COMPILED FROM LITERATURE SOURCES ............................................. 33

TABLE 32.AVERAGE SURFACE AREA OF RESIDENTIAL BUILDINGS.......................................................................... 36

TABLE 33. PHYSICAL AND THERMAL PROPERTIES OF OFFICES OBTAINED IN THIS MASTER THESIS ......................... 36

TABLE 34. PHYSICAL AND THERMAL PROPERTIES OF RETAILS OBTAINED IN THIS MASTER THESIS ......................... 37

TABLE 35. PHYSICAL AND THERMAL PROPERTIES OF WAREHOUSES OBTAINED IN THIS MASTER THESIS ................ 37

TABLE 36. QUANTIFICATION OF THE NUMBER OF BUILDINGS IN THE UK EXISTING BUILDING STOCK. ................... 38

TABLE 37. NET ENERGY DEMAND BY END USE OBTAINED IN THIS WORK ............................................................... 39

TABLE 38. FINAL ENERGY USE BY FUEL AND END USE OBTAINED IN THIS WORK. ................................................... 39

TABLE 39. COMPARISON OF ECCABS OUTPUTS AND DECC TABLES (FINAL ENERGY) ......................................... 40

TABLE 40. RESULTS FOR SENSITIVITY ANALYSIS IN RESIDENTIAL BUILDING STOCK OBTAINED IN THIS WORK ....... 42

TABLETABLE 41. RESULTS FOR SENSITIVITY ANALYSIS IN RESIDENTIAL BUILDING STOCK OBTAINED IN THIS WOR

...................................................................................................................................................................... 44

TABLE 42. COMPARISON OF THIS STUDY WITH PREVIOUS STUDIES DON IN PATHWAYS PROJECT. .......................... 48

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TECHNICAL ABBREVIATIONS

Location_no Weather region

A Area of heat floor space

Ac Average constant consumption of the appliances

HRec_eff Efficiency of the heat recovery system

Hw Demand of hot water

HyP Consumption of the hydro pumps

Lc Average constant lighting load in the building

Oc Average constant gain due to people in the building

Pfh Heat losses of the fan

Ph Response capacity of the heating system

S Total external surfaces of the building

SFP Specific Fan Power

Sh Maximum hourly capacity of the heating system

Sw Total surface of window the building

T0 Initial indoor temperature TC Effective heat capacity of a heated space (whole

building) Trmin Minimum indoor temperature

Ts Coefficient of solar transmission of the window Tv Tint to start opening windows/nat ventilation

U Mean U value of the building

Vc Sanitary ventilation rate

Wc Shading coefficient of the window

Vcn Natural ventilation rate

Weight Coefficient to scale up the type to the Building Stock Wf Frame coefficient of the window

ATT The attached character of the dwelling

Form A parameter which indicates the configuration of the building LS The living space or heated floor area

Levels Number of floors of the building

HR Height under the roof ρi Density of the layer Cpi Specific heat capacity of the layer

Si Area of the layer di Thickness of the layer

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1. Introduction

1.1 Background

Kyoto agreement is designed to cut emissions of greenhouse gases which cause climate change. According to this Protocol developed countries are committed to reduce their emissions of greenhouse gases by an average of 5.2%, based on 1990 levels, between 2008 and 2012. The scale of reductions is not the same in all countries. The UK is required to reduce its emissions by 12.5% over this time period in order to commit the European target (Johnston, 2003). The United Kingdom has decided to go even beyond the reduction targets introduced by Kyoto (Johnston, 2003).

Carbon emissions from building sector (i.e. residential and non residential) are responsible for 27% of all UK carbon emissions (Collins, et al., 2010). In the non-residential sector, commercial and public buildings are responsible for 12% of total UK GHG emissions (CCC, 2012). UK has more than 27 million buildings where approximately 80% of them are built before 1985 (described further in following chapters). Since a big part of the stock is old in the UK, it seems that there are significant opportunities of improving energy efficiency in the building sector, especially in connection to renovation of existing buildings.

1.2 Context of the report

This master thesis is undertaken as a part of the project Pathways to Sustainable European Energy Systems (PSEES, 2012). This international project aims to evaluate and plan robust pathways, or bridging systems, towards a sustainable energy system in Europe. The Pathways project is a part of the Alliance for Global Sustainability (AGS). In AGS companies e.q. Ford, Du Pont and Vattenfall and academic institutes such as MIT (Massachusetts Institute of Technology), ETH (Eidgenössische Technische Hochschule, Zurich), Tokyo University and Chalmers University of Technology are involved and cooperate in order to find ways to a sustainable future. After a successful first phase of Pathways, the project continues into a second phase. The second phase started in January 2011 and will be running for three years. The areas of research in phase two are those for which there is a solid base in the methodology developed and for which it is believed that the Pathways research group has scientific excellence.

The European building sector is included in the Pathways project and one of the aims of the project is to approximate the potential energy savings by applying different energy efficiency measures to the existing building stock (Johnsson, 2011). Previous works within the project has developed a methodology for assessing potential energy savings in the European building stock. The methodology includes a description of the existing building stock and the development of modeling tools to facilitate the assessment of energy efficiency potentials. The six EU countries with the largest building stocks representing over 70% of the buildings’ energy use in Europe will be studied. The member states with the highest final energy

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consumption in the residential and non-residential sector are Germany, United Kingdom, France, Italy, Spain, and Poland. In addition Sweden and a fictitious country representative for the rest of EU countries are also studied.

One of the models developed for the study of the building sector is a model named Energy Carbon and Cost Assessment of Building Stocks (ECCABS) (Mata & Kalagasidis, 2009) , which is a bottom-up model to assess energy-saving measures (ESM) and carbon dioxide (CO2) mitigation strategies in building stocks. The model is based on a one-zone hourly heat balance that calculates the net energy demand for a number of buildings representative of the building stock and an additional code for the input and output data. The model generates results in terms of delivered energy, associated CO2 emissions, and the costs of implementing different ESM. The results are extended to the entire building stock by means of weighting factors. Empirical and comparative validations of the heat-balance modelling of single buildings have been presented (Mata, et al., 2011). The building stock modelling has been validated against the current Swedish residential stock, for which the results of the modelling are in agreement with the statistical data (Mata & Kalagasidis, 2009). The model has also been used to investigate the Spanish building stock (i.e. residential and non-residential buildings) (Benejam, 2011).

1.3 Aim of this master thesis

The overall aim of this master thesis is to continue the development of a methodology to describe a building stock by selecting a number of reference buildings that are representative of the stock and then check the suitability of the ECCABS model to be applied to the UK building stock. Thus, this thesis work seeks to answer the following questions:

o Is it possible to describe the UK building stock through archetype buildings?

o Does the already developed modeling methodology (within Pathway project) need to be adapted to be applied for the UK?

Results of this work should help the investigation of the effects of efficiency measures in the UK buildings, although it is beyond the scope of this thesis work.

1.4 Structure of the report

Data sources are introduced in chapter 2. Moreover a brief explanation about the existing models on the building stock in UK is included in chapter 3.

The methodology used to select the archetype buildings is introduced in chapter 4 and a comparison to the previous studies in other countries is conducted. Characterization of the UK building stock is reported in chapter 4 and the way of setting the input parameters are introduced separately. Moreover in each part the assumptions and estimations made due to lack of data are explained.

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In chapter 5 a summary of characterization and quantification of the UK building stock are presented. Moreover results obtained from the EABS model are compared to the official data sources both in sub sectors and type of fuel.

Finally, sensitivity analysis has been done and reported in chapter 6. Final results, the modeling limitations, and the potential factors which could have affected the results are discussed in chapter 7 and a number of conclusions are derived.

2. Data sources

This section presents the main data sources that have provided the required information. The national data bases present most of data which is needed to describe the UK building stock. The building regulations help to describe the energy system of the buildings and the indoor climate conditions. International databases which provide statistics are used to compare the results taken from the simulating model.

2.1 National databases

2.1.1. Department of energy and climate change

The Department of Energy and Climate Change (DECC) is a new government department which was created by the prime minister on 3rd October 2008. It covers the tasks which have been previously undertaken by the Climate Change Group housed within the Department for Environment, Food and Rural Affairs (Defra) and the Energy Group from the Department for Business, Enterprise and Regulatory Reform (BERR). Their current priorities are:

o Save energy with the Green Deal and support vulnerable consumers o Deliver secure energy on the way to a low carbon energy future o Drive ambitious action on climate change at home and abroad (DECC, 2012)

Data regarding the final energy use of the UK building stock is provided by the DECC. In this master thesis this kind of data is used to calibrate the model.

2.1.2 Building Research Establishment

The Building Research Establishment (BRE) was first formed in 1917 as an organization to investigate various building materials and methods of construction suitable to use in new housing following the First World War. This organization was originally called the Building Research Station, and later the Building Research Establishment. In 1997 they became a private company and now they are called BRE. They are known as an independent research-based organization. They offer expertise in every aspect of the built environment and

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associated industries. They also help government, industry and business to meet the challenges of the built environment (BRE, 2012).

A model called BREDEM (The Building Research Establishment’s Domestic Energy Model) which is developed by BRE is the most widely used physically based model for the estimation of domestic energy demand in the UK. In this master thesis work their both domestic and non-domestic building fact files (Palmer & Cooper, 2011 ; bre, 1998) have been used.

2.1.3 Chartered Institution of Building Services Engineers The Chartered Institution of Building Services Engineers (CIBSE) is an association that represents building services engineers. It provides consultation to the government on matters relating to construction, engineering and sustainability (CIBSE, 2012). CIBSE publishes several guides including standards and recommendation for designers; a number of its publications have been cited within the UK building regulations. The main guides are:

o Guide A: Environmental Design o Guide B: Heating, Ventilating, Air Conditioning and Refrigeration o Guide C: Reference Data o Guide D: Transportation systems in Buildings o Guide E: Fire Safety Engineering o Guide F: Energy Efficiency in Buildings o Guide G: Public Health Engineering o Guide H: Building Control Systems o Guide J: Weather, Solar and Illuminance Data o Guide K: Electricity in Buildings o Guide L: Sustainability o Guide M: Maintenance Engineering and Management

Guide A: Environmental Design is cited in various parts of this master thesis work. Data regarding the ventilation and infiltration rates in non-domestic buildings, internal gains, etc. have been extracted from this reference.

2.1.4 Environmental Change Institute

The environmental Change Institute (ECI) was started 20 years ago with a mission to organize and promote interdisciplinary research on the nature, causes and impact of environmental change and to contribute to the development of management strategies for coping with future environmental change; it is still base of the ECI’s ethos of focused environmental research and knowledge exchange (ECI, 2011).

One of their publications most used in this work is a study under taken by Fawcett, et al. (2000) that covers domestic gas and electricity energy consumption in lighting, appliances and water heating. Moreover they have developed a bottom up model named UKDCM which

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is nowadays freely available and is used to estimate the energy use in the residential stock (Kavgic, et al., 2010).

2.1.5 The Government’s Boiler Efficiency Database

The Boiler Efficiency Database is a website which presents data for boilers in current production. This data has been provided by boiler manufacturers, who have had an opportunity to check the database entries before publication (BED, 2012).

In this work in order to calculate the final energy use the boiler efficiency has been required. This data has been derived from this source mainly for the buildings constructed during the recent periods.

2.2 International Databases

Eurostat is the statistical office of the European Union. It presents the statistics at European level and enables comparisons between countries and regions. Statistical authorities of each country send the national data to Eurostat to be verified and analyzed to ensure that the data of different countries can be compared (Eurostat, 2012).

In this master thesis work the data on the residential final energy use was obtained from Eurostat to compare with the results obtained from the ECCABS model.

2.3 Legislations

Building regulations have been one of the most useful sources in the current work. Data regarding the U-value of buildings, building fabrics, infiltration, and ventilation rates are extracted from the regulation of each time period.

The more referred legislation documents in this work have been part L (DCLG, 2012) and F (Part F, 2010) of building legislation which take care of energy use and indoor climate in England and Wales. Part L has been first introduced in 1985. It mainly dealt with heating systems. It was revised in 1990 and again in 1995 to standardize the “conservation of fuel and power”. In 2002 it was divided into two main parts L1 and L2 dealing with Dwellings and Non-Dwellings respectively. Afterward in 2006 and 2010 the standards for U-Values and plant efficiency were improved (STROMA, 2011).

Part F of the building regulations deals with the ventilation systems and the standards for air quality requirements for all buildings are included in this part. Scotland and north Ireland have their own legislation known as “Technical Handbook Section 6” and “technical booklet F” respectively.

Relevant regulations that apply to the building stock which are used in this thesis work are summarized in table 1.

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Table 1. Building regulations used most in this master thesis work

Building legislations Title Related region

Part L Conservation of fuel and power England and Wales

Technical booklet F Conservation of fuel and power North Ireland

Technical handbook Section 6 Energy Scotland

3. Existing modeling tools for the UK

A study done by Kavgic, et al. (2010) makes a comparison between different assessment methods as well as comparing the previous models developed on building stock in the UK. The existing models aim to approximate the baseline energy use of the existing stock and provide an estimation of the future of residential energy demand. BREDEM (The Building Research Establishment’s Domestic Energy Model) is the most widely used physically based model for the estimation of domestic energy use (Kavgic, et al., 2010). It applies a series of heat balance equations and empirical relationships to calculate the yearly or monthly energy use of an individual building. One of the main advantages of the BREDEM algorithms is the overall modular structure which enables to be modified to meet particular requirements. For example, BREDEM defines the electricity use for lighting and appliances using simple relationships based on floor area and occupant numbers that can easily be replaced by a more complicated approach if needed. The other models which are listed bellow will be analyzed in more details in this chapter.

o The Building Research Establishment’s Housing Model for Energy Studies (BREHOMES) developed by Shorrock and Dunster (Shorrock & Dunster, 1997)

o The Johnston model developed by Johnston (2003)

o The UK Carbon Domestic Model (UKDCM) developed by Boardman, et al. (2005) as part of the 40% House project

o The DECarb model developed by Natarajan & Levermore (2007)

o The Community Domestic Energy Model (CDEM) developed by Firth, et al. (2009)

These models are very different in their segmentation methodology. DECarb is widely disaggregated. It uses a relational data set to describe 8064 unique combinations for 6 time periods. UKDCM similarly includes over 20000 building types by 2050, classified by climate zones, age bands, types of construction, number of floors, tenure and construction method. BREHOMES divides the housing stock into over 1000 categories, defined by built form,

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construction age, tenure and the central heating ownership. CDEM aggregates yearly energy use of merely 47 house archetypes, derived from unique combinations of built form type and dwelling age. On the other hand, the Johnston model has been developed around only two ‘notional’ dwelling types (pre- and post-1996). All the models need assumptions both in the absence of direct data and in the application of input values where some supporting data are available. Table 2 compares the previously developed models.

Table 2. Comparative analysis of previously developed models. Source: (Kavgic, et al., 2010) Name BREHOMES Johnston UKDCM DECarb CDEM

Developer Building Research

Establishment (BRE)

PhD thesis (Leeds University)

Environmental Change Institute (ECI), Oxford

University

University of Bath, University of Manchester

Department of Civil and

Building Engineering,

Loughborough University,

Loughborough, UK

Year Early 1990s 2003 2006 2007 2009

Level of disaggregation

1000 dwelling types (defined by age group, built

form, tenure type and the ownership of central heating)

Two dwelling types (pre- and post-

1996)

20000 dwelling types by 2050

8064 unique combinations for 6

age bands

47 house archetypes,

derived from unique combinations of

built form type and

dwelling age

Level of data input Requirement

Medium (national statistics)

Medium (national statistics)

Medium (national statistics)

Low (defaults from national statistics)

Medium (national statistics)

Application Policy advice tool (used by DEFRA)

Policy advice tool Policy advice tool

(Oxford) Policy advice tool Policy advice tool

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4. Methodology

The methodology undertaken in this master thesis to calculate the UK´s buildings energy demand has been developed within Pathways project (Benejam, 2011). The several steps of the process are illustrated in figure 1. The first three steps (segmentation, characterization and quantification) aim to the representation of the existing building stock through archetype buildings. An archetype building is a sample building representing a group of buildings. Once these steps are taken, the energy simulation is ready to be run.

As the ECCABS model gives the net energy demand the next step would be to calculate the final energy use in the UK building stock. By considering the heating systems efficiencies the final energy demand is calculated and results are compared to the official data sources.

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Figure 1. Processes undertaken in this work to calculate the energy demand of the UK building stock in order to check the suitability of the ECCABS model to be applied to the UK building stock.

4.1 About the ECCABS model

The model used in the present work, which is termed Energy, Carbon and Cost Assessment for Building Stocks (ECCABS), is designed to assess the effects of Energy saving measures (ESM) for building stocks. The main outputs from the model are: net energy demand by end-uses; delivered energy (to the building); CO2 emissions; and costs associated with the implementation of ESM. In this master thesis work the model is used to calculate the net energy demand and the delivered energy to the building stock.

In addition, the model aims to:

o facilitate the modelling of any building stock of any entire region or country

o allow for easy and quick changes to inputs and assumptions in the model

o provide detailed outputs that can be compared to statistics, as well as in a form

such that they can be used as inputs to other (top-down) models

SEGMENTATION

Definition of the amount of archetype buildings

CHARACTERISATION

Physical characteristics of the buildings and building services

QUANTIFICATION

Number of buildings of each type in the reference year

SIMULATION

Calculation of net energy demand with the ECCABS model

FINAL ENERGY

Transferring the net energy to final energy demand

VALIDATION

Comparison of the results to statistics

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o be transparent

To achieve these objectives, the complexity of the model has to be limited so as to avail of inputs from available databases and to facilitate short calculation times. Reducing the amount of input data will support efforts to gather data in regions for which information is lacking. Therefore, the buildings are described in the model with a restricted number of parameters, the outputs from the model are given in an aggregated form for the studied building stock, and the levels of input data required to describe the energy system and the possible scenarios are also limited. The model is a bottom-up engineering model, which means that calculation of the energy demand of a sample of individual buildings is based on the physical properties of the buildings and their energy use (e.g., for lighting, appliances, and water heating), and the results are scaled-up to represent the building stock of the region studied. Thus, the modelling assumes that a number of buildings can be assigned as being representative of the region to be evaluated. The energy demand and associated CO2 emissions of the existing stock are calculated for a reference (baseline) year and the potential improvements of the ESM application are given as a comparison to the baseline. The model is written to be generally applicable and, thus, does not have any embedded data. (Mata, et al., 2011).

The parameters introduced to the model as input data will be presented in following chapters of this thesis work. As it is mentioned in previous sections a number of models have been already developed for the UK buildings stock. But this work aims at using a tool which is capable to be applied to any region.

4.2 Segmentation Methodology

The characterization of the building stock is carried out for a number of buildings considered representative of the entire UK building stock: the archetype buildings. The number of such archetype buildings is decided in the segmentation process and they are defined according to categories previously considered as the ones that have the largest impact on the energy consumption of the buildings.

The number of archetype buildings chosen is a compromise between accuracy and feasibility since the more type of buildings, the more precisely the stock is represented, but it also becomes more difficult to work with the data and it increases the simulation time. The criteria applied in most of the studies are similar, as was discussed in Benejam (2011). The category “dwelling typology/ type of building” is included in all the studies, and “climate zone” and “age of construction” are the other categories most often considered.

Following the segmentation proposed by Mata (2011) and included in the Pathways Project, four categories are considered in this master thesis to segment the UK building stock into archetype buildings: building type, climate zone , period of construction and type of heating

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system. Type of heating system was added due to the fact that buildings with different type of heating systems use different kind of fuels and have different efficiencies.

In the following chapters these factors are investigated in details and number of the archetype buildings is presented in chapter 4.2.5.

The above mentioned is in agreement with what is concluded in Tabula (2010) , that is a recent study which examines the experiences with building typologies in the European countries. The objective is to learn how to structure the variety of energy-related features of existing buildings (Tabula, 2010). Current models in the UK use different segmentation methodologies. The most important models applied to the building stock in UK are: BREHOMES, Johnston, UKDCM, DECarb, and CDEM. Segmentation methodologies used by these models are reported in Table 3.

Table 3. Examples of classification methodology in the UK

Model Segmentation Criteria

Resulting amount of archetypes

BREHOMES age group

1000

built form

tenure type

the ownership of central heating

Johnston Age

Pre-1996 post-1996

-

DECarb age band 6 age bands 8064

CDEM built

form type and dwelling age

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4.2.1 Building Type

Building type has a significant impact on energy performance of buildings; heating energy is related to external wall area and windows area. Detached buildings have more external walls and more glazing than semi detached or terraced buildings, on the other hand flats use considerably less energy since they have less external surface. A number of inputs to the ECCABS which are listed below are dependent on the building type:

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o Effective heat capacity of the building (Tc) o Floor area o External surface area o Internal gains o Minimum desired indoor temperature (Trmin) o Maximum desired indoor temperature (Trmax) o Sanitary ventilation rate(Vcn)

In this master thesis work the author has decided to classify the domestic buildings into six categories: detached, semi detached, terraced, flat, bungalow, and others. This classification strategy has been chosen due to the form of available data in the data source (see Palmer & Cooper (2011)).

Segmentation of non-domestic buildings is done based upon the classification used by the Valuation Office (BRE, 1998). Most of data presented in BRE (1998), groups buildings into offices, factories, warehouses and retails. These are known as the Valuation Office’s bulk classes. The bulk classes cover about 70% of the all ratable non-domestic buildings (Ratable value represents the open market annual rental value of a business/ non-domestic property), they cover most non residential premises but exclude most hospitals, schools churches etc. In this thesis work the factory buildings are also excluded due to lack of data on energy use of this kind of buildings to compare the results obtained from the energy demand simulation. In Eurostat final energy consumption in households, services, etc. covers quantities consumed by private households, commerce, public administration, services, agriculture and fisheries. Gains database includes: agriculture, commercial and public services, residential and 'non-specified other' sectors. Table 4 presents the classification used in this master thesis work for both domestic and non-domestic buildings.

Table 4. Building type classification used in this work.

Building subsector Building type

Domestic buildings

Detached semi detached

traced flat

bungalow others

Non- Domestic buildings Offices Retails

Warehouses

The building types used in this master thesis work is different from the previous works done within the Pathways project. In previous work done by Benejam (2011) which have studied

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the Spanish building stock, the dwellings are classified into two types: SFD (Single Family Dwelling) and MFD (Multi Family Dwelling). Also Martinlagardette (2008) considers just permanent occupied dwellings (PODs). Due to the form of available data in the UK these types of classifications has not been realistic to be applied.

The studies done on the UK building stock use almost the same classification which is applied in this master thesis work. Table 5 reports the classification method used by (Collins, et al. (2010) and Firth, et al. (2009). As the table shows they have both considered the same method. In this thesis work the flats and terraced dwellings are considered in one category as data regarding the Converted apartment, Purpose built apartment, End terrace, and mid terraced was lacking.

Table 5. Dwelling types in previous studies in the UK

Source Building Type classification

Collins, et al. (2010)

End terrace Mid terrace

Semi-detached Detached

Converted apartment Purpose built apartment Temporary/unknown

Firth, et al. (2009)

End terrace Mid terrace

Semi-dethatched Detached

Converted apartment Purpose built apartment

4.2.2 Construction period

Construction period is an important parameter in performing simulation for the UK building stock, the construction technology and building materials has changed dramatically during recent decades. On the other hand building regulation has been revised frequently during the history of the UK. A number of input parameters to the ECCABS model are strictly dependent on construction age. These parameters are as follow:

o Average U-value of the building (U) o Window area (Sw) o Sanitary ventilation rate(Vcn)

Part L of the building regulation which has been used as one of the most important sources in this thesis work covers the requirements to decrease energy use of premises. Part L deals with

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premises in England and Wales. Scotland and north Ireland have their own legislation (see appendix 3). Part L of building regulations has been considered as the base to perform the time period segmentation for the entire UK because the largest portion of buildings are located in England and Wales. Based upon this explanation and according to the updates of the regulation Part L (see section 2.3) the construction period in the UK is classified into seven categories:

o Before 1985 o 1986-1991 o 1992-1995 o 1996-2002 o 2003-2006 o 2007-2010 o After 2010

Construction periods considered in previous studies in UK building stock are different from the one applied in this work. Johnston (2003) for instance considers two construction periods (before and after 1996) while in the study undertaken by Collins, et al. (2010) existing stock has been defined as housing built up to 1996.

4.2.3 Climate zone

The outdoor climate affects the heating and the cooling demand. Therefore, the ECCABS model considers a different weather file for each climate zone. The weather files are input files required by the ECCABS model. These files are introduced to the ECCABS model as a txt file which can be created from a normal Excel file. The file includes the inputs described in Table 6.

Table 6. Weather data file inputs

Description Unit Time S

Air temperature °C

Dew point temperature °C

Global radiation on horizontal surface W/m2

Diffuse radiation on horizontal surface W/m2

Normal direct radiation W/m2

Long wave radiation W/m2

Illuminance global Lux

Illuminance diffuse Lux

Illuminance direct Lux

Wind direction Deka degrees

Wind speed m/s

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One of the objectives of the model is to avoid complexity and reduce the computational time. Increasing the amount of climate zones considerably increases calculation time (Mata, et al., 2011). Thus, the minimum possible climate zones have to be considered, because the more climate zones considered the more archetypes need to be selected and the more computational time required. Classification of the climate zones in the UK is done based on the climate maps presented by Met Office. Figure 2 is taken from Met Office and has been used as the base of climate zones classification for the UK.

Figure 2. Climate zones cosidered in this work. Source: (MetOffice, 2000)

Bearing in mind that heating demand is the largest share of total energy use of the building stock in the UK, climate zones are considered based on winter maps. Table 7 lists the cities which have been chosen to represent the entire climate zone where corresponding weather data files have been introduced to the model (climate numbers in table 8 are related to the figure 2). These cities have been selected as they have the largest population and consequently the largest number of buildings in each region. Thus, the climate data of the weather stations is assumed to be representative of the corresponding climate zone.

Table 7. Cities chosen in different climate zones

Chosen cities Climate Number London 1

Birmingham 2

Newcastle 3

Glasgow

4

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The study under taken by Collins, et al. (2010) selects the weather data based on the HadRM3 model which has data for 50×50 km2 grid boxes over the UK. Four grid boxes were Chosen, which contained four UK locations: Ringway, Manchester, Edinburgh, Heathrow, London, and Cardiff. In the CDEM and Johnston models the dwellings are subjected to the same weather conditions (Firth, et al., 2009 ; Johnston, 2003). Boardman, et al. (2005) considers nine geographical areas but it is not specified which climate zones are chosen. Furthermore the UK´s building regulation codes do not include any information about the climate zones.

4.2.4 Type of heating system

There are two types of heating system in the UK, central and non-central. These two categories have been considered due to the reason that some parameters which are listed below are dependent on this factor.

o Internal temperature o Fuel share

In the BRE’s housing fact file the average internal temperature for centrally heated dwellings is given to be 17.5 °C while it is 14°C for non-centrally heated premises (Palmer & Cooper, 2011). Note that during the past few decades the old non-central heating systems have been changed into central ones, thus the author of this thesis work has assumed that premises which currently have non-central heating system are all built before 1985. Therefore this factor does not increase the number of archetypes which are built after 1985.

4.2.5 Total number of archetypes based on the developed methodology

Based on the segmentation methodology presented in this chapter 168 archetype were chosen in domestic sector and 84 archetypes were selected in non-domestic category. Table 8 summarizes the amount of archetypes resulting of the segmentation procedure under taken based on previous explanations.

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Table 8. Total number of archetype buildings

Type Period of

construction Climate

Zone Heating system

Total number of archetypes

Domestic 6 6 4 2 168

Non-Domestic

3 6 4 2 84

Notice that, as the reference year for the simulation procedure is 2010 for domestic buildings and 2009 for non-domestic sector, the buildings built after 2010 are not considered and that’s why the number of groups in the period of construction is 6 (not 7). Furthermore as it was previously mentioned the heating system type is just considered for the first construction period (before 1985).

4.3 Characterization of the UK building stock

In this chapter the thermal properties and energy related parameters of the building stock of the UK are explained. These parameters are considered based on the input requirements of the ECCABS model.

4.3.1 Average heated floor area

The average heated floor area of the dwellings has been determined based on a study undertaken by Roys (2008). According to this source the floor area of flats has stayed approximately 60 square meters on average; there has been very little change over time. In the newer stock (after 1981) flats have an average range of 50 to 55 square meters of floor area. The average floor area of bungalows as well has stayed almost constant over that time period. On average the floor area of bungalows is 70 to 75 square meters. Figure 3 illustrates the average floor area of different dwelling types by age band.

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Figure 3. Surface area of different dwelling types over time (Roys, 2008).

Based on this illustration the surface area of different building types is estimated by considering the average floor area of each building type in each time period (see table 9). The results obtained are in agreement with Johnston (2003) where the weighted average useable floor area of the Great Britain housing stock is assumed to be 85m2 while Roys (2008) which is applied in this work estimates it to be slightly over 80 m2.

Table 9. Dwelling floor area

Building type Average floor area(m2)

Detached 150

Semi-detached 90

Traced 80

Flat 60

bungalow 73

Other 85

Comprehensive data about the floor area of non-domestic buildings disaggregated by building types is not available in national and international data bases. The author of this master thesis work has decided to do some calculations based on data presented by BRE’s Non-Domestic Building Fact File (BRE, 1998) to determine the floor area of non-dwellings (see appendix.1). In the BRE’s Non-Domestic Building Fact the number of non-domestic buildings in each time period and the total surface area of each building type are given. In this work the surface area of each building type is calculated in different time periods by dividing the total surface area by the total number of buildings in each time period. Table 11 reports the floor area surface assumed based upon BRE’s Non-Domestic Building Fact File. The values shown in table 10 are the average surface area of each non-domestic building type over the various time periods.

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Table 10. Floor area of non-dwellings (m2) considered in this work for the different building types and construction periods

Building type Before 1985 1986-1990 After 1990

Retails 143 479 463

Offices 227 428 423

Warehouses 630 680 793

4.3.1 Total windows area

A few numbers of methods of approximating windows area have been introduced in previous studies. The most common method is introducing a ratio between the total window area and total floor area of the building (Chapman, 1994).

A method presented by Chapman (1994) suggests a new way to estimate the total window area. It is done by applying a formula which is given bellow.

Sw=λ + µTfa Equation 1

Where Tfa is the total floor area, λ and µ are coefficients to be determined for each archetype. These coefficients are given for each dwelling type and construction period (see tables 11 and 12). Since in this master thesis the dwellings built before 1985 are classified in a single group, the weighted average values for the buildings built in his period have been used. Coefficients of the dwellings built after 1985 are assumed to be identical with the ones given as post-1976 by Chapman (1994).

Table 11. λ and µ for the dwellings built before 1985. Calculated (Weighted average) based on (Chapman, 1994)

built before 1984 λ µ

Detached 10.43 0.10

Semi-detached 10.9 0.08

Terraced 6.05 0.12

Bungalow 5.75 0.13

Table 12. λ and µ for the dwellings built after 1985 calculated based on (Chapman, 1994)

built after 1985 λ µ

Detached 2.33 0.133

Semi-detached 9.43 0.069

Terraced 5.96 0.077

Bungalow 6.56 0.110

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Window surface area for flats is taken from The Government’s Standard Assessment Procedure for Energy Rating of Dwellings (Garston, 2009). Table 13 presents the formula to calculate the window area given by Garston (2009). In this master thesis work it has been decided to apply Garston (2009) method to calculate the windows area as it is specific for the UK.

Table 13. Window surface area of dwellings (G(Garston, 2009)arston, 2009)

Period Window area(m2)

Before 1985 0.0801 TFA1 + 5.580 1986-1990 0.0510 TFA + 4.554 1991-1995 0.0813 TFA + 3.744 1996-2002 0.1148 TFA + 0.392 2003-2006 0.1148 TFA + 0.392 2006-2010 0.1148 TFA + 0.392

Smith (2009) suggests a number of ratios to calculate the windows surface area in non-domestic buildings. Table 14 is taken from his work and the same amounts have been considered by the author to run the ECCABS model. For the buildings built before 1985 the weighted average ratio has been considered.

Table 14. Window wall ratio in all types of non-domestic buildings for all building types. Source: (Smith, 2009)

Period Window Wall ratio Before 1965 set to 10% of floor area 1966-1984 33% 1985-1995 35% After 1996 40%

Table 15 lists the methods used to calculate the window surface area for different building types.

1 TFA is the total floor area

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Table 15. Methods used to calculate thw windows surface area for all buildings types in this work

Building type Method used to calculate

windows surface

Detached

Semidetached

Bungalow

Terraced

The method presented by Chapman (1994)

Flats The method presented by

Garston (2009)

Non- domestic buildings The method presented by

Smith (2009)

4.3.2 Total external surface

No data has been found in literature review on the external wall surface. But by making some assumptions there are still some strategies to estimate the external wall areas. The most common floor-plan shape for a dwelling in the UK is a rectangle (Chapman, 1994). Literature study makes it clear that dwellings with a rectangular floor plan normally have an aspect ratio of between 1.4 and 1.5 (Chapman, 1994). Accordingly in this master thesis work it was assumed that the aspect ratio for all dwellings is of 1.5. Therefore by having the aspect ratio, total floor area and ceiling height it would be realistic to approximate the external wall areas. Chapman’s method is compatible with 3CL-method2. According to the 3-CL method total wall area is calculated by following expression (Martinlagardette, 2009).

Swell=ATT × Form ×� ������ × (Level× HR) - Sw Equation 2

Where: ATT is the attached character of the dwelling Form is a parameter which indicates the configuration of the building A is the living space or heated floor area Levels is the number of floors of the building HR is the height under the roof (2.5 m) Sw is the window area The attached character of the dwelling (ATT) can be taken from Table 16 according to the 3-CL method.

2 French Environment and Energy Management Agency (ADEME) introduces algorithms from the 3-CL method For calculating end-use

energy consumption in dwellings.

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Table 16. Detached factors

Building type ATT Detached 1

Semi detached 0.7 Terraced 0.35

For the dwellings in the UK, based on method presented by Chapman (1994) it is assumed that the dwellings have rectangular floor plan and the configuration factor (Form) for this kind of buildings is given to be 4.12. Table 17 compares the floor areas obtained from 3DL and Chapman methods for detached, attached and semi detached buildings. To carry out this comparison the author has considered one floor buildings. As the table shows values obtained from these two models are almost identical.

Table 17. Comparison of Chapman and 3DL

Building type Floor area Level External walls area obtained

from Chapman’s method

External walls area obtained from 3DL’s

method Terraced 80 1 36.5 32.24 Detached 150 1 125 126.14

Semi detached 90 1 67.7 68.4

The author has decided to apply Chapman’s method since it is more specific for the United Kingdom. No data was found on the external surface of the non-residential buildings. Thus the author has assumed that non-residential buildings have also a rectangular floor area with the aspect ratio of 1.5 and ceiling height of 2.5m.mTable 18 presents the values of external walls introduced to the ECCABS model.

Table 18. External wall surface of dwellings obtained in this work.

Dwelling type External walls surface

(m2) Detached 430

Semidetached 250 Terraced 198 Bungalow 236

Flat 54 Others 268

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4.3.3 Average U-Value of Buildings

The average U-value of buildings is calculated based on the requirements set by building legislations. It has been assumed that all buildings constructed in each period satisfy the requirements of building regulations of that period. Part L of building regulation controls the minimum requirements for buildings from energy efficiency point of view (see appendix 3). The average U-value has been calculated using Equation 3.

� = ���� ���� ��( ����� �����)�(� ���� � ����)�(������� ������� )� Equation 3

Where A and U are the surface area and the U-value of each element respectively. Building standards in North Ireland is derived from the Department of Finance and Personnel (DFP) while the Scottish legislations are taken from the Scottish Government website (see Appendix.3). The average U-value for the buildings constructed before 1985 is taken from a number of sources which are given in table 19. Based on the values found in literature review the author has decided to introduce the following values to the ECCABS model:

o Average U-vale of walls : 1.36 (W/m2K) o Average U-vale of floor : 0.51(W/m2K) o Average U-vale of roof : 1(W/m2K) o Average U-vale of windows: 5.7(W/m2K)

Table 19. U-value of dwellings built befor 1985

Source U-value (W/m2K) (Johnston, 2003) Walls : Uninsulated cavity : 1.36

Uninsulated solid : 2.12 Roofs : Insulated accessible : 0.36 Uninsulated accessible : 2.02 Inaccessible : 0.51 Floors : Solid concrete and suspended timber : 0.60 & 0.80 Windows: Double and single-glazed units : 3.30 & 4.70

(Collins, et al., 2010) Based on average for historic group UK stock built between 1981 and 1996

Walls: 0.3999 Floor : 0.4577 Roof : 0.1416 Window : 2.3967

(Firth, et al., 2009) U-values for the1945 to 1964 semi-detached house archetype

Wall : 1.2 Roof : 0.44

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4.3.4 Average constant lighting load

Since a big change has taken place in lighting energy, in this master thesis work it is assumed that the lighting system is unique in all dwellings independent of their construction period. Collins, et al. (2010) is the only found source which introduces the lighting energy use in buildings. According to this source the lighting energy use is 6 w/m2. If one assumes that the lights are in average on for 3 to 4 hours per day then the average constant lighting load would be around 0.9 w/m2. This is in agreement with the figure given by the BRE’s Housing Energy Fact File which suggests the constant lighting load of 0.88 w/m2 (see appendix 4). The average constant lighting Load in Non-Domestic buildings is taken from Pout, et al. (2002) where the commercial and public sector energy consumption for lighting per unit floor area is given (see appendix 4). Table 20 reports the average constant lighting load taken from this data source.

Table 20. Average constant lighting load in domestic and non-domestic sector used in this work

Building type Average Constant Lighting Load(W/m2)

Offices 4.3

Retails 10.8

Warehouses 4.0

Dwellings 0.9

4.3.5 Average constant gain due to people in the building

Heat generated by occupants depends on number of persons per household and the amount of heat generated per person. Based on the BRE’s Housing Fact File the average number of people per dwelling for all building types was 2.34 in 2009 and it continues to decrease because of new constructions (Palmer & Cooper, 2011). The average metabolic heat gain from occupants is calculated based on data provided by The Engineering Tool Box (ETB, 2011) which is summarized in Table 21.

Table 21. Average Metabolic rate based on activities. Source: (ETB, 2011)

Degree of Activity Typical Application Average Metabolic rate - male adult

(W) Seated at rest Cinema, theatre, school 100

Seated, very light work Computer working 120

Office work Hotel reception, cashier 130

Standing, walking slowly Laboratory work 130

Moderate work Servant, hair dresser 160

Light bench work Mechanical production 220

Heavy work Athletics 430

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The average heat gain from people in different kind of buildings is calculated according to table 21 and the obtained value is reported in table 22. Notice that the occupancy factor of dwelling is 2.34 (Palmer & Cooper, 2011).

Table 22. Average constant gain due to people by different dwelling types.

Building Type Average constant gain W/m2

Detached 0.52 Semi detached 0.86

Terraced 0.97 Flat 1.30

Bungalow 1.06 Offices 3.31 Retails 3.20

Warehouses 1.39

The only available data base regarding the occupancy factor of non-residential buildings is 2003 CBECS Detailed Tables published by US. Energy Information administration (CBECS, 2003). Based on this database, density of people in warehouses is 158m2/person. Since it has been the only source available this figure has been applied to the model.

A survey of a number of different office buildings with different densities of people in 1993 and 2000 was undertaken by Stanhope (2001). The results of the surveys showed the occupant density of 12 m2/person and 16 m2/person for city center offices and business parks respectively Stanhope (2001). It is in agreement with data taken from British council for offices where the occupant density is considered to be 11.8 m2/person (BCO, 2008). In the current work it is decided to consider the office hours between 07:00hr to 19:00hr, and 5 days a week, as suggested in DM (2012). Density of occupants in retails is taken from CIBSE (2006) where the heat gain in typical buildings is introduced. Based on this document the average constant heat gain due to people in retails is calculated. A rough estimation gives the occupants heat gain of 3.2 W/m2 in retails.

4.3.6 Average constant consumption of appliances

The growth in appliances’ energy use has been very sharp. It has tripled in less than 40 years. The annual rise seems to be slowing but it has been nearly 3% a year. Domestic appliances used less than 5% of entire energy in 1970; they now use approximately 12% (Palmer & Cooper, 2011). According to the BRE’s Housing Fact file, the total final energy use of appliances in the UK was 58.4 TWh in 2008 (Palmer & Cooper, 2011), which is considered to be the same net energy for the direct electricity. The ECCABS model requires the input to be given in W/m2. In national and international databases no data was found regarding the appliances energy use. But still by knowing the total energy use of appliances and total number of buildings (taken from Palmer & Cooper (2011)) it is possible to estimate the

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average use of appliances per m2. Constant consumption of appliances is calculated to be 293W per dwelling. (see appendix 4). Knowing the surface area of each dwelling type (presented in previous chapters) the constant consumption of appliances in W/m2 is calculated and presented in table 23. Regarding the NR sector, no data were found about the thermal gain or energy consumption of appliances in warehouses. In this master thesis it has been decided to estimate the energy consumption of appliances base on data given by the BRE’s non-domestic buildings fact file. Based on this document the total energy consumption of appliances in warehouses is 3,222 TWh per year and the total area of warehouses (presented in section 4.3) is approximately 1.226 × 108 m2, which gives the constant appliances use of 3 w/m2. The survey undertaken by Stanhope (2001) reports an appliances use of 5.36 W/m2 for all types of offices. The average constant consumption of appliances in retails is derived from CIBSE (2006), which gives 7.3 W/m2. Table 23 summarizes the appliances use for each building type.

Table 23 constant consumption of appliances considered in this work

Building Type Appliances use (W/m2)

Residential

Detached 2.4 Semi detached 3.9

Bungalow 4.9 Terraced 4.4

Flat 5.9 Other 4.2

Non-residential Retail 7.3 Office 5.36

Warehouse 3 The appliances use in the UK seems to be considerably higher than the amounts obtained by Benejam (2011) in Spain where the appliances use in the residential sector is 1.65 W/m2 and in offices and commercial sector it is 1.5 W/m2.

4.3.7 Hot water demand

The average amount of hot water consumption is assumed to be 103 litres per household per day for all dwelling types Johnston (2003). As the average number of occupants in dwellings is assumed to be 2.34 Palmer & Cooper (2011) then the hot water consumption would be 44 l/person per day. Thus the hot water demand is obtained by (equation 4).

Q = ρ × v × C� × ∆T Equation 4

Where: ρ is the density of water v is the volume of water

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C� is the specific heat capacity of water ∆T is the temperature difference The specific heat capacity and density of water are expected to equal 1.16Wh/kg°K and 1Kg/l respectively. It is assumed that the temperature of cold water entering the dwelling equals 10°C and is supplied to the hot water tap at 55°C. These assumptions result in a hot water net energy demand of 224W/household. Table 24 reports the hot water demand for each dwelling type in W/m2. Hot water energy consumption of non-dwellings is taken from Pout, et al. (2002). Table 24 contains information derived from this source.

Table 24. Hot water energy use obtained in this work

Building type Hot water energy consumption (w/m2)

Non-Domestic buildings Offices 1.3 Retails 1.6

warehouses 0.9

Domestic buildings

Semi detached 2.5 Terraced 2.8 detached 1.5

Flat 3.7 Bungalow 3.0

Other 2.6

4.3.8 Indoor temperature

In the UK according to the BRE’s housing fact file the average indoor temperature in 2008 has been 17.3°C and 14.8°C for centrally and non-centrally heated dwellings respectively (Palmer & Cooper, 2011). Indoor temperature in non-domestic buildings is taken from CIBSE (2006) in which recommended temperature ranges for buildings with different usages are provided for heating and cooling design. The author of this master thesis has assumed that all non-domestic buildings are operating according to these recommendations. Based on this guide internal temperature for offices is considered to be 21°C while it is 20°C and 16°C for retails and factories respectively. In this master thesis it has been assumed that warehouses are identical with factories from internal temperature point of view. The maximum allowed internal temperature is assumed to be 26°C while the tint to open windows (natural ventilation) is considered to be 24°C. Table 25 reports the indoor temperature for each building type considered in this work.

27

Table 25. Indoor temprature in differnt building type applied in this m aster thesis

Building type Indoor temprature (°C)

Residential Buildings

Centrally heated

17.3

Non-centrally Heated

14.8

Non-residential buildings

Retails 20

Offices 21

Warehouses 16

4.3.9 Sanitary ventilation flow rate

Part F of building regulations takes care of ventilation systems. It is assumed that buildings constructed in each period have satisfied the minimum requirements of the building regulations. According to approved documents part F in 2006 and 2010 it is needed for houses to have minimum ventilation flow rate of 0.3 l/s per m2 in dwellings. In 1990 almost no house was equipped with ventilation system (DECC, 2011). Regarding the non-residential sector 10 l/s per person for non-domestic buildings (Part F, 2010) . The ventilation rates in warehouses are given to be 3-6 ACH (Air change per hour)3 (Tombling, 2004). Reliable data about the number of non domestic buildings equipped with ventilation system were not found. Thus it has been assumed that all non-domestic buildings built after 1985 have a mechanical ventilation system and heat recovery system has been installed after 1990. Table 26 reports data on ventilation rates which were introduced to the ECCABS model.

Table 26. Ventilation rates considered in this work for the different building types and construction years.

Building type Ventilation rate (l/s per m2)

Domestic buildings 0.3 l/s

Non domestic buildings offices 0.9

warehouses 1.6 retails 1.2

4.3.10 Natural ventilation rates

The infiltration rate that is corresponding to chimneys, fans and flues depends on the numbers and type of chimneys, fans and flues within the buildings (Johnston, 2003). Unfortunately, 3 Air changes per hour is a measure of how many times the air within a defined space (normally a room or house) is replaced.

28

comprehensive data on the average infiltration rate or on the average number of chimneys, fans and flues installed in the existing UK housing stock, is not available. Still, Johnston have made a number of assumptions based upon standard values for the infiltration due to chimneys, fans and flues found within BREDEM Version 9.60 (see Johnston (2003)). Johnston has assumed that for pre-1996 dwellings the infiltration rate due to chimneys, fans and flues is 100 m3/h. In the case of the post-1996 dwelling, the infiltration rate is considered to be equivalent to 20 m3/h. On the other hand, Part L of building regulation clearly mentions the maximum allowed infiltration rate. In order to apply these information one should assume that the buildings built in each period have an infiltration rate of equal or less than the amount required by legislations. In this master thesis work for the buildings built after introducing the approved documents infiltration rate is considered to be identical with legislation requirements and for older buildings it is taken from Johnston’s work. The infiltration rate of non-domestic buildings is taken from CIBSE (2006). Based upon this reference the infiltration rate in warehouses and offices is given to be 0.5 h-1 and 1 h-1 respectively. The natural ventilation rate in retails has been calculated based upon data given by CIBSE (2006). The weighted average infiltration rate for retails is 1 h-1.

4.3.11 Response capacity and maximum hourly capacity of the heating

system

It is assumed that the heating system is capable of supplying the required energy to satisfy the heat demand. Moreover the heating system is assumed to be capable of responding to any change in the demand, thus response capacity of the heating system and the maximum hourly capacity of the heating system are set to be high enough to ensure that the heat demand is fully satisfied.

4.3.12 Effective heat capacity of whole building

Effective internal heat capacity of the building, representing the thermal inertia of the building is found by summing the volumetric heat capacities of the internal layers of the building. These layers are the ones which are in direct contact with internal air, such as internal layers of exterior walls, internal walls and middle floors. Equation 5 which is taken from Mata & Kalagasidis (2009) is used to calculate the effective heat capacity of the building.

Tc = ∑ !� . #$� . %� . �� Equation 5

Where: &� is the density of the layer, (kg/m3) #$� is the specific heat capacity of the layer (J/kg K) %� is the area of the layer (m3) �� is the thickness of the layer (m) The sum should be done for all layers of each element. It starts from the internal surface and stops at the first insulating layer. The maximum thickness is 10 cm or the middle of the building element, whichever comes first.

29

In literature study no data were found on the typical heat capacity of buildings in the UK. Moreover to apply the above equation the thermal properties of the building materials are required. Construction materials are taken from the work undertaken by Smith (2009). It explains the typical construction materials in each time period for walls, ceiling and floor. (See appendix 2). It has been assumed that it represents both domestic and non-domestic building in the entire UK. Thermal properties of building materials are given in one of BRE’s publications by Clarke, et al. (1990). Appendix 2 presents the specific heat capacity calculated for different archetype buildings. Notice that in each time period the weighted average thermal heat capacity has been considered.

4.4 Quantification of the UK building stock

One of the input parameters to the ECCABS model is named ‘weight’. It represents the number of buildings in the country represented by each archetype building. This coefficient is used to extrapolate the results obtained for each representative building. In order to introduce this input parameter to the model it has been needed to estimate the total number of buildings in each category. Total number of buildings in each region and each time period is derived from the BRE’s domestic and non-domestic fact files (see appendix 1) , these two databases also include data regarding number of buildings by building type and heating system (it is explained in more details in chapter 5.1.3). Number of buildings in the North Ireland has been estimated by making the assumption that the total number of buildings in each region corresponds to the population of that region. The total number of domestic building in the UK has been estimated to be 26,080,000. And non-domestic buildings (retails, offices and warehouses) have been approximated to be 1,388,000 in 2010. For the non-domestic buildings the construction and demolition rates are assumed to be constant after 1994 which is 0.34%. BRE (1998) Table 27 reports number of buildings by time period and building type obtained in this master thesis. Table 27. Number of buildings by Type and time periods obtained in this work.

Construction periods

Bui

ldin

g T

ypes

Before 1985

1986-1990

1991-1995

1996-2002

2003-2006

2007-2010

Detached 3653316 205498 132431 214630 132431 164397 Semi detached 5587426 314291 202543 328259 202543 251431

Terraced 5768835 324495 209119 338917 209119 259595 Bungalow 2056323 108720 70109 113626 66469 87033

Flat 3868217 217584 140221 227256 140220 174067 Other 64466 3624 2335 3787 2335 2899 Retail 624074 22759 19087 27165 15418 15418 office 269477 28959 24132 34187 19306 19306

Warehouse 178006 20813 17527 24372 13967 13967

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4.5 Final energy demand

To transform the net energy consumption to the final energy demand, the heating system efficiency and in particular the boiler efficiency is required. Moreover the different fuel shares need to be taken into account due to a few reasons. Different fuels have corresponding different associated carbon emissions and different prices, so the choice of fuel for heating, makes a big variation to energy costs for an individual household and entire country as well. In this chapter the fuel use in buildings will be analyzed and the average boiler efficiency in buildings will be introduced. Note that since buildings with central and non-central heating systems have different patterns in fuel use Palmer & Cooper (2011) they are investigated separately.

4.5.1 Fuel use in buildings without central heating

Significant changes in the fuels used for heating dwellings without central heating (which are just 4-5% of dwellings Palmer & Cooper (2011)) have taken place since 1970. Solid fuel has been replaced by electricity and gas Palmer & Cooper (2011). Figure 4 shows the trend in fuel used in homes by time. By 2008, the share of solid fuel had decreased to less than 8% while electricity had increased to 40% and gas to 50% of households with no central heating. In this period, use of oil dropped from under 4% to zero.

Figure 4. Fuel share in non-centrally heated dwellings. Source: (Palmer & Cooper, 2011)

Fuel share of the non-centrally heated dwellings has been introduced to the model based on figure 4 and explanations given above. Table 28 reports the value of fuel shares introduced to the model. Table 28. Fuel shares for non-centrally heated dwellings (Built before 1985)

Type of fuel Portion (%) Gas 50

Electricity 40 Others 10

Oil 0

31

4.5.2 Fuel use in buildings with central heating

During the last 40 years the share of different fuels in centrally heated dwellings has experienced significant changes. Solid fuel, electricity and oil have been replaced by gas. Today gas is the main fuel for heating in homes with central heating (Palmer & Cooper, 2011). As Figure 5 shows, in 2008, share of solid fuel was less than 1%, electricity accounted for just 8%, while oil use had decreased to 4%. At the same time the proportion of households using gas for their central heating had increased to 85%. Notice that gas central heating – and especially condensing gas boilers – made average heating systems much more efficient (Palmer & Cooper, 2011).

Figure 5. Fuel share in centrally heated dwellings. Source: (Palmer & Cooper, 2011)

Table 29 gives the fuel shares for centrally heated domestic buildings which are determined based on figure 5 and explanations above.

Table 29. Fuel shares for centrally heated dwelling

Fuel Portion (%)

Gas 85 Electricity 8

Others 3 Oil 4

4.5.3 Fuel use in non-domestic buildings

BRE’s non-domestic building energy fact file presents the fuel share in non-domestic buildings, but it is just until 1994 and disaggregated in fossil fuel and electricity (BRE, 1998). Department of Energy and Climate Change (DECC, 2011) reports how the fuels used within the non-residential sector have changed since 1970. Figure 6 shows that in recent years natural gas and electricity usage have increased up to 42% and 47% respectively of all energy used in Non-domestic sector (DECC, 2011). Note that natural gas is predominately used for space heating and heating water, whilst electricity is used for lighting, space heating and cooking purposes.

32

Figure 6. Fuel share in non-domestic buildings (DECC, 2011)(REF)

Final energy use in different types of non-residential buildings is presented by the Department of Energy and Climate Change (DECC, 2012), disaggregated in oil, natural gas, and electricity. In this master thesis work the fuel shares introduced to the ECCABS model are taken from data given by DECC. Table 30 summarizes the values inserted to the ECCABS model. Table 30. Fuel shares in non-domestic buildings for all construction periods used in this work

Building type Fuel shares (%) Oil Natural Gas Electricity4

Offices 10.7 65.9 23.4 Retails 3.7 50.1 46.2 Warehouses 23.7 59.6 16.7

4.5.4 Heating System efficiency

Data regarding the efficiency of the energy systems has been taken from a few sources. Efficiency of the newer boilers (2006 to 2010) is taken from The Boiler Efficiency Database website (BED, 2012) where the data regarding the boilers which are currently produced is recorded. The efficiencies for older oil and gas boilers were set as suggested by Johnston (2003). Johnston predicts the average heating system efficiency to be around 80% in 2009. In addition the BRE’s housing fact file contains some information regarding the heating system

4 Data on the Type of electricity heaters was missing. To be able to run the model it is considered as direct electricity.

33

efficiency where the average heating system efficiency of the entire existing stock is given to be 77%. Table 31 reports the values on boiler efficiencies taken from different sources. In this master thesis work the weighted average efficiency for all archetype buildings introduced to the model roughly equals to: 77% and 75% for gas and oil boilers respectively.

Table 31. Heating system efficiencies compiled from literature sources

Source Heating system efficiency (%) (BED, 2012) Gas boiler : 90 Oil boiler : 93.3

(Johnston, 2003) ≈80 (Palmer & Cooper, 2011) 77

The efficiency of the solid fuel boilers is taken from Clinch, et al. (2001) where it is assumed to be 65%. The efficiency of direct electricity heating is assumed to be 100%.

5 Results

5.1 Description of the UK building stock through archetype buildings

This section presents the characterization of the UK building stock as a result of applying the methodology explained in section 4. These results are reported for: segmentation, characterization, and quantification.

5.1.1 Segmentation

This section reports the distribution of the UK building stocks for each category that is applied in the segmentation which are: building type, climate, period of construction, and type of heating system. Building type Residential buildings are classified into six types: detached, semidetached, terraced, flats, bungalow, and others. Figure 7 shows the distribution of number of existing buildings in year 2010 for domestic sector and 2009 for non-domestic sector by building typology.

34

Figure 7. Distribution of the number and surface area the of existing buildings by type obtained in this thesis work

Climate zone Figure 8 illustrates the distribution of the existing building stock in 2009 for non-domestic and 2010 for domestic sector by climate zone. Climate zones are numbered based on figure 2.

Figure 8. Distribution of the number and surface area of existing buildings by climate zone obtained in this thesis work

35

Period of construction Distribution of the existing stock in 2009 and 2010 for non-domestic and domestic sector by period of construction is presented by figure 9.

Figure 9. Distribution of the number and surface area of existing buildings by time of construction obtained in this

thesis work

Type of heating system Figure 10 shows the distribution of existing buildings (Both residential and non-residential sector) by type of heating system in 2010.

Figure 10. Distribution of the number of existing buildings by type of heating system obtained in this thesis work

5.1.2 Characterisation

The physical and technical characteristics considered for the archetype buildings in this master thesis work are reported in this section. The values presented in this section are determined based on the explanations of section 4.3.

36

Residential buildings The average surface area for residential buildings is reported in table 32. A weighted average residential building has a ventilation rate of 0.3 l/s/m2 and average heat transfer coefficients of 0.72W/m2K. The ceiling height for dwellings is 2.5 m and they typically have a rectangular floor plan with an aspect ratio of 1.5. The average natural ventilation rate for dwellings is considered to be 0.23 l/s/m2. The corresponding values for all building types are given in table 32. Table 32.Average surface area of residential buildings

Building type Average surface area(m2)

External surface area(m2)

Windows area(m2)

Detached 150 430 22 Semi detached 90 247 15

Flat 60 53 7 Bungalow 73 233 14 Terraced 80 196 12 Others 85 267 8

Non-residential buildings In this chapter the characteristics and technical properties of non-residential buildings obtained in this master thesis work are presented. Offices The average floor area, heat transfer coefficient and external surface area of offices constructed in different time periods which are considered in the current work are reported by table 33. The sanitary ventilation rate for the offices built after 1985 is considered equal to 0.85 l/s/m2. The offices contracted before 1985 are assumed not to have a mechanical ventilation system. A weighted average office in the UK has the floor surface area of 290 m2, Heat transfer coefficient of 0.86 W/m2°K and external surface area of 376 m2.

Table 33. Physical and thermal properties of offices obtained in this master thesis

Before 1985

1986-1990

1991-1995

1996-2002

2003-2006

2007-2010

Floor area(m2) 227 428 423 423 423 423

Heat transfer coefficient(W/m2°K)

0.92 0.79 0.79 0.70 0.70 0.70

External surface area(m2)

380 639 638 638 638 638

Retails The average floor area, heat transfer coefficient and external surface area of retails constructed in different time periods which are considered in the current work are reported in table 34. The ventilation rate for the retails that are constructed after 1985 is equal to 1.2

37

l/s/m2. The retails built before 1985 are assumed not to have mechanical ventilation system. A weighted average retail building in the UK has the floor surface area of 187 m2, heat transfer coefficient of 1.09 W/m2°K and external surface area of 510 m2. Table 34. Physical and thermal properties of retails obtained in this master thesis

Before 1985

1986-1990

1991-1995

1996-2002

2003-2006

2007-2010

Floor area(m2) 143 479 463 463 463 463

Heat transfer coefficient(W/m2°K)

1.16 0.99 0.8 0.6 0.60 0.60

External surface area(m2)

408 1181 1145 1145 1145 1145

Warehouses Table 35 presents values regarding the floor area, heat transfer coefficient and external surface area of warehouses constructed in different time periods which are considered in the current work. The infiltration rate of warehouses is equal to 0.33 l/s/m2 and the sanitary ventilation rate for the warehouses built after 1985 is 1.6 l/s/m2. A weighted average warehouse building in the UK has the floor surface area of 658 m2, Heat transfer coefficient of 0.88 W/m2°K and external surface area of 1562 m2. Table 35. Physical and thermal properties of warehouses obtained in this master thesis

Before 1985

1986-1990

1991-1995

1996-2002

2003-2006

2007-2010

Floor area(m2) 630 680 793 793 793 793

Heat transfer coefficient(W/m2°K)

0.95 0.91 0.71 0.5 0.5 0.5

External surface area(m2)

1500 1610 1856 1856 1856 1856

5.1.3 Quantification

In this part the number of buildings in each category is presented. The UK building stock has grown from 19 million homes in 1974 to more than 25 million dwellings in 2008. Total number of buildings obtained in this master thesis work by different categories is reported in table 36. More detailed data in this regard is given in appendix 1.

38

Table 36. Quantification of the number of buildings in the UK existing building stock.

Domestic Non-domestic

Reg

ions

1 11208311

875075

2 6427377

359163

3 6156216

455861

4 2291591

154594

Tim

e pe

riod

Before 1985

20864000 1071558

1986-1990

1173600 72532

1991-1995

756320 60748

1996-2002

1225760 85726

2003-2006

756320 48691

2007-2010

938880 48691

After 2010

234720 11783

Typ

e

Detached 4433600 Retails 734204

Semi-detached

6780800

Bungalow 2347200 Offices 402205

Flat 4694400

Traced 7302400 Warehouses 273857

Other 78240

Hea

ting

Sys

tem

Central 1304174 69397

Non-central

24779320 1318544

TOTAL 26083495 1387942

5.2 Net energy demand of the UK building stock

The calculated net energy demand in 2010 in domestic sector is 472.7 TWh while the net energy consumed by non-domestic sector was 73.6 TWh in 2009. Due to lack of data on the net energy demand of the UK building stock it was not possible to compare these results to official databases.

39

Table 37 reports the calculated net energy use by end use and the annual specific energy use by end use.

Table 37. Net energy demand by end use obtained in this work

Electricity (TWh/y)

Hot water (TWh/y)

Heating (TWh/y)

Annual specific energy use (KWh/m2)

Residential sector 101.8 99.6 279.8 200.2

Non-residential sector 46.7 4.6 32.1 177.5

5.3 Final energy demand of the UK building stock

The total final energy demand in residential sector is 564.63 TWh in 2010. In non-residential sector the final energy equals 77.28 TWh in 2009. Table 38 reports the final energy use obtained in this work by fuel. Table 38. Final energy use by fuel and end use obtained in this work.

Residential sector (TWh/y)

Non-residential sector (TWh/y)

Oil 23.1 4.6

Gas 410.9 21.6

Electricity 133.1 51.0

Others 11.6 -

The annual specific energy use in domestic and non-domestic buildings is 247 KWh/y and 179 KWh/y respectively.

The final energy use obtained in this work is 1.6% higher than the value taken from DECC tables. Notice that the energy use for cooking has not been introduced to the model. The reason was that no data could find to allocate the corresponding energy demand for cooking to electricity or to natural gas (cooking energy demand is included in DECC tables). By adding 14.2 TWh of cooking final energy (see appendix 3) the deviation would be approximately 2.6%. The final energy use calculated by the ECCABS is 8% higher than the amount given by the Eurosts (Eurostat, 2012). The obtained energy demand for the non-domestic sector equals 77.28 TWh in 2009 which is approximately 3.2% lower than the value given by DECC tables. Table 39 reports the results

40

from the ECCABS model and DECC tables and shows the deviation between these two values. Table 39. Comparison of ECCABS outputs and DECC tables (final energy)

Subsector ECCABS(TWh) DECC(TWh) Deviation

Residential 578.83 563.7 2.6% Non-residential 77.28 79.88 3.2%

Figure 11 illustrates the final energy demand by fuel in 2010 both based on the ECCABS results and DECC tables. As the illustration shows the energy demand by fuel is almost identical in the created simulation model and the data presented by the Department of Energy and Climate Change. There is a deviation in oil consumption where the reason was not known.

Figure 11. Energy demand in domestic buildings by fuel based on ECCABS model and DECC tables

In non domestic sector the energy demand by fuel obtained from the ECCABS model is very closed to the figure presented by the DECC tables. As it is shown by figure 12 the share of different fuels in non-domestic buildings is very different from the fuel share in domestic buildings.

Figure 12. Energy demand in non-domestic buildings by fuel based on EABS model and DECC tables

The bar chart illustrated in figure 13 is a comparison of energy demand by different building types in non-domestic sector. As the figure shows the energy demand calculated by the ECCABS model very well mimics the reality in non-domestic premises.

41

Figure 13. Comparison of energy demand by sub-sectors in non-domestic buildings

6 Sensitivity analysis

Sensitivity analysis examines the changes in the model’s output variables based on minor changes in the model’s inputs (Saltelli, et al., 2000). Sensitivity analysis procedure helps to determine which variables could have larger effects on outputs. It is beneficial to apply when the model has a large number of input parameters and it is not obvious that which one needs to be included in improvement measures and which parameters could be ignored. The sensitivity analysis in this master thesis has been carried out based on the method presented by Firth, et al. (2009). According to this reference sensitivity analysis should be undertaken in the following steps:

o Each input parameter should be assigned a set value (ki) o Each input parameter faces a small change ∆ki while the other input parameters are

kept constant, i.e ±1% change in the input parameter (Saltelli, et al., 2000). o For each change in the input parameters the model is run o New output variables are used to calculate the sensitivity coefficients and normalized

sensitivity coefficients.

Sensitivity coefficients characterize the partial derivatives of output variables to input parameters and for a model with n output variables and m input parameters are given by:

)*�)+� ≈ *�(+�� ,+�)-*�(+�- ,+�)

.,+� Equation 6

i=1,…,n and j=1,…,m

Where:

/0 : ith output variable

10: jth input parameter

234254

: Sensitivity coefficient for output variable /0 and input parameter 10

42

/0(10 + Δ10) : The value of /0 when the input parameter 10 is increased by 810

In order to be able to make a comparison of sensitivity coefficients of input parameters with different units the normalized sensitivity coefficients must be calculated. Firth, et al. (2009) Suggests a formula to calculate this coefficient (Equation 8).

%�,: = ;�<� )*�

)+� i=1,…,n and j=1,…,m Equation 7

Sensitivity analysis was carried out on the ECCABS model by varying input parameters and recording the change in final energy consumption. Tables 40 and 41 show the results for the sensitivity analysis for ECCABS based on initial set values of the UK building stock.

The largest Si,j values shown in Tables 40 and 41 are all related to the input parameters which most influence space heating energy consumption in buildings. The indoor air temperature results in the most sensitivity (1.63 in residential sector and 1.57 in non-residential sector). This can be explained as a 1% increase in the indoor air temperature leads to a 1. 63 and 1.57 percent increase in the energy consumption of the buildings. This is considerably higher than the other Si,j values and suggests that the indoor air temperature is the key determinant energy use in buildings. The external surface and average U-value of the building have the second largest sensitivity. The negative sensitivity is because an increase in a number of parameters will make a decrease in space heating energy consumption. Table 40. Results for sensitivity analysis in residential building stock obtained in this work

Input parameters

Initial set value for the

input parameter(ki)

Overall change in the input

parameter (2 ,;�)

Overall change in the output parameter

Sensitivity coefficient

Normalized sensitivity coefficient

Sw 16.475 0.329 0.79 2.401 0.070 Ts 0.700 0.014 -0.79 -56.428 -0.070

Umean 1.160 0.023 9.820 426.956 0.882 Wc 0.700 0.014 -0.790 -56.428 -0.070 Wf 0.677 0.013 -0.790 -60.769 -0.073

Boiler eff. 0.760 0.015 -6.6 -440 -0.596 A 90.507 1.810 2.380 1.314 0.212 Ac 4.282 0.085 0.43 5.058 0.038 S 229.218 4.584 9.820 2.142 0.875

HyP 0.048 0.001 0.020 20.000 0.001 Oc 1.148 0.023 -0.320 -13.913 -0.028 Tv 24.000 0.48 0.000 0.000 0.000 Hw 5.038 0.100 2.300 23.000 0.206 Lc 0.992 0.019 0.000 0.000 0.000 Tc 22969664 459393.28 -0.17 0.000 0.000

Tmax 26 0.52 0.000 0.000 0.000 Tmin 17.188 0.343 18.260 53.236 1.63

Vc 0.036 0.001 0.120 120.000 0.007 Vcn 0.309 0.006 0.000 0.000 0.000

43

To give a clearer perspective on the results of the sensitivity analysis the behavior of input parameters with higher normalized sensitivity coefficients are illustrated by figure 14. To plot these behaviors the input parameters have varied by 1% , 5% , 10% , -1% , -5% , -10% and the output was recorded for each step.

Figure 14. Behavior of the input parameter with the highest normalized sensitivity coefficient in residential sector

obtained in this work

The results of sensitivity analysis of the non-residential sector are reported in table 41. There are a small number of differences observed compared to the residential sector. The input parameters with small normalized sensitivity coefficient are considered to be non-relevant because, a small change in these parameters does not have a big effect on the final energy use of the sector.

44

Table 41. Results for sensitivity analysis in non-residential building stock obtained in this work

Input parameters

Initial set value for the

input parameter(ki)

Overall change in the input

parameter (2 ,;�)

Overall change in the output parameter

Sensitivity coefficient

Normalized sensitivity coefficient

Sw 38.143 0.762 0.210 0.275 0.136 Ts 0.7 0.014 -0.070 -5.000 -0.045

Umean 0.974 0.019 1.100 57.894 0.729 Wc 0.700 0.014 -0.070 -5.000 -0.045 Wf 0.678 0.013 -0.070 -5.384 -0.048

Boiler eff. 0.83 0.016 -0.62 -38.75 -0.416 A 311.309 6.226 0.210 0.033 0.135 Ac 7.082 0.141 0.17 1.205 0.110 S 708.132 14.162 1.640 0.115 1.061

HyP 0.091 0.001 0.010 10.000 0.011 Oc 2.870 0.057 -0.110 -1.929 -0.071 Tv 24 0.48 0.010 0.020 0.006 Hw 1.308 0.026 0.070 2.692 0.045 Lc 7.313 0.146 0.180 1.132 0.116 Tc 96884447 1937688 -0.010 0.000 0.000

Tmax 26 0.52 0.000 0.000 0.000 Tmin 19.645 0.329 2.04 6.200 1.575

Vc 0.267 0.005 0.240 48.000 0.165 Vcn 0.463 0.009 0.000 0.000 0.000

As illustrated by figure 15 the minimum indoor temperature and external surface area and the mean U-value have the strongest effect on the energy use in the non-residential sector. The boiler efficiency and sanitary ventilation rate have modest effect on energy consumption.

Figure 15. Behaviour of the input parameter with the highest normalized sensitiity coefficient in non-residential sector

obtained in this work

45

Figure 16 illustrates the individual Si,j values for each building type for the input parameters shown in Tables 40 and 41. This shows, for each input parameter, the difference in sensitivity caused by building stock sector and construction period. In all cases the Si,j is broadly distributed and there are large variations between the building stock sectors and between older and newer premises.

Figure 16. Normalized sensitivity coefficients by premises type and age band for four selected input parameters

As it is seen in the figure 16 the Si,j values become smaller with increasing building age. This is due to the reason that modern buildings have increased insulation and higher air tightness. Therefore they will be less sensitive to the same change in an input parameter than older ones. Moreover it is concluded from the figure that the non-domestic buildings are more sensitive to the considered parameters. One way to interpret this is that the old non-domestic buildings could have a higher potential for efficiency improvements. Note that this conclusion is purely technical and does not consider the complexity of the applicability of the measures or their costs. Figure 17 compares the normalized sensitivity coefficient obtained for the different dwelling types and the selected input parameters. As the figure shows there are considerable differences between the built form types (notably detached houses and flats) and between older dwellings and newer ones. In many cases detached houses have the largest Si,j values. Terraced houses and flats have the lowest Si,j values.

Figure 17. Normalized sensitivity coefficient in domestic buildings

In the next part the focus is on normalized sensitivity coefficient for the Ucalculated (see figure 18). As the figure shows the most sensitive climate of U-value is climate zone 4 (Scotland). value would be the dwellings built before 1985 which are located in Scotland. Based on this illustration the buildings in moderate and waU-value. Bear in mind that this conclusion is purely technical ancomplexity of the applicability of the measures or their costs.

Figure 18. Normalized sensitivity coefficient

46

. Normalized sensitivity coefficient in domestic buildings

the focus is on the average U-value of dwellings. Thetivity coefficient for the U-value in all considered climate zones has been

). As the figure shows the most sensitive climate zonevalue is climate zone 4 (Scotland). Accordingly the best choice for improving th

value would be the dwellings built before 1985 which are located in Scotland. Based on this the buildings in moderate and warmer locations are less sensitive to the average

r in mind that this conclusion is purely technical and does not consider the complexity of the applicability of the measures or their costs.

. Normalized sensitivity coefficient for the U-value in different climate zones

. The weighted average value in all considered climate zones has been

zone to the changes choice for improving the U-

value would be the dwellings built before 1985 which are located in Scotland. Based on this er locations are less sensitive to the average

d does not consider the

in different climate zones

47

7 Discussion

7.1 On the description of the UK building stock

A brief discussion on the potential factors which could have affected the results will be presented in this chapter. The most likely factor affecting the accuracy of the outputs might have been ‘data lacking’, ‘assumptions’, and ‘estimations’ in introducing the input parameters. These factors are discussed in the same order as the previous chapters which are: segmentation, characterization, quantification, and the energy use.

7.1.1 Segmentation

Due to the form of available data in the UK, the author has considered each single apartment as an archetype (not the whole block of apartments). Therefore the external surface area and effective heat capacity of this type of dwellings was calculated based on the assumption that each block includes 4 single units. It is probable to affect the energy demand in flats. The same limitation exists in traced dwellings where the accurate data about the number of dwelling in each block is lacking.

7.1.2 Characterization

Indoor temperature has a large effect on the energy demand of buildings (based on the results of sensitivity analysis) however accurate data on this parameter for each building type was lacking and only an average figure for the whole buildings is provided by the BRE’s housing energy fact file and a limited number of recommendations in CIBSE guides. Data about the number of people in different archetype building was lacking and the author has decided to consider the average occupancy ratio given by the BRE’s housing fact file for all the archetype dwellings.

7.1.3 Quantification

The number of non-domestic buildings has been estimated based on the growing rate during the recent years where the data has been available; however, it might not have been very accurate and potentially could affect the final results.

7.1.4 Final energy demand

A weighted average value for heating system efficiency is considered for all archetypes and as this input parameter affects the final energy use. This might be a possible reason for the differences between the energy demand calculated by the ECCABS model and the official databases.

48

7.2 On the methodology and model

Here a discussion on the methodology and the model used in this work will be presented. The weather data introduced to the model has been assumed to be representative of the whole climate zone which might affect the inaccuracy of outputs since the locations chosen might not be optimal representative for the entire climate zone, although they were chosen on a population basis. Energy demand for cooking was problematic to allocate for gas and electricity fuels. Therefore it was impossible to introduce this parameter to the ECCABS model. Because if it was considered as ‘appliances’ then the model would calculate the whole cooking energy demand as electrical energy. This factor needs to be introduced to the model and it can be considered as future improvement.

7.3 Comparison between this work and previous work within the

pathways project

A comparison of the earlier studies applied to the Spanish and French building stocks with this master thesis is presented in this chapter. This comparison is done because these studies have been developed with the same methodology and within the same context, i.e., the pathways project. Table 42 reports the differences and similarities between this study and previous studies. Both this study and previous studies have used the ECCABS model and all have succeeded in describing the residential and non-residential sector by means of the archetype buildings. Table 42. Comparison of this study with previous studies don in Pathways Project.

Comments regarding:

Spanish building sector (Benejam, 2011)

French building sector (Portella, 2012)

The UK building stock in this work

Methodology to define archetype buildings Segmentation Possible to define archetype

buildings following the pathways methodology. No category for different ventilation systems and different heating systems.

Possible to define archetype buildings following the pathways methodology. New category for energy source used for heating.

Possible to define archetype buildings following the pathways methodology. New category for type of heating systems.

Characterization Main difficulties are linked to the non-residential sector. Lack of data regarding the efficiencies of energy systems.

Main difficulties are related to non-residential sector. No data available on Tc and mechanical ventilation.

Main difficulties are related to non-residential sector.

Quantification No major problem Difficult to obtain data on the number of residential and non-residential buildings

No major problem

Modeling methodology Sensitivity Entire building stock Residential and non- Residential and non-

49

analysis made for residential sector separately

residential sector separately and also for residential subsectors.

Parameters U, Hw, Tc Sw, Vc, Vcn, Ac, Lc, Oc, Wc, fuel

efficiencies

U, Hw, Tc Sw, Vc, Vcn, Ac, Lc

Saw, Ts, Umean, Wc, Wf, Boiler eff, A, Ac, S, HyP, Oc, Tv, Hw, Lc, Tc, Tmax, Tmin,

Vc, Vcn Relevant

parameters U, Hw, Tc, Sw,fuel

efficiencies U, Hw, Vc, Sw, Wc(Residential)

U,Lc,Vc,Hw(non-residential)

Trmin, S, U, A, Boiler Eff.(residential)

Trmin, Boiler Eff, U, S, Vc (non-residential)

8 Conclusion

To sum up for this master thesis work a number of conclusions can be mentioned, both about the methodology and suitability of the ECCABS model for the UK building stock. Above all and answering to the first research question stated in the aim of this Master thesis work, it was possible to describe the UK building stock through archetypes buildings. This could be done mostly following the methodology developed within Pathways project. The required data to complete the model could be found be following the developed methodology. For instance international data bases such as Eurostat was used to obtain the required data to compare the final results. Characterization and quantification of the archetype building has been possible in this master thesis. However a number of parameters have been derived by making assumptions, including the hours of use of the lighting systems and the U-value of the old buildings.

Data on efficiencies of the energy systems for each archetype building in the UK was lacking while the modeled energy demand is proven to be sensitive to the values set for heating system efficiencies. Finally, it needs to be highlighted again that the scarcity of data on non-domestic buildings represents the major cause of the deviation between the ECCABS’s results and the statistics extracted from DECC tables. Regarding the second research question stated in the aim of this MSc work, i.e. the suitability of the EABS model for the UK building stock it can be concluded:

The EABS model is well suitable to be applied to the UK building stock because the results are close to the values provided by official databases. Furthermore as the current model is validated and the outputs are almost accurate it is very suitable to be used to check the effect of possible energy efficiency measures and potential energy savings in each sector and the entire country.

50

Sensitivity analysis shows that the indoor air temperature and the U-value of buildings are the most important factors that could affect the total energy demand. In the previous work in pathway projets undertaken by (Benejam, 2011) the parameters with highest influence on the final energy demand has been U-value and Sw, and hot water demand. It seems to be essential to update the model to make it possible to include the cooking energy demand as it was problematic to introduce the cooking energy demand to the ECCABS. The climate data of selected cities was assumed to be representative of the entire climate zones. A methodology could be useful to build weather data files capable of describing the climate of each zone with more accuracy.

9 Further work

The work undertaken in this master thesis has focused on calculating the energy demand of the domestic and non-domestic building stock in the UK for a baseline year. Of course one of the first things to do would be to update the model according to the suggestions given in the conclusions. In addition, future work could use the description of the building stock and the obtained baseline energy demand as a basis for the assessment of the potential energy saving in the building stock in the UK. The economical assessment, .The assessment of the energy savings, associated CO2 emissions and cost can be done by using the ECCABS model. Rebound effects, and barriers to energy efficiency could also be studied as future research.

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55

11 Appendix1. Statistics

Population and Households (millions) Source: (Palmer & Cooper, 2011) Year Population/

Households

Mean Size

(Households)

1970 2.94

1971 2.92 2.91

1972 2.90

1973 2.88 2.83

1974 2.86

1975 2.84 2.78

1976 2.81

1977 2.79 2.71

1978 2.76

1979 2.74 2.67

1980 2.73

1981 2.70 2.70

1982 2.69

1983 2.67 2.64

1984 2.65 2.59

1985 2.63 2.56

1986 2.60 2.55

1987 2.58 2.55

1988 2.56 2.48

1989 2.53 2.51

1990 2.51 2.46

1991 2.50 2.48

1992 2.49 2.45

1993 2.48 2.44

1994 2.47 2.44

1995 2.46 2.40

1996 2.45

1997 2.44

1998 2.43 2.32

1999 2.42

2000 2.41 2.30

2001 2.40 2.33

2002 2.39

2003 2.38

2004 2.38

2005 2.37

2006 2.36 2.32

2007 2.36

2008 2.35

2009 2.34

56

Number of Households by Region (millions). Source (Palmer & Cooper, 2011)

Year South West South East London East West East Yorks & the North West North East

Midlands Midlands Humber

England Wales Scotland

1,981 1.65 2.66 2.63 1.77 1.87 1.42 1.83 2.56 0.98 17.36 1.03 1.88

1,982 1.66 2.69 2.63 1.79 1.88 1.42 1.84 2.56 0.98 17.45 1.03 1.90

1,983 1.69 2.72 2.64 1.81 1.89 1.44 1.85 2.57 0.99 17.59 1.03 1.91

1,984 1.72 2.76 2.65 1.84 1.91 1.45 1.86 2.58 0.99 17.76 1.04 1.93

1,985 1.74 2.80 2.66 1.87 1.93 1.47 1.87 2.60 1.00 17.94 1.05 1.95

1,986 1.77 2.84 2.68 1.90 1.94 1.49 1.89 2.61 1.00 18.13 1.07 1.96

1,987 1.81 2.88 2.69 1.93 1.97 1.52 1.90 2.63 1.01 18.34 1.08 1.98

1,988 1.84 2.93 2.70 1.96 1.99 1.54 1.92 2.65 1.02 18.55 1.10 2.00

1,989 1.87 2.96 2.73 1.98 2.01 1.56 1.95 2.68 1.03 18.78 1.11 2.01

1,990 1.88 3.00 2.77 2.01 2.03 1.58 1.97 2.70 1.04 18.97 1.12 2.03

1,991 1.9 3.03 2.80 2.03 2.05 1.60 1.99 2.72 1.05 19.17 1.11 2.04

1,992 13 3.05 2.80 2.05 2.06 1.62 2.00 2.73 1.05 19.28 1.12 2.06

1,993 1.94 3.07 2.80 2.07 2.07 1.63 2.01 2.75 1.06 19.39 1.13 2.08

1,994 1.96 3.10 2.81 2.08 2.08 1.64 2.02 2.75 1.06 19.49 1.14 2.09

1,995 1.98 3.13 2.82 2.11 2.09 1.66 2.02 2.77 1.06 19.63 1.15 2.11

1,996 1.99 3.15 2.84 2.13 2.10 1.67 2.03 2.77 1.06 19.76 1.16 2.13

1,997 2.01 3.18 2.86 2.15 2.11 1.68 2.03 2.78 1.07 19.87 1.17 2.14

1,998 2.03 3.21 2.88 2.17 2.12 1.69 2.04 2.79 1.07 20.00 1.18 2.15

1,999 2.05 3.24 2.93 2.19 2.13 1.71 2.04 2.80 1.07 20.16 1.19 2.17

2,000 2.07 3.27 2.98 2.22 2.14 1.72 2.05 2.81 1.07 20.34 1.20 2.18

2,001 2.09 3.29 3.04 2.24 2.15 1.74 2.07 2.83 1.08 20.52 1.21 2.20

2,002 2.11 3.31 3.07 2.26 2.17 1.76 2.09 2.84 1.08 20.69 1.22 2.21

2,003 2.13 3.34 3.09 2.28 2.18 1.77 2.10 2.86 1.08 20.83 1.24 2.23

2,004 2.15 3.35 3.11 2.30 2.19 1.79 2.12 2.88 1.09 20.97 1.25 2.25

2,005 2.17 3.38 3.15 2.32 2.20 1.81 2.14 2.89 1.09 21.17 1.26 2.27

2,006 2.19 3.41 3.18 2.35 2.21 1.83 2.16 2.91 1.10 21.34 1.27 2.29

2,007 2.22 3.44 3.21 2.37 2.23 1.85 2.18 2.92 1.11 21.53 1.28 2.31

2,008 2.24 3.48 3.24 2.41 2.24 1.87 2.20 2.94 1.11 21.73 1.30 2.33

2,009 2.27 3.52 3.28 2.44 2.26 1.89 2.23 2.96 1.12 21.96 1.31 2.35

%

Change

37.8% 32.3% 24.5% 37.7% 20.9% 33.6% 21.8% 15.6% 14.5% 26.5% 27.9% 24.9%

57

Housing Stock Distribution by Type (millions) Source: (Palmer & Cooper, 2011)

Year Semi Terraced Flat Detached Bungalow Other detached Total

1,970 5.93 5.70 3.07 1.96 1.42 0.32 18.41

1,971 6.00 5.77 3.11 1.98 1.44 0.33 18.64

1,972 6.11 5.78 3.16 1.94 1.47 0.33 18.80

1,973 6.38 5.69 3.09 1.98 1.47 0.35 18.96

1,974 6.34 5.54 3.27 2.08 1.55 0.35 19.13

1,975 6.44 5.79 3.35 2.02 1.44 0.24 19.29

1,976 6.79 5.75 3.34 1.86 1.52 0.19 19.45

1,977 6.41 5.93 3.25 2.25 1.58 0.19 19.62

1,978 6.33 5.79 3.30 2.39 1.65 0.31 19.78

1,979 6.37 6.06 3.05 2.60 1.67 0.20 19.94

1,980 6.38 6.32 3.12 2.50 1.58 0.21 20.11

1,981 6.39 6.17 3.11 2.70 1.70 0.20 20.27

1,982 6.43 6.20 3.12 2.71 1.71 0.20 20.38

1,983 6.44 6.24 3.16 2.76 1.79 0.14 20.53

1,984 6.51 6.30 3.19 2.78 1.80 0.15 20.73

1,985 6.56 6.31 3.26 2.86 1.82 0.13 20.94

1,986 6.64 6.39 3.28 2.88 1.84 0.13 21.16

1,987 6.59 6.33 3.42 3.02 1.93 0.11 21.39

1,988 6.60 6.32 3.53 3.12 1.95 0.13 21.64

1,989 6.68 6.15 3.76 3.29 1.93 0.09 21.91

1,990 6.79 6.21 3.76 3.36 1.93 0.07 22.13

1,991 6.76 6.32 3.91 3.30 1.96 0.07 22.32

1,992 6.74 6.38 4.02 3.26 2.00 0.07 22.47

1,993 6.69 6.33 4.14 3.34 2.03 0.07 22.60

1,994 6.68 6.27 4.23 3.43 2.05 0.07 22.73

1,995 6.68 6.34 4.28 3.48 2.04 0.07 22.90

1,996 6.73 6.34 4.33 3.55 2.03 0.07 23.04

1,997 6.77 6.35 4.36 3.62 2.02 0.07 23.19

1,998 6.81 6.39 4.39 3.64 2.03 0.07 23.34

1,999 6.84 6.42 4.42 3.72 2.05 0.07 23.51

2,000 6.88 6.43 4.46 3.84 2.04 0.07 23.71

2,001 6.80 6.63 4.55 3.88 2.01 0.07 23.93

2,002 6.85 6.68 4.58 3.91 2.03 0.07 24.13

2,003 6.87 7.01 4.14 3.98 2.26 0.03 24.30

2,004 7.01 6.93 4.00 4.18 2.31 0.04 24.47

2,005 6.75 7.14 4.20 4.30 2.22 0.07 24.69

2,006 6.93 7.20 4.22 4.29 2.17 0.09 24.91

2,007 6.97 7.00 4.18 4.59 2.30 0.08 25.13

2,008 6.66 7.25 4.73 4.36 2.27 0.08 25.36

58

Housing Stock Distribution by Age (millions) Source : (Palmer & Cooper, 2011)

Year Pre-1918 1918-38 1939-59 1960-75 1976- Total

households

1,970 4.68 5.00 4.84 3.88 18.41

1,971 4.58 4.89 4.73 4.44 18.64

1,972 4.64 4.82 5.20 4.15 18.80

1,973 4.49 4.89 5.06 4.52 18.96

1,974 4.35 4.71 4.73 5.34 19.13

1,975 4.47 4.78 4.52 5.51 19.29

1,976 4.24 4.83 4.62 5.44 0.32 19.45

1,977 4.08 4.76 4.56 5.59 0.63 19.62

1,978 4.67 4.20 4.85 5.24 0.82 19.78

1,979 4.89 4.38 4.17 5.40 1.11 19.94

1,980 5.14 4.45 4.16 5.33 1.03 20.11

1,981 5.06 4.38 4.01 5.39 1.42 20.27

1,982 5.07 4.37 3.99 5.19 1.75 20.38

1,983 4.96 4.33 4.00 5.27 1.95 20.53

1,984 4.87 4.36 4.02 5.25 2.23 20.73

1,985 4.83 4.35 4.02 5.29 2.45 20.94

1,986 4.70 4.40 4.06 5.39 2.60 21.16

1,987 4.60 4.45 4.10 5.31 2.93 21.39

1,988 4.44 4.48 4.16 5.37 3.20 21.64

1,989 4.49 4.51 4.21 5.28 3.42 21.91

1,990 4.53 4.47 4.26 5.29 3.58 22.13

1,991 4.55 4.49 4.24 5.25 3.79 22.32

1,992 4.52 4.47 4.25 5.26 3.98 22.47

1,993 4.54 4.50 4.27 5.18 4.11 22.60

1,994 4.52 4.48 4.25 5.16 4.32 22.73

1,995 4.56 4.51 4.28 5.15 4.39 22.90

1,996 4.56 4.54 4.31 5.12 4.52 23.04

1,997 4.54 4.52 4.29 5.10 4.73 23.19

1,998 4.55 4.53 4.29 5.09 4.88 23.34

1,999 4.56 4.51 4.30 5.10 5.03 23.51

2,000 4.60 4.56 4.32 5.12 5.12 23.71

2,001 4.62 4.55 4.36 5.15 5.26 23.93

2,002 4.65 4.58 4.39 5.19 5.31 24.13

2,003 5.01 4.54 5.04 3.74 5.96 24.30

2,004 5.10 4.40 5.12 3.74 6.11 24.47

2,005 5.31 4.32 4.88 3.84 6.35 24.69

2,006 5.32 4.54 4.97 3.78 6.29 24.91

2,007 5.29 4.38 4.95 3.75 6.75 25.13

2,008 5.43 4.12 4.97 3.82 7.02 25.36

59

Non-domestic buildings by age band and type. Source: (BRE, 1998)

Age of premises

Office Retail Warehouse Number of premises

Area 1,000 m2

Average area

Number of premises

Area 1,000 m2

Average area

Number of premises

Area 1,000 m2

Average area

Pre 1985 239665 54465 227 558276 80108 143 158133 99748 630

1986-1990

24337 10421 428 19383 9286 479 18530 12605 680

1991-1994

12529 5312 423 9847 4566 463 7869 6243 793

1995 3100 - 423 2460 - 463 1967 - 793

1996-2002

21700 - 423 17220 - 463 13769 - 793

2003-2006

12400 - 423 9840 - 463 7868 - 793

2007-2010

12400 - 423 9840 - 463 7868 - 793

After 2010

6200 - 423 4920 - 463 2934 - 793

60

Households with Central Heating(millions) Source: (Palmer & Cooper, 2011) Year Without With central

central heating heating

Total households

1,970 5.78 12.62 18.41

1,971 6.41 12.22 18.64

1,972 7.01 11.79 18.80

1,973 7.82 11.15 18.96

1,974 8.79 10.34 19.13

1,975 9.46 9.83 19.29

1,976 10.01 9.44 19.45

1,977 10.54 9.07 19.62

1,978 10.70 9.08 19.78

1,979 11.26 8.69 19.94

1,980 11.57 8.53 20.11

1,981 11.91 8.36 20.27

1,982 12.40 7.97 20.38

1,983 13.44 7.08 20.53

1,984 14.00 6.73 20.73

1,985 14.72 6.22 20.94

1,986 15.27 5.89 21.16

1,987 15.91 5.48 21.39

1,988 16.35 5.30 21.64

1,989 17.07 4.83 21.91

1,990 17.56 4.56 22.13

1,991 18.27 4.05 22.32

1,992 18.64 3.83 22.47

1,993 19.05 3.55 22.60

1,994 19.49 3.24 22.73

1,995 19.91 2.98 22.90

1,996 20.05 3.00 23.04

1,997 20.34 2.84 23.19

1,998 20.79 2.55 23.34

1,999 20.97 2.55 23.51

2,000 21.12 2.59 23.71

2,001 21.58 2.35 23.93

2,002 21.79 2.34 24.13

2,003 22.99 1.31 24.30

2,004 23.39 1.08 24.47

2,005 23.74 0.94 24.69

2,006 24.13 0.78 24.91

2,007 24.54 0.58 25.13

2,008 24.32 1.04 25.36

61

12 Appendix 2. Data used to calculate the effective heat capacity Source : (Smith, 2009)

62

63

64

65

66

67

68

69

70

71

72

73

74

Thermal properties of construction materials. Source: (Clarke, et al., 1990)

Material = (Kg/m3) Cp(J/Kg°K)

Brick, aerated 0.30 1000 Clay tile, hollow 0.54105 1120

Conc., aerated, cellular 0.5 1300 Papyrus, insulating board 0.055 255

Gypsum plaster 0.512 1120 Straw slabs, compressed 0.085 260

Timber 0.072 480 Brick, inner leaf 0.62 1800 Brick, outer leaf 0.96 2000

Corkboard 0.043 130 Mineral fibre, 0.02740 48

Calculated effective heat capacity of buildings

Building type Construction period

Tc(J/K)

Semidetached B 1985 25259319 Semidetached A 1985 22230324

Traced B 1985 22743709 Traced A 1985 17957935

Flat B 1985 13995366 Flat A 1985 13368998

Detached B 1985 33636454 Detached A 1985 38355364 Bungalow B 1985 17625830 Bungalow A 1985 20445292

Other B 1985 20288742 Other A 1985 23898804 Retail B 1985 71829796 Retail 1985-1990 1,74E+08 Retail A 1990 1,69E+08 Offices B 1985 63588024 Offices 1985-1990 96548349 Offices A 1990 94665163

Warehouses B 1985 1,59E+08 Warehouses 1985-1990 1,5E+08 Warehouses A 1990 1,74E+08

75

13 Appendix 3. U-values

U-Values : England and Wales

Year Window Wall Roof Floor Source 1985 - 0,45 0,25 0,45 (Killip, 2005)

1991 3,3 0,45 0,25 0,45 (Killip, 2005)

1995 3,3 0,45 0,25 0,45 (DCLG, 1995)

2002 2,2 0,35 0,25 0,25 (DCLG, 2002)

2006 2,2 0,35 0,25 0,25 (DCLG, 2006)

2010 2 0,3 0,2 0,25 (DCLG, 2012)

U-Values : North Ireland

Year Window Wall Roof Floor Source 1998 3 0.45 0.25 0.35 (DFP, 1998) 2006 2.2 0.35 0.25 0.25 (DFP, 2006)

U-Values : Schotland

Year Window Wall Roof Floor Source 2005 2.2 0.3 0.25 0.25 (SG, 2005) 2006 2.2 0.3 0.25 0.25 (SG, 2006) 2007 2.2 0.3 0.2 0.25 (SG, 2007) 2008 2.2 0.3 0.2 0.25 (SG, 2008) 2009 2.2 0.3 0.2 0.25 (SG, 2009)

76

14 Appendix.4 Final Energy use

Household Energy Use for Water Heating (TWh) Source: (Palmer & Cooper, 2011) Year Water heating % household

energy

1,970 118.9 29.1%

1,971 120.3 30.4%

1,972 118.8 29.5%

1,973 118.5 28.4%

1,974 117.9 28.0%

1,975 116.7 28.4%

1,976 117.7 29.0%

1,977 117.4 27.9%

1,978 115.1 26.8%

1,979 114.4 24.8%

1,980 112.2 25.4%

1,981 111.6 25.4%

1,982 110.5 25.4%

1,983 108.9 25.1%

1,984 106.9 25.4%

1,985 107.3 23.0%

1,986 107.8 22.2%

1,987 104.7 21.7%

1,988 103.9 22.1%

1,989 102.8 23.0%

1,990 102.0 22.6%

1,991 101.2 20.4%

1,992 99.9 20.4%

1,993 99.2 19.6%

1,994 97.8 20.1%

1,995 96.6 20.4%

1,996 95.7 17.9%

1,997 94.8 19.1%

1,998 94.4 18.4%

1,999 92.7 18.1%

2,000 92.3 17.8%

2,001 90.5 16.9%

2,002 90.6 17.2%

2,003 90.7 16.9%

2,004 90.6 16.6%

2,005 89.2 16.8%

2,006 88.2 17.1%

2,007 86.7 17.4%

2,008 83.8 16.6%

77

Household Energy Usefor Lighting (TWh) Source: (Palmer & Cooper, 2011) Year Lighting % household

energy

1,970 10.4 2.5%

1,971 10.7 2.7%

1,972 11.0 2.7%

1,973 11.3 2.7%

1,974 11.6 2.8%

1,975 11.9 2.9%

1,976 12.2 3.0%

1,977 12.5 3.0%

1,978 12.7 3.0%

1,979 13.0 2.8%

1,980 13.3 3.0%

1,981 13.5 3.1%

1,982 13.8 3.2%

1,983 14.0 3.2%

1,984 14.2 3.4%

1,985 14.5 3.1%

1,986 14.7 3.0%

1,987 14.9 3.1%

1,988 15.1 3.2%

1,989 15.2 3.4%

1,990 15.3 3.4%

1,991 15.5 3.1%

1,992 15.6 3.2%

1,993 15.8 3.1%

1,994 15.9 3.3%

1,995 16.0 3.4%

1,996 16.2 3.0%

1,997 16.4 3.3%

1,998 16.5 3.2%

1,999 16.7 3.3%

2,000 16.9 3.3%

2,001 17.1 3.2%

2,002 17.3 3.3%

2,003 17.1 3.2%

2,004 17.0 3.1%

2,005 16.7 3.1%

2,006 16.9 3.3%

2,007 16.8 3.4%

2,008 16.5 3.3%

78

Household Energy Use for Appliances (TWh) Source: (Palmer & Cooper, 2011) Year Appliances % household

energy

1,970 19.1 4.7%

1,971 20.5 5.2%

1,972 22.0 5.5%

1,973 23.9 5.7%

1,974 25.7 6.1%

1,975 27.3 6.6%

1,976 28.5 7.0%

1,977 29.6 7.0%

1,978 30.6 7.1%

1,979 31.6 6.9%

1,980 32.6 7.4%

1,981 33.6 7.6%

1,982 34.7 8.0%

1,983 35.9 8.3%

1,984 37.4 8.9%

1,985 39.3 8.4%

1,986 41.0 8.4%

1,987 42.5 8.8%

1,988 43.7 9.3%

1,989 44.7 10.0%

1,990 45.4 10.0%

1,991 46.1 9.3%

1,992 46.7 9.6%

1,993 47.4 9.4%

1,994 47.9 9.8%

1,995 48.2 10.2%

1,996 48.7 9.1%

1,997 49.2 9.9%

1,998 49.7 9.7%

1,999 50.2 9.8%

2,000 50.6 9.7%

2,001 51.1 9.6%

2,002 52.2 9.9%

2,003 53.4 10.0%

2,004 55.0 10.0%

2,005 56.5 10.6%

2,006 58.3 11.3%

2,007 58.8 11.8%

2,008 58.4 11.6%

79

Household Energy Consumption by End Use (TWh) for Cooking Source: (Palmer & Cooper, 2011) Year Cooking % household

energy

1,970 24.4 6.0%

1,971 24.2 6.1%

1,972 24.0 6.0%

1,973 23.9 5.7%

1,974 23.8 5.6%

1,975 23.6 5.7%

1,976 23.3 5.7%

1,977 23.1 5.5%

1,978 22.8 5.3%

1,979 22.5 4.9%

1,980 22.1 5.0%

1,981 21.7 4.9%

1,982 21.4 4.9%

1,983 20.9 4.8%

1,984 20.3 4.8%

1,985 20.0 4.3%

1,986 19.3 4.0%

1,987 18.7 3.9%

1,988 18.2 3.9%

1,989 17.6 3.9%

1,990 17.0 3.8%

1,991 16.6 3.3%

1,992 16.2 3.3%

1,993 15.9 3.1%

1,994 15.6 3.2%

1,995 15.4 3.2%

1,996 15.2 2.8%

1,997 15.1 3.0%

1,998 15.0 2.9%

1,999 14.9 2.9%

2,000 14.7 2.8%

2,001 14.7 2.7%

2,002 14.7 2.8%

2,003 14.7 2.7%

2,004 14.6 2.7%

2,005 14.6 2.7%

2,006 14.7 2.8%

2,007 14.5 2.9%

2,008 14.2 2.8%

80

15 Appendix 5. DECC tabeles

Source: (DECC, 2012)

Domesic energt consumption by fuel

Fuel Thousand tonnes of oil equivalent

Oil 3,426 Gas 33,499

Electricity 10,205 Others 1,289

Non-Domestic energy consumption by fuel (Thousand tonnes of oil equivalent)

Electricity Natural Gas

Oil Solid fuel All

Commercial Offices

759 564 98 - 1,421

Retail 2,673 719 55 - 3,447

Warehouses 957 738 314 - 2,009

Non-domestic energy consumption by sub-sector

Building Type Total energy Use Commercial Offices 1,421

Retail 3,447 Warehouses 2,009