Energy Use in the EU Building Stock

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CASE STUDY: UK

Examinr : Bahram Moshfegh (Linkoping University)

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

Reza Arababadi 2012

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Abstract

Previous studies in building energy assessmnet have made it clear that the largest potential energy efficiency improvements are conected to the retrofitting of existing buildings. But, lack of information about the building stock and associated modelling tools is one of the barriers to assessment of energy efficiency strategies in the building stocks. Therefore, a methodology has been developed to describe any building stock by the means of archetype buildings. The aim has been to assess the effects of energy saving measures. The model which is used for the building energy simulation is called: Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS). This model calculated the net energy demand aggregated in heating, cooling, lighting, hotwater and appliances.

This model has already been validated using the Swedish residential stock as a test case. The present work continues the development of the methodology by focusing on the UK building stock by discribing the UK building stock trough archetype buildings and their physical properties which are used as inputs to the ECCABS. In addition, this work seekes to check the adequacy of applying the ECCABS model to the UK building stock. The outputs which are the final energy use of the entire building stock are compared to data available in national and international sources.

The UK building stoch is described by a total of 252 archetype buildings. It is determined by considering nine building typologies, four climate zones, six periods of construction and two types of heating systems. The total final energy demand calculated by ECCABS for the residential sector is 578.83 TWh for the year 2010, which is 2.6 % higher than the statistics provided by the Department of Energy and Climate Change(DECC). In the non-residential sector the total final energy demand is 77.28 TWh for the year 2009, which is about 3.2% lower than the energy demand given by DECC. Potential reasons which could have affected the acuracy of the final resualts are discussed in this master thesis.

Keywords: archetype buildings, UK building stock, energy demand, bottom-up modelling, energy simulation

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Acknowledgements

I would like to express my gratitude to all those who gave me the possibility to complete this report. I am grateful to my examiner Prof. Bahram Moshfegh for his vital encouragement and guidence. This research project would not have been possible without his support.

I am deeply indebted to my supervisor Erika Mata Las Heras whose help, stimulating suggestions, knowledge and encouragement helped me in all the times of study and analysis of the project.

My special thanks to my family to whom this thesis is dedicated to. I have no suitable word that can fully describe their everlasting love for me. I cannot ask for more from my love ‘Atefeh’ who has been a constant source of love, concern, support and strength.

Reza Arababadi Nov. 2012

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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

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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

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TECHNICAL ABBREVIATIONS

Location_no Weather region

A Area of heat floor space

Ac Average constant consumption of the appliances

HRec_eff Efficiency of the heat recovery system

Hw Demand of hot water

HyP Consumption of the hydro pumps

Lc Average constant lighting load in the building

Oc Average constant gain due to people in the building

Pfh Heat losses of the fan

Ph Response capacity of the heating system

S Total external surfaces of the building

SFP Specific Fan Power

Sh Maximum hourly capacity of the heating system

Sw Total surface of window the building

T0 Initial indoor temperature

TC Effective heat capacity of a heated space (whole

building)

Trmin Minimum indoor temperature

Ts Coefficient of solar transmission of the window

Tv Tint to start opening windows/nat ventilation

U Mean U value of the building

Vc Sanitary ventilation rate

Wc Shading coefficient of the window

Vcn Natural ventilation rate

Weight Coefficient to scale up the type to the Building

Stock

Wf Frame coefficient of the window

ATT The attached character of the dwelling

Form A parameter which indicates the configuration of

the building

LS The living space or heated floor area

Levels Number of floors of the building

HR Height under the roof

ρi Density of the layer

Cpi Specific heat capacity of the layer

Si Area of the layer

di Thickness of the layer

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1. Introduction

1.1 Background

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

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

1.2 Context of the report

This master thesis is undertaken as a part of the project Pathways to Sustainable European Energy Systems (PSEES, 2012). This international project aims to evaluate and plan robust pathways, or bridging systems, towards a sustainable energy system in Europe. The Pathways project is a part of the Alliance for Global Sustainability (AGS). In AGS companies e.q. Ford, Du Pont and Vattenfall and academic institutes such as MIT (Massachusetts Institute of

Technology), ETH (Eidgenössische Technische Hochschule, Zurich), Tokyo University and

Chalmers University of Technology are involved and cooperate in order to find ways to a sustainable future. After a successful first phase of Pathways, the project continues into a second phase. The second phase started in January 2011 and will be running for three years. The areas of research in phase two are those for which there is a solid base in the methodology developed and for which it is believed that the Pathways research group has scientific excellence.

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

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

One of the models developed for the study of the building sector is a model named Energy Carbon and Cost Assessment of Building Stocks (ECCABS) (Mata & Kalagasidis, 2009) , which is a bottom-up model to assess energy-saving measures (ESM) and carbon dioxide

(CO2) mitigation strategies in building stocks. The model is based on a one-zone hourly heat

balance that calculates the net energy demand for a number of buildings representative of the building stock and an additional code for the input and output data. The model generates results in terms of delivered energy, associated CO2 emissions, and the costs of implementing different ESM. The results are extended to the entire building stock by means of weighting factors. Empirical and comparative validations of the heat-balance modelling of single buildings have been presented (Mata, et al., 2011). The building stock modelling has been validated against the current Swedish residential stock, for which the results of the modelling are in agreement with the statistical data (Mata & Kalagasidis, 2009). The model has also been used to investigate the Spanish building stock (i.e. residential and non-residential buildings) (Benejam, 2011).

1.3 Aim of this master thesis

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

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

o Does the already developed modeling methodology (within Pathway project) need to

be adapted to be applied for the UK?

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

1.4 Structure of the report

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

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

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

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

2. Data sources

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

2.1 National databases

2.1.1. Department of energy and climate change

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

o Save energy with the Green Deal and support vulnerable consumers

o Deliver secure energy on the way to a low carbon energy future

o Drive ambitious action on climate change at home and abroad (DECC, 2012)

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

2.1.2 Building Research Establishment

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

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

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

2.1.3 Chartered Institution of Building Services Engineers

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

o Guide A: Environmental Design

o Guide B: Heating, Ventilating, Air Conditioning and Refrigeration

o Guide C: Reference Data

o Guide D: Transportation systems in Buildings

o Guide E: Fire Safety Engineering

o Guide F: Energy Efficiency in Buildings

o Guide G: Public Health Engineering

o Guide H: Building Control Systems

o Guide J: Weather, Solar and Illuminance Data

o Guide K: Electricity in Buildings

o Guide L: Sustainability

o Guide M: Maintenance Engineering and Management

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

2.1.4 Environmental Change Institute

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

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

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

2.1.5 The Government’s Boiler Efficiency Database

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

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

2.2 International Databases

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

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

2.3 Legislations

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

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

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

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

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

Building legislations Title Related region

Part L Conservation of fuel and power England and Wales

Technical booklet F Conservation of fuel and power North Ireland

Technical handbook Section 6 Energy Scotland

3. Existing modeling tools for the UK

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

o The Building Research Establishment’s Housing Model for Energy Studies

(BREHOMES) developed by Shorrock and Dunster (Shorrock & Dunster, 1997)

o The Johnston model developed by Johnston (2003)

o The UK Carbon Domestic Model (UKDCM) developed by Boardman, et al. (2005) as

part of the 40% House project

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

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

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

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

Table 2. Comparative analysis of previously developed models. Source: (Kavgic, et al., 2010)

Name BREHOMES Johnston UKDCM DECarb CDEM

Developer Building Research Establishment (BRE) PhD thesis (Leeds University) Environmental Change Institute (ECI), Oxford University University of Bath, University of Manchester Department of Civil and Building Engineering, Loughborough University, Loughborough, UK Year Early 1990s 2003 2006 2007 2009 Level of disaggregation 1000 dwelling types (defined by

age group, built form, tenure type and the ownership of central heating)

Two dwelling types (pre- and post-

1996) 20000 dwelling types by 2050 8064 unique combinations for 6 age bands 47 house archetypes, derived from unique

combinations of built form type and

dwelling age

Level of data input Requirement Medium (national statistics) Medium (national statistics) Medium (national statistics)

Low (defaults from national statistics)

Medium (national statistics)

Application Policy advice tool

(used by DEFRA) Policy advice tool

Policy advice tool

(Oxford) Policy advice tool Policy advice tool

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.

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

4.1 About the ECCABS model

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

In addition, the model aims to:

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

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

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

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

SEGMENTATION Definition of the amount of

archetype buildings

CHARACTERISATION Physical characteristics of the buildings

andbuilding services

QUANTIFICATION Number of buildings of each type

in the reference year SIMULATION

Calculation of net energy demand with the ECCABS model

FINAL ENERGY

Transferring the net energy to final energy demand

VALIDATION Comparison of the results to

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o be transparent

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

The parameters introduced to the model as input data will be presented in following chapters of this thesis work.

As it is mentioned in previous sections a number of models have been already developed for the UK buildings stock. But this work aims at using a tool which is capable to be applied to any region.

4.2 Segmentation Methodology

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

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

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

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

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

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

Table 3. Examples of classification methodology in the UK

Model

Segmentation Criteria Resulting amount of

archetypes

BREHOMES age group 1000

built form tenure type the ownership of central heating Johnston Age Pre-1996 post-1996 -

DECarb age band 6 age bands 8064

CDEM

built

form type and dwelling age

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:

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o Effective heat capacity of the building (Tc)

o Floor area

o External surface area

o Internal gains

o Minimum desired indoor temperature (Trmin)

o Maximum desired indoor temperature (Trmax)

o Sanitary ventilation rate(Vcn)

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

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

Table 4. Building type classification used in this work.

Building subsector Building type Domestic buildings Detached semi detached traced flat bungalow others Non- Domestic buildings

Offices Retails Warehouses

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

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

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

Table 5. Dwelling types in previous studies in the UK

Source Building Type classification

Collins, et al. (2010) End terrace Mid terrace Semi-detached Detached Converted apartment Purpose built apartment

Temporary/unknown Firth, et al. (2009) End terrace Mid terrace Semi-dethatched Detached Converted apartment Purpose built apartment

4.2.2 Construction period

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

o Average U-value of the building (U)

o Window area (Sw)

o Sanitary ventilation rate(Vcn)

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

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premises in England and Wales. Scotland and north Ireland have their own legislation (see appendix 3). Part L of building regulations has been considered as the base to perform the time period segmentation for the entire UK because the largest portion of buildings are located in England and Wales. Based upon this explanation and according to the updates of the regulation Part L (see section 2.3) the construction period in the UK is classified into seven categories: o Before 1985 o 1986-1991 o 1992-1995 o 1996-2002 o 2003-2006 o 2007-2010 o After 2010

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

4.2.3 Climate zone

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

Table 6. Weather data file inputs

Description Unit

Time S

Air temperature °C

Dew point temperature °C

Global radiation on horizontal surface W/m2

Diffuse radiation on horizontal surface W/m2

Normal direct radiation W/m2

Long wave radiation W/m2

Illuminance global Lux

Illuminance diffuse Lux

Illuminance direct Lux

Wind direction Deka degrees

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

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

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

Table 7. Cities chosen in different climate zones

Chosen cities Climate Number

London 1

Birmingham 2

Newcastle 3

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The study under taken by Collins, et al. (2010) selects the weather data based on the HadRM3

model which has data for 50×50 km2 grid boxes over the UK. Four grid boxes were Chosen,

which contained four UK locations: Ringway, Manchester, Edinburgh, Heathrow, London, and Cardiff. In the CDEM and Johnston models the dwellings are subjected to the same weather conditions (Firth, et al., 2009 ; Johnston, 2003). Boardman, et al. (2005) considers nine geographical areas but it is not specified which climate zones are chosen.

Furthermore the UK´s building regulation codes do not include any information about the climate zones.

4.2.4 Type of heating system

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

o Internal temperature

o Fuel share

In the BRE’s housing fact file the average internal temperature for centrally heated dwellings is given to be 17.5 °C while it is 14°C for non-centrally heated premises (Palmer & Cooper, 2011).

Note that during the past few decades the old non-central heating systems have been changed into central ones, thus the author of this thesis work has assumed that premises which currently have non-central heating system are all built before 1985. Therefore this factor does not increase the number of archetypes which are built after 1985.

4.2.5 Total number of archetypes based on the developed methodology

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

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16 Table 8. Total number of archetype buildings

Type Period of construction Climate Zone Heating system Total number of archetypes Domestic 6 6 4 2 168 Non-Domestic 3 6 4 2 84

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

4.3 Characterization of the UK building stock

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

4.3.1 Average heated floor area

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

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

Based on this illustration the surface area of different building types is estimated by considering the average floor area of each building type in each time period (see table 9). The results obtained are in agreement with Johnston (2003) where the weighted average useable

floor area of the Great Britain housing stock is assumed to be 85m2 while Roys (2008) which

is applied in this work estimates it to be slightly over 80 m2.

Table 9. Dwelling floor area

Building type Average floor area(m2₎

Detached 150 Semi-detached 90 Traced 80 Flat 60 bungalow 73 Other 85

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

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

Building type Before 1985 1986-1990 After 1990

Retails 143 479 463

Offices 227 428 423

Warehouses 630 680 793

4.3.1 Total windows area

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

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

Sw=λ + µTfa Equation 1

Where Tfa is the total floor area, λ and µ are coefficients to be determined for each archetype.

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

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

built before 1984 λ _µ

Detached 10.43 0.10

Semi-detached 10.9 0.08

Terraced 6.05 0.12

Bungalow 5.75 0.13

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

built after 1985 λ _µ

Detached 2.33 0.133

Semi-detached 9.43 0.069

Terraced 5.96 0.077

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Window surface area for flats is taken from The Government’s Standard Assessment

Procedure for Energy Rating of Dwellings (Garston, 2009). Table 13 presents the formula to

calculate the window area given by Garston (2009). In this master thesis work it has been decided to apply Garston (2009) method to calculate the windows area as it is specific for the UK.

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

Period Window area(m2)

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

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

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

Period Window Wall ratio

Before 1965 set to 10% of floor area

1966-1984 33%

1985-1995 35%

After 1996 40%

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

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

Building type Method used to calculate windows surface Detached

Semidetached Bungalow

Terraced

The method presented by Chapman (1994)

Flats The method presented by

Garston (2009)

Non- domestic buildings

The method presented by Smith (2009)

4.3.2 Total external surface

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

Chapman’s method is compatible with 3CL-method2. According to the 3-CL method total

wall area is calculated by following expression (Martinlagardette, 2009).

Swell=ATT × Form ×

× (Level× HR) - Sw Equation 2

Where:

ATT is the attached character of the dwelling

Form is a parameter which indicates the configuration of the building A is the living space or heated floor area

Levels is the number of floors of the building HR is the height under the roof (2.5 m) Sw is the window area

The attached character of the dwelling (ATT) can be taken from Table 16 according to the 3-CL method.

2

French Environment and Energy Management Agency (ADEME) introduces algorithms from the 3-CL method For calculating end-use energy consumption in dwellings.

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21 Table 16. Detached factors

Building type ATT

Detached 1

Semi detached 0.7

Terraced 0.35

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

Table 17. Comparison of Chapman and 3DL

Building type Floor area Level External walls area obtained from Chapman’s method External walls area obtained from 3DL’s method Terraced 80 1 36.5 32.24 Detached 150 1 125 126.14 Semi detached 90 1 67.7 68.4

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

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

Dwelling type

External walls surface (m2) Detached 430 Semidetached 250 Terraced 198 Bungalow 236 Flat 54 Others 268

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4.3.3 Average U-Value of Buildings

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

= × ( ×)( × )( × )

Equation 3

Where A and U are the surface area and the U-value of each element respectively.

Building standards in North Ireland is derived from the Department of Finance and Personnel (DFP) while the Scottish legislations are taken from the Scottish Government website (see Appendix.3).

The average U-value for the buildings constructed before 1985 is taken from a number of sources which are given in table 19. Based on the values found in literature review the author has decided to introduce the following values to the ECCABS model:

o Average U-vale of walls : 1.36 (W/m2K)

o Average U-vale of floor : 0.51(W/m2K)

o Average U-vale of roof : 1(W/m2K)

o Average U-vale of windows: 5.7(W/m2K)

Table 19. U-value of dwellings built befor 1985

Source U-value (W/m2K)

(Johnston, 2003) Walls : Uninsulated cavity : 1.36

Uninsulated solid : 2.12

Roofs : Insulated accessible : 0.36

Uninsulated accessible : 2.02 Inaccessible : 0.51

Floors : Solid concrete and suspended timber :

0.60 & 0.80

Windows: Double and single-glazed units : 3.30

& 4.70

(Collins, et al., 2010)

Based on average for historic group UK stock built between 1981 and 1996

Walls: 0.3999 Floor : 0.4577 Roof : 0.1416 Window : 2.3967 (Firth, et al., 2009)

U-values for the1945 to 1964

semi-detached house archetype

Wall : 1.2 Roof : 0.44

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4.3.4 Average constant lighting load

Since a big change has taken place in lighting energy, in this master thesis work it is assumed that the lighting system is unique in all dwellings independent of their construction period. Collins, et al. (2010) is the only found source which introduces the lighting energy use in

buildings. According to this source the lighting energy use is 6 w/m2. If one assumes that the

lights are in average on for 3 to 4 hours per day then the average constant lighting load would

be around 0.9 w/m2. This is in agreement with the figure given by the BRE’s Housing Energy

Fact File which suggests the constant lighting load of 0.88 w/m2 (see appendix 4).

The average constant lighting Load in Non-Domestic buildings is taken from Pout, et al. (2002) where the commercial and public sector energy consumption for lighting per unit floor area is given (see appendix 4). Table 20 reports the average constant lighting load taken from this data source.

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

Building type Average Constant Lighting Load(W/m2)

Offices 4.3

Retails 10.8

Warehouses 4.0

Dwellings 0.9

4.3.5 Average constant gain due to people in the building

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

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

Degree of Activity Typical Application Average Metabolic rate - male adult

(W)

Seated at rest Cinema, theatre, school 100

Seated, very light work Computer working 120

Office work Hotel reception, cashier 130

Standing, walking slowly Laboratory work 130

Moderate work Servant, hair dresser 160

Light bench work Mechanical production 220

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

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

Building Type Average constant gain

W/m2 Detached 0.52 Semi detached 0.86 Terraced 0.97 Flat 1.30 Bungalow 1.06 Offices 3.31 Retails 3.20 Warehouses 1.39

The only available data base regarding the occupancy factor of non-residential buildings is 2003 CBECS Detailed Tables published by US. Energy Information administration (CBECS,

2003). Based on this database, density of people in warehouses is 158m2/person. Since it has

been the only source available this figure has been applied to the model.

A survey of a number of different office buildings with different densities of people in 1993 and 2000 was undertaken by Stanhope (2001). The results of the surveys showed the occupant

density of 12 m2/person and 16 m2/person for city center offices and business parks

respectively Stanhope (2001). It is in agreement with data taken from British council for offices where the occupant density is considered to be 11.8 m2/person (BCO, 2008). In the current work it is decided to consider the office hours between 07:00hr to 19:00hr, and 5 days a week, as suggested in DM (2012).

Density of occupants in retails is taken from CIBSE (2006) where the heat gain in typical buildings is introduced. Based on this document the average constant heat gain due to people in retails is calculated. A rough estimation gives the occupants heat gain of 3.2 W/m2 in retails.

4.3.6 Average constant consumption of appliances

The growth in appliances’ energy use has been very sharp. It has tripled in less than 40 years. The annual rise seems to be slowing but it has been nearly 3% a year. Domestic appliances used less than 5% of entire energy in 1970; they now use approximately 12% (Palmer & Cooper, 2011). According to the BRE’s Housing Fact file, the total final energy use of appliances in the UK was 58.4 TWh in 2008 (Palmer & Cooper, 2011), which is considered to be the same net energy for the direct electricity. The ECCABS model requires the input to be

given in W/m2. In national and international databases no data was found regarding the

appliances energy use. But still by knowing the total energy use of appliances and total number of buildings (taken from Palmer & Cooper (2011)) it is possible to estimate the

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average use of appliances per m2. Constant consumption of appliances is calculated to be 293W per dwelling. (see appendix 4). Knowing the surface area of each dwelling type

(presented in previous chapters) the constant consumption of appliances in W/m2 is calculated

and presented in table 23.

Regarding the NR sector, no data were found about the thermal gain or energy consumption of appliances in warehouses. In this master thesis it has been decided to estimate the energy consumption of appliances base on data given by the BRE’s non-domestic buildings fact file. Based on this document the total energy consumption of appliances in warehouses is 3,222 TWh per year and the total area of warehouses (presented in section 4.3) is approximately

1.226 × 108 m2, which gives the constant appliances use of 3 w/m2. The survey undertaken by

Stanhope (2001) reports an appliances use of 5.36 W/m2 for all types of offices. The average

constant consumption of appliances in retails is derived from CIBSE (2006), which gives 7.3

W/m2. Table 23 summarizes the appliances use for each building type.

Table 23 constant consumption of appliances considered in this work

Building Type Appliances use (W/m2)

Residential Detached 2.4 Semi detached 3.9 Bungalow 4.9 Terraced 4.4 Flat 5.9 Other 4.2 Non-residential Retail 7.3 Office 5.36 Warehouse 3

The appliances use in the UK seems to be considerably higher than the amounts obtained by

Benejam (2011) in Spain where the appliances use in the residential sector is 1.65 W/m2 and

in offices and commercial sector it is 1.5 W/m2.

4.3.7 Hot water demand

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

Q = ρ × v × C × ∆T Equation 4 Where:

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

Energy Use in the EU Building Stock - Case Study: UK