Download ENERGY USE IN THE EU BUILDING STOCK CASE STUDY: UK

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
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
1.2
1.3
1.4
2.
BACKGROUND ............................................................................................................................................ 1
CONTEXT OF THE REPORT ............................................................................................................................. 1
AIM OF THIS MASTER THESIS ......................................................................................................................... 2
STRUCTURE OF THE REPORT .......................................................................................................................... 2
DATA SOURCES ........................................................................................................................................ 3
2.1
2.1.1.
2.1.2
2.1.3
2.1.4
2.1.5
2.2
2.3
NATIONAL DATABASES ................................................................................................................................. 3
DEPARTMENT OF ENERGY AND CLIMATE CHANGE .......................................................................................... 3
BUILDING RESEARCH ESTABLISHMENT ............................................................................................................ 3
CHARTERED INSTITUTION OF BUILDING SERVICES ENGINEERS .............................................................. 4
ENVIRONMENTAL CHANGE INSTITUTE ............................................................................................................. 4
THE GOVERNMENT’S BOILER EFFICIENCY DATABASE.......................................................................................... 5
INTERNATIONAL DATABASES ......................................................................................................................... 5
LEGISLATIONS ............................................................................................................................................ 5
3.
EXISTING MODELING TOOLS FOR THE UK ................................................................................................ 6
4.
METHODOLOGY ....................................................................................................................................... 7
4.1
4.2
4.2.1
4.2.2
4.2.3
ABOUT THE ECCABS MODEL ........................................................................................................................ 8
SEGMENTATION METHODOLOGY ................................................................................................................... 9
BUILDING TYPE ........................................................................................................................................ 10
CONSTRUCTION PERIOD ............................................................................................................................. 12
CLIMATE ZONE ......................................................................................................................................... 13
DIFFUSE RADIATION ON HORIZONTAL SURFACE ........................................................................... 13
4.2.4
4.2.5
4.3
TYPE OF HEATING SYSTEM........................................................................................................................... 15
TOTAL NUMBER OF ARCHETYPES BASED ON THE DEVELOPED METHODOLOGY ........................................................ 15
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
4.5.2
4.5.3
4.5.4
5
FUEL USE IN BUILDINGS WITHOUT CENTRAL HEATING........................................................................................ 30
FUEL USE IN BUILDINGS WITH CENTRAL HEATING ............................................................................................. 31
FUEL USE IN NON-DOMESTIC BUILDINGS ........................................................................................................ 31
HEATING SYSTEM EFFICIENCY ...................................................................................................................... 32
RESULTS ................................................................................................................................................. 33
5.1
5.1.1
5.1.2
5.1.3
5.2
5.3
DESCRIPTION OF THE UK BUILDING STOCK THROUGH ARCHETYPE BUILDINGS ........................................................ 33
SEGMENTATION ....................................................................................................................................... 33
CHARACTERISATION .................................................................................................................................. 35
QUANTIFICATION ...................................................................................................................................... 37
NET ENERGY DEMAND OF THE UK BUILDING STOCK ......................................................................................... 38
FINAL ENERGY DEMAND OF THE UK BUILDING STOCK ....................................................................................... 39
6
SENSITIVITY ANALYSIS ........................................................................................................................... 41
7
DISCUSSION ........................................................................................................................................... 47
7.1
7.1.1
7.1.2
7.1.3
7.1.4
7.2
7.3
ON THE DESCRIPTION OF THE UK BUILDING STOCK .......................................................................................... 47
SEGMENTATION ....................................................................................................................................... 47
CHARACTERIZATION .................................................................................................................................. 47
QUANTIFICATION ...................................................................................................................................... 47
FINAL ENERGY DEMAN ............................................................................................................................... 47
ON THE METHODOLOGY AND MODEL ............................................................................................................ 48
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
vi
TECHNICAL ABBREVIATIONS
Location_no
A
Ac
HRec_eff
Hw
Weather region
Area of heat floor space
Average constant consumption of the appliances
Efficiency of the heat recovery system
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
T0
TC
Trmin
Ts
Tv
U
Vc
Wc
Total surface of window the building
Initial indoor temperature
Effective heat capacity of a heated space (whole
building)
Minimum indoor temperature
Coefficient of solar transmission of the window
Tint to start opening windows/nat ventilation
Mean U value of the building
Sanitary ventilation rate
Shading coefficient of the window
Vcn
Weight
Wf
ATT
Form
LS
Natural ventilation rate
Coefficient to scale up the type to the Building
Stock
Frame coefficient of the window
The attached character of the dwelling
A parameter which indicates the configuration of
the
Thebuilding
living space or heated floor area
Levels
HR
ρi
Cpi
Si
di
Number of floors of the building
Height under the roof
Density of the layer
Specific heat capacity of the layer
Area of the layer
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
1
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.
2
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 researchbased organization. They offer expertise in every aspect of the built environment and
3
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 nondomestic 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
o
o
o
o
o
o
o
o
o
o
o
Guide A: Environmental Design
Guide B: Heating, Ventilating, Air Conditioning and Refrigeration
Guide C: Reference Data
Guide D: Transportation systems in Buildings
Guide E: Fire Safety Engineering
Guide F: Energy Efficiency in Buildings
Guide G: Public Health Engineering
Guide H: Building Control Systems
Guide J: Weather, Solar and Illuminance Data
Guide K: Electricity in Buildings
Guide L: Sustainability
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
4
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.
5
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,
6
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 post1996)
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
Current
availability
Used only by the
developers
Used only by the
developer
Freely available
Open framework
Open structure
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.
7
SEGMENTATION
CHARACTERISATION
Definition of the amount of
archetype buildings
Physical characteristics of the buildings
and building services
SIMULATION
QUANTIFICATION
Calculation of net energy demand
with the ECCABS model
Number of buildings of each type
in the reference year
FINAL ENERGY
VALIDATION
Transferring the net energy to final
energy demand
Comparison of the results to
statistics
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 enduses; 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
8
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
9
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
Resulting amount of
archetypes
Segmentation Criteria
Model
BREHOMES
age group
1000
built
form
tenure type
the ownership
of central heating
Age
Pre-1996
post-1996
-
DECarb
age band
6 age bands
8064
CDEM
built
form type and dwelling
age
Johnston
47
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:
10
o
o
o
o
o
o
o
Effective heat capacity of the building (Tc)
Floor area
External surface area
Internal gains
Minimum desired indoor temperature (Trmin)
Maximum desired indoor temperature (Trmax)
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 'nonspecified 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 type
Building subsector
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
11
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
End terrace
Mid terrace
Semi-detached
Detached
Converted apartment
Purpose built apartment
Temporary/unknown
Collins, et al. (2010)
End terrace
Mid terrace
Semi-dethatched
Detached
Converted apartment
Purpose built apartment
Firth, et al. (2009)
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
12
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
o
o
o
o
o
o
Before 1985
1986-1991
1992-1995
1996-2002
2003-2006
2007-2010
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
Time
Air temperature
Dew point temperature
Global radiation on horizontal surface
Diffuse radiation on horizontal surface
Normal direct radiation
Long wave radiation
Illuminance global
Illuminance diffuse
Illuminance direct
Wind direction
Wind speed
13
Unit
S
°C
°C
W/m2
W/m2
W/m2
W/m2
Lux
Lux
Lux
Deka degrees
m/s
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
London
Birmingham
Newcastle
Glasgow
Climate Number
1
2
3
4
14
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.
15
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
NonDomestic
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.
16
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
Detached
Semi-detached
Traced
Flat
bungalow
Other
Average floor area(m2)
150
90
80
60
73
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.
17
Table 10. Floor area of non-dwellings (m2) considered in this work for the different building types and construction
periods
Building type
Retails
Offices
Warehouses
Before 1985
143
227
630
1986-1990
479
428
680
After 1990
463
423
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
Detached
Semi-detached
Terraced
Bungalow
λ
10.43
10.9
6.05
5.75
µ
0.10
0.08
0.12
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
18
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
1986-1990
1991-1995
1996-2002
2003-2006
2006-2010
0.0801 TFA1 + 5.580
0.0510 TFA + 4.554
0.0813 TFA + 3.744
0.1148 TFA + 0.392
0.1148 TFA + 0.392
0.1148 TFA + 0.392
Smith (2009) suggests a number of ratios to calculate the windows surface area in nondomestic 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
Before 1965
1966-1984
1985-1995
After 1996
Window Wall ratio
set to 10% of floor area
33%
35%
40%
Table 15 lists the methods used to calculate the window surface area for different building types.
1 TFA is the total floor area
19
Table 15. Methods used to calculate thw windows surface area for
all buildings types in this work
Method used to calculate
windows surface
Building type
Detached
Semidetached
The method presented by
Chapman (1994)
Bungalow
Terraced
The method presented by
Garston (2009)
Flats
The method presented by
Non- domestic buildings
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 ×
Where:
ATT
Form
A
Levels
HR
Sw
× (Level× HR) - Sw
Equation 2
is the attached character of the dwelling
is a parameter which indicates the configuration of the building
is the living space or heated floor area
is the number of floors of the building
is the height under the roof (2.5 m)
is the window area
The attached character of the dwelling (ATT) can be taken from Table 16 according to the 3CL method.
2
French Environment and Energy Management Agency (ADEME) introduces algorithms from the 3-CL method For calculating end-use
energy consumption in dwellings.
20
Table 16. Detached factors
Building type
Detached
Semi detached
Terraced
ATT
1
0.7
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
Terraced
Detached
Semi detached
80
150
90
1
1
1
External walls
area obtained
from Chapman’s
method
36.5
125
67.7
External walls
area obtained
from 3DL’s
method
32.24
126.14
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.
External walls surface
(m2)
430
250
198
236
54
268
Dwelling type
Detached
Semidetached
Terraced
Bungalow
Flat
Others
21
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
o
o
o
Average U-vale of walls : 1.36 (W/m2K)
Average U-vale of floor : 0.51(W/m2K)
Average U-vale of roof : 1(W/m2K)
Average U-vale of windows: 5.7(W/m2K)
Table 19. U-value of dwellings built befor 1985
U-value (W/m2K)
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
Walls: 0.3999
(Collins, et al., 2010)
Based on average for historic group UK Floor : 0.4577
stock built between 1981 and 1996
Roof : 0.1416
Window : 2.3967
Wall : 1.2
(Firth, et al., 2009)
U-values for the1945 to 1964 semi- Roof : 0.44
detached house archetype
Source
(Johnston, 2003)
22
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
Offices
Average Constant Lighting
Load(W/m2)
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
Seated at rest
Seated, very light work
Office work
Standing, walking slowly
Moderate work
Light bench work
Heavy work
Cinema, theatre, school
Computer working
Hotel reception, cashier
Laboratory work
Servant, hair dresser
Mechanical production
Athletics
23
Average Metabolic rate male adult
(W)
100
120
130
130
160
220
430
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
0.52
0.86
0.97
1.30
1.06
3.31
3.20
1.39
Detached
Semi detached
Terraced
Flat
Bungalow
Offices
Retails
Warehouses
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
24
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
Appliances use (W/m2)
2.4
3.9
4.9
4.4
5.9
4.2
7.3
5.36
3
Building Type
Detached
Semi detached
Bungalow
Terraced
Flat
Other
Retail
Office
Warehouse
Residential
Non-residential
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
Where:
ρ
v
is the density of water
is the volume of water
25
Equation 4
C
∆T
is the specific heat capacity of water
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
Non-Domestic buildings
Domestic buildings
Offices
Retails
warehouses
Semi detached
Terraced
detached
Flat
Bungalow
Other
Hot water energy consumption
(w/m2)
1.3
1.6
0.9
2.5
2.8
1.5
3.7
3.0
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.
26
Table 25. Indoor temprature in differnt building type applied in this master thesis
Building type
Residential
Buildings
Nonresidential
buildings
Indoor temprature (°C)
Centrally
heated
17.3
Non-centrally
Heated
14.8
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
Domestic buildings
Non domestic buildings
offices
warehouses
retails
Ventilation rate
(l/s per m2)
0.3 l/s
0.9
1.6
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.
27
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.
28
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.
Building Types
Table 27. Number of buildings by Type and time periods obtained in this work.
Detached
Semi detached
Terraced
Bungalow
Flat
Other
Retail
office
Warehouse
Before
1985
3653316
5587426
5768835
2056323
3868217
64466
624074
269477
178006
Construction periods
1986199119961990
1995
2002
205498
132431
214630
314291
202543
328259
324495
209119
338917
108720
70109
113626
217584
140221
227256
3624
2335
3787
22759
19087
27165
28959
24132
34187
20813
17527
24372
29
20032006
132431
202543
209119
66469
140220
2335
15418
19306
13967
20072010
164397
251431
259595
87033
174067
2899
15418
19306
13967
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
Gas
Electricity
Others
Oil
Portion (%)
50
40
10
0
30
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
Portion (%)
Fuel
Gas
Electricity
Others
Oil
85
8
3
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.
31
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
Offices
Retails
Warehouses
Fuel shares (%)
Natural Gas
65.9
50.1
59.6
Oil
10.7
3.7
23.7
Electricity4
23.4
46.2
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.
32
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
(BED, 2012)
(Johnston, 2003)
(Palmer & Cooper, 2011)
Heating system efficiency (%)
Gas boiler : 90 Oil boiler : 93.3
≈80
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.
33
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
34
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.
35
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
Detached
Semi detached
Flat
Bungalow
Terraced
Others
Average surface
area(m2)
150
90
60
73
80
85
Windows area(m2)
External surface
area(m2)
430
247
53
233
196
267
22
15
7
14
12
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
Floor area(m2)
Heat transfer
coefficient(W/m2°K)
External surface
area(m2)
Before
1985
19861990
19911995
19962002
20032006
20072010
227
428
423
423
423
423
0.92
0.79
0.79
0.70
0.70
0.70
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
36
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
19861990
19911995
19962002
20032006
20072010
143
479
463
463
463
463
1.16
0.99
0.8
0.6
0.60
0.60
408
1181
1145
1145
1145
1145
Floor area(m2)
Heat transfer
coefficient(W/m2°K)
External surface
area(m2)
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
19861990
19911995
19962002
20032006
20072010
630
680
793
793
793
793
0.95
0.91
0.71
0.5
0.5
0.5
1500
1610
1856
1856
1856
1856
Floor area(m2)
Heat transfer
coefficient(W/m2°K)
External surface
area(m2)
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.
37
Table 36. Quantification of the number of buildings in the
UK existing building stock.
Regions
1
2
3
Time period
4
Before
1985
19861990
19911995
19962002
20032006
20072010
After
2010
Heating
System
Type
Detached
TOTAL
5.2
Domestic
Non-domestic
11208311
875075
6427377
359163
6156216
455861
2291591
154594
20864000
1071558
1173600
72532
756320
60748
1225760
85726
756320
48691
938880
48691
234720
11783
4433600
Retails
734204
Offices
402205
Warehouses
273857
Semidetached
6780800
Bungalow
2347200
Flat
4694400
Traced
7302400
Other
78240
Central
1304174
69397
Noncentral
24779320
1318544
26083495
1387942
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.
38
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
39
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
Non-residential
578.83
77.28
563.7
79.88
2.6%
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.
40
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:
)*
)+ ≈
* (+ ,+ )-* (+ - ,+ )
.,+ i=1,…,n
and
Equation 6
j=1,…,m
Where:
/0 : ith output variable
10 : jth input parameter
234
254
: Sensitivity coefficient for output variable /0 and input parameter 10
41
/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)
Sw
Ts
Umean
Wc
Wf
Boiler eff.
A
Ac
S
HyP
Oc
Tv
Hw
Lc
Tc
Tmax
Tmin
Vc
Vcn
16.475
0.700
1.160
0.700
0.677
0.760
90.507
4.282
229.218
0.048
1.148
24.000
5.038
0.992
22969664
26
17.188
0.036
0.309
Overall
change in
the input
parameter
(2 ,; )
0.329
0.014
0.023
0.014
0.013
0.015
1.810
0.085
4.584
0.001
0.023
0.48
0.100
0.019
459393.28
0.52
0.343
0.001
0.006
42
Overall
change in
the output
parameter
Sensitivity
coefficient
Normalized
sensitivity
coefficient
0.79
-0.79
9.820
-0.790
-0.790
-6.6
2.380
0.43
9.820
0.020
-0.320
0.000
2.300
0.000
-0.17
0.000
18.260
0.120
0.000
2.401
-56.428
426.956
-56.428
-60.769
-440
1.314
5.058
2.142
20.000
-13.913
0.000
23.000
0.000
0.000
0.000
53.236
120.000
0.000
0.070
-0.070
0.882
-0.070
-0.073
-0.596
0.212
0.038
0.875
0.001
-0.028
0.000
0.206
0.000
0.000
0.000
1.63
0.007
0.000
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.
43
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)
Sw
Ts
Umean
Wc
Wf
Boiler eff.
A
Ac
S
HyP
Oc
Tv
Hw
Lc
Tc
Tmax
Tmin
Vc
Vcn
38.143
0.7
0.974
0.700
0.678
0.83
311.309
7.082
708.132
0.091
2.870
24
1.308
7.313
96884447
26
19.645
0.267
0.463
Overall
change in
the input
parameter
(2 ,; )
0.762
0.014
0.019
0.014
0.013
0.016
6.226
0.141
14.162
0.001
0.057
0.48
0.026
0.146
1937688
0.52
0.329
0.005
0.009
Overall
change in
the output
parameter
Sensitivity
coefficient
Normalized
sensitivity
coefficient
0.210
-0.070
1.100
-0.070
-0.070
-0.62
0.210
0.17
1.640
0.010
-0.110
0.010
0.070
0.180
-0.010
0.000
2.04
0.240
0.000
0.275
-5.000
57.894
-5.000
-5.384
-38.75
0.033
1.205
0.115
10.000
-1.929
0.020
2.692
1.132
0.000
0.000
6.200
48.000
0.000
0.136
-0.045
0.729
-0.045
-0.048
-0.416
0.135
0.110
1.061
0.011
-0.071
0.006
0.045
0.116
0.000
0.000
1.575
0.165
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
44
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.
45
Figure 17.. Normalized sensitivity coefficient in domestic buildings
In the next part the focus is on the average U-value of dwellings.. The weighted average
normalized sensitivity
tivity coefficient for the U-value
U value in all considered climate zones has been
calculated (see figure 18).
). As the figure shows the most sensitive climate zone to the changes
of U-value
value is climate zone 4 (Scotland). Accordingly the best choice for improving the
th Uvalue would be the dwellings built before 1985 which are located in Scotland. Based on this
illustration the buildings in moderate and warmer
wa er locations are less sensitive to the average
U-value. Bearr in mind that this conclusion is purely technical and
and does not consider the
complexity of the applicability of the measures or their costs.
Figure 18.. Normalized sensitivity coefficient for the U-value in different climate zones
46
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.
47
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
Spanish building sector
regarding:
(Benejam, 2011)
Methodology to define archetype buildings
Possible to define archetype
Segmentation
buildings following the
pathways methodology. No
category for different
ventilation systems and
different heating systems.
Characterization Main difficulties are linked
to the non-residential sector.
Lack of data regarding the
efficiencies of energy
systems.
Quantification
Sensitivity
French building
sector (Portella, 2012)
Possible to define
archetype buildings
following the pathways
methodology. New
category for energy
source used for heating.
Main difficulties are
related to nonresidential sector. No
data available on Tc
and mechanical
ventilation.
No major problem
Difficult to obtain data
on the number of
residential and nonresidential buildings
Modeling methodology
Entire building stock
Residential and non48
The UK building
stock in this work
Possible to define
archetype buildings
following the pathways
methodology. New
category for type of
heating systems.
Main difficulties are
related to nonresidential sector.
No major problem
Residential and non-
residential sector
separately
analysis made for
Parameters
U, Hw, Tc Sw, Vc, Vcn,
Ac, Lc, Oc, Wc, fuel
efficiencies
U, Hw, Tc Sw, Vc,
Vcn, Ac, Lc
Relevant
parameters
U, Hw, Tc, Sw,fuel
efficiencies
U, Hw, Vc, Sw,
Wc(Residential)
U,Lc,Vc,Hw(nonresidential)
residential sector
separately and also for
residential subsectors.
Saw, Ts, Umean, Wc,
Wf, Boiler eff, A, Ac,
S, HyP, Oc, Tv, Hw,
Lc, Tc, Tmax, Tmin,
Vc, Vcn
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.
49
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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54
11 Appendix1. Statistics
Population and Households (millions) Source: (Palmer & Cooper, 2011)
Year
Mean Size
(Households)
1970
Population/
Households
2.94
1971
2.92
2.91
1972
2.90
1973
2.88
1974
2.86
1975
2.84
1976
2.81
1977
2.79
1978
2.76
1979
2.74
1980
2.73
1981
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
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
2007
2.36
2008
2.35
2009
2.34
2.83
2.78
2.71
2.67
2.70
2.32
2.32
55
Number of Households by Region (millions). Source (Palmer & Cooper, 2011)
Year
South West
South East
London
East
Midlands
West
Midlands
East
Humber
Yorks & the
North West North East
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%
56
Housing Stock Distribution by Type (millions) Source: (Palmer & Cooper, 2011)
Year
Semi
Terraced
Flat
Detached
Bungalow
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
57
Other detached
Total
Housing Stock Distribution by Age (millions) Source : (Palmer & Cooper, 2011)
Year
1976-
Total
households
Pre-1918
1918-38
1939-59
1960-75
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
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
19.29
58
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
19861990
19911994
1995
24337
10421
428
19383
9286
479
18530
12605
680
12529
5312
423
9847
4566
463
7869
6243
793
3100
-
423
2460
-
463
1967
-
793
19962002
20032006
20072010
After
2010
21700
-
423
17220
-
463
13769
-
793
12400
-
423
9840
-
463
7868
-
793
12400
-
423
9840
-
463
7868
-
793
6200
-
423
4920
-
463
2934
-
793
59
Households with Central Heating(millions)
Source: (Palmer & Cooper, 2011)
Year
Without
central heating
With central
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
60
12 Appendix 2. Data used to calculate the effective heat capacity
Source : (Smith, 2009)
61
62
63
64
65
66
67
68
69
70
71
72
73
Thermal properties of construction materials. Source: (Clarke, et al., 1990)
= (Kg/m3)
0.30
0.54105
0.5
0.055
0.512
0.085
0.072
0.62
0.96
0.043
0.02740
Material
Brick, aerated
Clay tile, hollow
Conc., aerated, cellular
Papyrus, insulating board
Gypsum plaster
Straw slabs, compressed
Timber
Brick, inner leaf
Brick, outer leaf
Corkboard
Mineral fibre,
Calculated effective heat capacity of buildings
Construction
period
B 1985
Semidetached
A 1985
Semidetached
B 1985
Traced
A 1985
Traced
B 1985
Flat
A 1985
Flat
B 1985
Detached
A 1985
Detached
B 1985
Bungalow
A 1985
Bungalow
B 1985
Other
A 1985
Other
B 1985
Retail
1985-1990
Retail
A 1990
Retail
B 1985
Offices
1985-1990
Offices
A 1990
Offices
B 1985
Warehouses
1985-1990
Warehouses
A 1990
Warehouses
Building type
Tc(J/K)
25259319
22230324
22743709
17957935
13995366
13368998
33636454
38355364
17625830
20445292
20288742
23898804
71829796
1,74E+08
1,69E+08
63588024
96548349
94665163
1,59E+08
1,5E+08
1,74E+08
74
Cp(J/Kg°K)
1000
1120
1300
255
1120
260
480
1800
2000
130
48
13 Appendix 3. U-values
U-Values : England and Wales
Year
1985
1991
1995
2002
2006
2010
Window
3,3
3,3
2,2
2,2
2
Wall
0,45
0,45
0,45
0,35
0,35
0,3
Roof
0,25
0,25
0,25
0,25
0,25
0,2
Floor
0,45
0,45
0,45
0,25
0,25
0,25
Source
(Killip, 2005)
(Killip, 2005)
(DCLG, 1995)
(DCLG, 2002)
(DCLG, 2006)
(DCLG, 2012)
U-Values : North Ireland
Year
1998
2006
Window
3
2.2
Wall
0.45
0.35
Roof
0.25
0.25
Floor
0.35
0.25
Source
(DFP, 1998)
(DFP, 2006)
Window
2.2
2.2
2.2
2.2
2.2
Wall
0.3
0.3
0.3
0.3
0.3
Roof
0.25
0.25
0.2
0.2
0.2
Floor
0.25
0.25
0.25
0.25
0.25
Source
(SG, 2005)
(SG, 2006)
(SG, 2007)
(SG, 2008)
(SG, 2009)
U-Values : Schotland
Year
2005
2006
2007
2008
2009
75
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%
76
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%
77
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%
78
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%
79
15 Appendix 5. DECC tabeles
Source: (DECC, 2012)
Domesic energt consumption by fuel
Fuel
Thousand tonnes of oil
equivalent
3,426
33,499
10,205
1,289
Oil
Gas
Electricity
Others
Non-Domestic energy consumption by fuel (Thousand tonnes of oil equivalent)
Electricity
Commercial
Offices
Retail
759
Natural
Gas
564
Oil
Solid fuel
All
98
-
1,421
2,673
719
55
-
3,447
Warehouses
957
738
314
-
2,009
Non-domestic energy consumption by sub-sector
Building Type
Commercial Offices
Retail
Warehouses
Total energy Use
1,421
3,447
2,009
80