Predicting the urban solar fraction: a methodology for energy advisers and planners based on GIS

$: Predicting the urban solar fraction: a methodology for energy advisers and planners based on GIS$
Predicting the urban solar fraction: a methodologyfor energy advisers and planners based on GIS

Stuart Gadsdenb, Mark Rylatta,*, Kevin Lomasb, Darren Robinsonc

aBarnet Council, Barnet House, 7th floor, 1255 High Road, Whetstone, London N20 0EJ, UKbInstitute of Energy and Sustainable Development, De Montfort University, Scraptoft Campus, Leicester, LE7 9SU, UK

cBDSP Partnership, Summit House, 27 Sale Place, London, W2 1YR, UK

Abstract

This paper describes the development of the underlying methodology of a solar energy planning (SEP) system for energy advisers and

policy makers. The methodology predicts the baseline energy consumption of domestic properties and determines the potential for reducing

this using the three key solar technologies of passive solar design, solar water heating and photovoltaic (PV) systems. A new dwelling

classification system has been developed to address the major problem of data collection for city-wide domestic energy modelling. The system

permits baseline energy demands to be estimated using assumed values or more accurately calculated using dwelling survey data. The

methodology integrates existing models with new approaches to both identify suitable dwellings for installing solar water heating and PV

systems and to quantify the potential energy savings and reductions in carbon dioxide emissions. Guidance on improving estate layouts to

enhance passive solar conditions is also given. Results can be presented using a geographical information system (GIS). The paper concludes

with a discussion of possible planning scenarios to illustrate how the methodology may enable planners to consider the urban-scale application

of solar energy with greatly increased confidence.

# 2003 Elsevier Science B.V. All rights reserved.

Keywords: Energy models; Dwelling classification; Passive solar design; Solar water heating; Photovoltaic systems; Climate change

1. Introduction

World energy demand is continually increasing. This

demand is primarily being met by burning fossil fuels. At

the same time, concern about the environment is at an all

time high prompting world leaders to consider problems

such as escalating carbon dioxide (CO2) emissions and

possible climate change consequences. In the UK, the

Government has a manifesto target of reducing national

CO2 emissions to 20% below 1990 levels by 2010 [1]. This

exceeds their Kyoto agreement to reduce emissions of the

six main greenhouse gases to 12.5% below 1990 levels by

2008–2012 [1]. The operation of building services for space

heating, domestic hot water, lighting, mechanical ventilation,

air conditioning, etc. consumes energy which causes CO2

emissions amounting to 46% of the national total (27% from

dwellings and 19% from non-domestic buildings) [2]. Redu-

cing emissions from buildings, by making them more energy

efficient, would help the UK meet its CO2 reduction targets.

In addition, the possibility of displacing conventionally

produced energy with non-polluting alternatives is receiving

much attention. The UK Government recently underlined its

commitment to promoting the development of renewable

sources of energy as an essential ingredient of its climate

change programme [3], emphasising the role that regional and

local planning authorities have to play.

Solar energy has long been recognised as a major source of

renewable energy for heating and lighting buildings. At

present, however, the huge solar resource available within

cities is not exploited. One reason for this is that city planners

have no tools to help them make informed decisions on how

best to deploy solar energy technology. To try and redress this

situation, the solar energy planning (SEP) system is being

developed. The SEP system aims to predict the baseline

energy consumption of buildings and determine the potential

for reducing this by deploying the key solar energy tech-

nologies of passive solar design, solar water heating and

photovoltaic (PV) systems. To date, the system has focused

on dwellings. Existing energy-related models and new

approaches have been integrated in a structured way. Results

are presented using a geographical information system (GIS).

This paper sets the SEP concept in the context of other UK

energy planning tools. It describes the method by which the

baseline energy consumption of dwellings is determined and

Energy and Buildings 35 (2003) 37–48

* Corresponding author. Tel.: þ44-116-257-7973;

fax: þ44-116-257-7981.

E-mail address: [email protected] (M. Rylatt).

0378-7788/03/$ – see front matter # 2003 Elsevier Science B.V. All rights reserved.

PII: S 0 3 7 8 - 7 7 8 8 ( 0 2 ) 0 0 0 7 8 - 6

the new dwelling classification system. Methods of predict-

ing the solar energy potential of dwellings are presented for

the three solar energy technologies. The paper concludes

with a discussion of possible planning scenarios and makes

observations on the expected benefits of the SEP system.

2. Related work

Three important projects with related aims to those of the

SEP project are LT-Urban [4], Energy and Environmental

Prediction (EEP) [5] and Building Research Establishment

Housing Model for Energy Studies (BREHOMES) [6].

Although all three are concerned with predicting building

energy consumption in urban areas, each one addresses the

problem of data collection in different ways.

LT-Urban predicts building energy consumption using a

modified version of the lighting and thermal (LT) energy

model [7]. Parameters which describe the building fabric

(for example, orientation of facades and angles of obstruc-

tion of the sky) are derived using image processing techni-

ques based on digital elevation models (DEMs). The

remaining inputs to the LT method are given sensible default

values. Although LT-Urban only considers non-domestic

buildings, the use of DEMs is an interesting approach which

could be adopted in the SEP system.

In the EEP model, a statistical clustering method is

deployed to estimate standard assessment procedure (SAP)

[8] energy ratings for domestic properties. Clustering is

carried out on the basis of only four variables related to built

form: heated ground floor area, total facade area, ratio of

window area to wall area and the end area of the property. This

data is derived from both manually drawing round building

outlines on a digital urban map and performing rapid site

surveys. Knowing these four variables along with the dwelling

age allows the dwelling to be placed into one of a hundred

clusters. Each cluster then has a SAP rating calculated for its

centroid property. Other SAP input variables are based on

global assumptions, for example that all properties are single-

glazed and have a central heating system with a wall mounted

gas boiler. Even if more detailed information is available for

individual dwellings there is no scope to incorporate it into the

model. Data is stored at postcode level. This means that the

number of dwellings per cluster in each postcode is known but

it is not possible to identify which cluster an individual

dwelling belongs to. Therefore, results are only available

for postcode regions, i.e. groups of dwellings.

BREHOMES is a physically-based model of the energy

use of the UK housing stock. It calculates energy use in

dwellings using the annual version of the Building Research

Establishment Domestic Energy Model (BREDEM). The

data required by BREDEM is obtained from a wide range of

sources and is broken down into categories defined by

dwelling type, age, tenure, etc. With all the data in place,

BREDEM calculations are carried out for each category of

dwelling in the UK. Multiplying by the number of dwellings

in that category and adding each category together produces

an estimate of the entire UK dwelling stock consumption.

BREHOMES has been widely accepted as a valuable policy

advice tool and is used by the UK Department of the

Environment, Transport and the Regions (DETR). BRE-

HOMES operates by using default data derived from sta-

tistical sources and applied to average dwellings. Although

acceptable for its intended use, making general predictions

about energy consumption in the UK housing stock, it is

unsuitable for considering individual dwellings.

It is important for local authorities to identify individual

properties which can benefit from energy efficiency

improvements or solar energy technologies in addition to

making broader city-wide estimates to, say, monitor pro-

gress towards a CO2 emissions reduction target. Computing

power today allows rapid calculations to be carried out on a

house-by-house basis for large numbers of dwellings. Data

collection problems imply the use of a coarse modelling

approach using only limited input knowledge, but the

increasing use of SAP and National Home Energy Rating

(NHER) software [9] within local authorities means that

detailed data is more readily available. The SEP system,

therefore, allows the possibility of collecting and utilising

full data sets at the level of individual dwellings but a multi-

levelled approach permits coarser-grained modelling where

necessary. This is different to both the EEP model and

BREHOMES which only consider large regions. Like BRE-

HOMES, SEP derives defaults wherever possible from

traceable statistical sources but it always applies this data

to individual dwellings. When calculating energy demands

the SEP system uses the most detailed data available, i.e.

from an in-house survey or site survey and finally, when

neither of these is available, the national statistical data for

the dwelling type. This makes the SEP system more flexible

for use at a local planning level.

3. Domestic energy modelling

The baseline energy consumption of a dwelling provides

the benchmark against which the effect of energy efficiency

measures and solar energy usage can be compared (Fig. 1).

In the SEP system, this is estimated using a new database-

integrated version of the Building Research Establishment’s

domestic energy model, BREDEM-8 [10], which calculates

the monthly energy consumption in dwellings in terms of

space heating, water heating, lighting, electrical appliances

and cooking. The main reason for choosing BREDEM-8 is

that it is a monthly calculation and information is, therefore,

available on seasonal variations. This is particularly impor-

tant for considering passive solar and low energy design

measures. BREDEM-8 is, therefore, better suited to the

methodology than annual calculations such as SAP and

NHER.

The volume of input data required by BREDEM-8 is

commensurate with the Government’s SAP. This is generally

38 S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48

considered to provide the correct balance between data

requirements and predictive accuracy [11]. All of this data

is measurable, but it is not easy to obtain in practice owing to

the high cost of detailed on-site surveys. This poses con-

siderable problems for energy modelling on an urban scale.

Leicester, for example, has approximately 110,000 dwell-

ings; therefore, it is extremely unlikely that sufficient data

will become available for every property to allow a full

BREDEM-8 calculation to be performed. To overcome the

problem of data collection, a dwelling classification system

has been developed (Fig. 1).

3.1. Dwelling classification system

Most of the dwelling-related input data required by

BREDEM-8 can be estimated from the built form and

age of the dwelling. Various studies have classified dwell-

ings according to their built form (for example, [12]). The

proposed SEP system divides dwellings into six main classes

of built form as local authorities already have considerable

data stored in this format: detached; semi-detached; end

terrace; mid terrace; mid terrace with unheated connecting

passageway and flat.

The built form of a dwelling can usually be inferred from

inspection of its outline on a digital urban map or by site

survey. Although knowing the built form is important, it is

not enough in itself to generate the geometrical data required

by BREDEM-8. To determine all the element areas (for

window areas, see below) required by BREDEM-8 it is

necessary to know, at the very least, the ground floor area and

the exposed perimeter of the dwelling. Site surveys are time-

consuming and expensive so an attractive alternative is to

extract acceptably accurate values from the digital urban

map.

In a typical GIS view composed of numerous super-

imposed map layers, building outlines, their footprints,

appear to form closed polygons. They are not in fact drawn

this way. The building outline is instead formed from

intersecting polylines—graphical objects with numerous

line segments—distributed across several map layers.

A closed polygon can be created by manually drawing

round the visual outline of the footprint on an additional

Fig. 1. Methodology for determining the solar energy potential of a dwelling.

S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48 39

superimposed layer. This is, however, an extremely time-

consuming exercise, rendering it impractical on a large

scale, though it is still much cheaper than the alternative

of site survey. The process is also prone to error as the

manual tracing may not exactly follow the building outline.

To overcome these problems, a customised GIS tool known

as the ‘Footprint Tool’ has been developed [13] to auto-

matically extract the outline of buildings as closed polygons,

or footprints, from digital maps such as the Ordnance

Survey’s Land-Line Plus. Once the footprint tool has iden-

tified the closed polygon using a complex combination of

spatial database search and image processing, it can readily

calculate the ground floor area. Fig. 2 shows a screen grab of

an extracted footprint. The user can specify its built form.

The lengths of the dwelling walls can also be calculated and

their nature (party, exposed, etc.) described. Estimating the

number and the height of storeys from [12] allows the total

floor area and total exposed wall area to be determined.

Local knowledge can help to ensure that these estimates are

reasonable.

A further complication arises from the nature of the

underlying energy model in that it requires a dwelling to

be divided into two zones: zone 1 represents the area

designated as the living room and zone 2 represents the rest

of the dwelling. The definition of these zones is different for

each built form and so rules have been developed based on

standard dwelling configurations [14]. As an example, the

assumptions for a two-storey detached house are shown in

Table 1.

The age of a dwelling is also a useful general attribute

from which to derive generic data. It is not possible to extract

this information from digital maps unless local user knowl-

edge enables broad assignments to be made. The age of

dwellings can usually be determined from records held by

local authorities or from historical local street directories.

Nine age groups are defined in the SEP system: pre-1900,

1900–1929, 1930–1949, 1950–1965, 1966–1976, 1977–

1981, 1982–1990, 1991–1995 and post-1995. The age

groups correspond to major changes in construction stan-

dards in the UK. These standards can be found in early

Public Health Acts and By-laws and latterly in the Building

Regulations. The Government is currently reviewing

Approved Document L (Conservation of Fuel and Power)

of the Building Regulations [2]. A further age group will be

added to those listed above when the new Regulations come

into force. Once the age of a dwelling has been specified, a

set of characteristics taken from the appropriate building

standard is applied to it. The type of data associated with

Fig. 2. Screen grab showing an extracted footprint. The user can specify the built form (OS Map# Crown Copyright. All rights reserved. Leicester City

Council (2000).


each age group includes the U-values of building elements

(e.g. walls and roofs), glazing specifications, water heating

system type and fuel and ventilation requirements.

Total dwelling window area is controlled by the Building

Regulations and hence depends on the age of the dwelling.

Up to 1990, the maximum window area was given as a

function of external wall area and since then as a function of

total floor area. After calculating the total dwelling window

area using allowable limits, the individual window areas for

zones 1 and 2 are determined using rules based on the

standard dwelling configurations. The assumptions for a

two-storey detached house constructed in 1998 are shown

in Table 2.

Knowledge of both the built form and the age of a

dwelling allows statistics contained within the English

House Condition Survey (EHCS) to be used to obtain data

on the primary and secondary space heating system, the

water heating system and the cooking system commonly in

use. The EHCS is produced by the DETR every five years to

provide information on the changing condition and compo-

sition of the housing stock and the characteristics of the

households living in different types of housing [15].

Although the statistics relate to national trends, they can

be usefully applied at a local scale. BREHOMES also uses

the EHCS as a valuable source of input data [6].

The dwelling classification system enables most of the

dwelling-related data required by the underlying energy

model to be supplied from traceable sources. Those para-

meters which are still unknown are given standard default

values as defined in the BREDEM-8 documentation. For

example the zone 1 heating demand temperature is assumed

to be 21 8C and the number of occupants are calculated using

a relationship based on the total floor area of the dwelling.

3.2. Other methods of data collection

Of course, the accuracy of the underlying energy model

will be improved if it is fed with more reliable data (Fig. 1).

Such data can be obtained in three ways. Firstly, a rapid site

survey can be carried out. This collects data simply from

observing the principal (street-facing) facade of a dwelling.

It is unlikely that access will be available to the rear. About

one-eighth of the input data required by BREDEM-8 can be

collected from a rapid site survey including the glazing type,

number of storeys and level of overshadowing. It is also

possible to identify potential sources of roof overshadowing

which could prevent the successful installation of PV sys-

tems. Obstructions include surrounding trees and tall build-

ings and roof components such as chimneys and television

aerials located to the south of the likely PV siting.

Secondly, the householder may have completed a Home

Energy Survey Form [16]. These forms are distributed by the

52 Energy Efficiency Advice Centres located throughout the

UK and contain a number of simple questions designed to

obtain more information about the dwelling, e.g. space

heating system type and fuel, presence of insulation and

water heating system type and fuel. Approximately one

quarter of the input data required by BREDEM-8 is available

from a completed Home Energy Survey Form. This input

data allows dwellings to be analysed at what is commonly

referred to as Level 0 [17]. In Leicester, about 10,000

householders have completed one of these forms. Unfortu-

nately, many householders have not answered all of the

questions thus reducing the amount of useful data available.

Finally, the method which would obtain all the inputs

required by BREDEM-8 is a full property survey. A full data

set allows analysis at Level 3 [17]. The local authority in

Table 1

Example of dwelling classification system: definitions for a two-storey detached house

Zone Ground floor

area (m2)

Total floor

area (m2)

Volume (m3) Gross external wall

area (m2)

Roof areaa (m2) External door

area (m2)

Zone 1 40% of dwelling ground

floor area

40% of dwelling

ground floor area

20%

of dwelling volume

20% of dwelling

gross external wall

area

0 0

Zone 2 60% of dwelling ground

floor area

60% of dwelling

ground floor area

þ dwelling first

floor area

80% of dwelling

volume

80% of dwelling

gross external wall area

Equal to dwelling

ground floor area

3.28 (assume

two standard

doors)

a Heat loss area assuming horizontal roof insulation [12].

Table 2

Example of window areas: definitions for a two-storey detached house constructed in 1998

Zone Window area (m2) Location

Dwelling 22.5% of total floor area (maximum) Divided equally between front and rear facades

Zone 1 15% of dwelling window area All on front facade

Zone 2 85% of dwelling window area 35% of the 85% on the front facade with the remainder on the

rear facade


Leicester has obtained Level 3 data for approximately 1000

dwellings and this figure is steadily increasing. Although

this is a reasonable number of dwellings with a full data set,

it represents <1% of Leicester’s dwelling stock. Therefore,

the use of derived default data to predict the baseline energy

consumption is necessary for the majority of dwellings. A

similar situation is likely to exist in most other cities in the

UK.

4. Predicting potential solar energy yield

The SEP prototype embraces the three key solar energy

technologies of passive solar design, solar water heating and

PV systems (Fig. 1). New approaches have been developed

to predict the potential solar energy yield of dwellings and

these are described for each technology in turn in the

following sections.

4.1. Passive solar design

Planners can set stringent energy consumption targets for

proposed new housing estates on land owned by the local

authority. Furthermore, they can stipulate the use of passive

solar design techniques and active solar technologies. To

ensure developers are complying with these targets and

maximising the use of passive solar energy, planners have

control over the design and layout of the dwellings. Passive

solar design measures are targeted at proposed new dwell-

ings because existing dwellings cannot easily be re-designed

to incorporate such measures. A methodology for consider-

ing passive solar design measures during the design of

proposed new estate layouts is presented.

In its original form, BREDEM-8 calculates the solar gains

from windows and glazed doors using a simple anisotropic

sky model to predict diffuse radiation. As these solar gains

are crucial to both the success of a passive solar dwelling and

the accurate prediction of baseline energy consumption, the

SEP implementation incorporates a more sophisticated all-

weather anisotropic sky model due to Perez et al. [18]. This

sky model is generally considered to produce accurate

predictions of diffuse radiation. The beam radiation and

the diffuse radiation reflected from the ground and adjacent

buildings are then added to give the total solar radiation

incident on the inclined (in this case vertical) surface. This

calculation is carried out for every daylight hour on the mean

day of every month.

On receiving plans of a new housing estate, a planner can

calculate the baseline energy consumption of each dwelling

using BREDEM-8. This provides a benchmark against

which passive solar design measures can be compared.

There are many measures which must be implemented to

ensure good passive solar design but only the most important

in terms of energy reduction are considered in this metho-

dology. The measures to be considered by the planner are

described below.

Orientation of the principal glazed facade which should

face within 308 of due south to maximise the potential for

direct solar gain [19,20,21].

Overshadowing of the principal glazed facade. This is

predicted using an approach which considers all obstructions

in a zone �308 of due south from the principal glazed facade

[19]. The urban horizon angle (UHA, the average angle of

elevation from the centre of the affected surface to the top of

the obstruction) is calculated and compared to the solar

altitude angle for every daylight hour. When the UHA

exceeds the solar altitude angle, overshadowing occurs

[22] and the calculation of incident solar radiation described

above is modified using the UHA.

Ratio of glazing area on the principal facade to that on the

other facades. In a typical dwelling there is likely to be a

50:50 split of glazing but good passive solar design normally

has a ratio of at least 75:25 in favour of the principal facade

[22].

Glazing type (for example, single, double, etc.) which

affects both the solar gain to and the heat loss from the

dwelling. An improved glazing specification will reduce

energy consumption.

The above measures will be considered for each dwelling

using a parametric calculation engine currently being devel-

oped for the SEP system. This will automatically perform a

series of BREDEM-8 simulations to vary the parameters

one-at-a-time across their expected range of operation while

the other parameters are kept constant. A graph will be

produced showing the change in baseline energy consump-

tion as a function of the change in the selected passive solar

design measure with the present level noted. This will allow

the planner to determine necessary design changes to reduce

the consumption to the minimum level. The possible reduc-

tion in energy consumption effectively represents the pas-

sive solar design potential of the dwelling.

4.2. Solar heating for domestic hot water

A three-stage approach has been developed to determine

the potential solar water heating yield of a domestic property

and the likelihood of the householder installing a solar water

heating system (Fig. 3). The use of combined database and

GIS technology allows this approach to be applied to either

individual properties or batches of properties selected

through textual or spatial queries. It is also equally applic-

able to both new and existing dwellings. Fig. 4 shows a

screen grab of the SEP control panel displayed alongside the

GIS. The user selects dwellings to undergo modelling from

the control panel and can then observe the progress before

viewing the results through the GIS.

4.2.1. Stage 1: filtering

Initially, the method focuses on a small subset of para-

meters in order to determine the viability of dwellings for the

installation of solar collectors (Fig. 3). These characteristics

are regarded as key indicators that can be used to filter out


the most unpromising candidates. The detailed filtering

criteria are described below.

Restrictions which may prevent the installation of a solar

water heating system, e.g. listed building, conservation area or

Article 4 Direction [23]. Local authorities keep records of

buildings affected by these restrictions (for example, [24,25]).

Roof orientation, which must lie between �458 of south.

In the Northern Hemisphere, the orientation for optimum

system performance is close to south, but provided the

orientation is anywhere between south-east and south-west

the system will function close to its optimum (for example,

[26,27]).

Roof inclination, which must lie between 0 and 608. In

dwellings where the roof inclination is <58, however, the

solar collector should be inclined between 5 and 608 to

ensure that the annual solar energy supplied is at least 90%

of that obtained at the optimum collector position [28].

Roof area, which must be >3 m2. A typical domestic solar

water heating system in the UK employs a collector area of

at least 3 m2 [29].

Fig. 3. Approach to determine the solar water heating potential of a domestic property.


Dwellings affected by restrictions under (1) will be

removed to a temporary list for further inquiries by the

planner on a case-by-case basis. It would be inappropriate to

apply further procedures until the uncertainty is resolved. If

the restrictions prove no obstacle then these dwellings can be

submitted to the remaining steps of the filtering process.

These steps can be applied in any order to each identifiable

roof plane of a dwelling’s roof structure (e.g. a simple

pitched roof with two equal planes and one ridge). If no

plane can be found within the boundary limits of a particular

step, the candidate dwelling is removed from the batch being

considered.

Software specially developed for the SEP system auto-

matically calculates the orientation and area of each roof

plane. As the ridge height of the dwelling is unknown, it is

not possible to calculate each roof plane’s inclination but a

rough assessment can be obtained based on local knowledge

and the age of the dwelling. Although it is very difficult to

give a precise inclination angle, it is reasonable to assume

that most roof planes will be inclined between 5 and 608. A

more exact inclination angle can be determined during a site

survey. Using the above data, the SEP system is able to

identify the most promising roof plane of the dwelling.

Possible shading problems on the roof plane can be identi-

fied as part of the rapid site survey described in Section 3.2.

Partial shading does not seriously affect the performance of

a solar water heating system but prolonged periods of

shading should be avoided.

4.2.2. Stage 2: targeting

The targeting stage compares each dwelling against a set

of parameters which are based on socio-economic factors

and related information inferred from physical aspects of the

dwelling (Fig. 3). These factors are: ownership of the

property (e.g. owner occupied, private rented, local authority

rented); income of occupants; number of occupants; floor

area of dwelling; value of dwelling and type of dwelling.

The methodology uses a set of rules based on these

parameters to assess the advantages of targeting a particular

dwelling. Studies have shown that owner-occupiers are the

most likely to install energy efficiency measures [30]. There-

fore, it is reasonable to assume that installation of a solar

Fig. 4. Screen grab showing the SEP control panel and the GIS (OS Map# Crown Copyright. All rights reserved. Leicester City Council (2000)).


water heating system is more likely if the property is owner

occupied rather than rented. This is because an owner-

occupier will directly benefit from the resultant reduced

fuel bills obtained after paying for the system’s installation.

In rented accommodation, however, the landlord would pay

for the system installation but the tenants would benefit from

the reduced bills. Hence, there is little incentive (at least in

the usual ‘sellers market’ operative in the UK) for a landlord

to install a solar water heating system. Incentive schemes

aimed at landlords might change this situation, but in their

absence it seems reasonable to give owner occupied dwell-

ings much higher priority than rented dwellings.

Due to the relatively high installation cost of a solar water

heating system, higher income homeowners might be tar-

geted before lower income groups. A study by Leicester City

Council [30] showed that the higher the income, the greater

the willingness to pay more for an energy efficient appliance.

This rule, however, needs to be weighed against the avail-

ability of house improvement grant that may be available.

The number of occupants is also highly relevant as this is an

indicator of the overall hot water demand. A solar water

heating system would be most cost-effective where the

number of occupants and, hence, the demand is greatest.

The built form allows inferences to be made when primary

information is unknown. The floor area of a dwelling can

give an approximate indication of the standard number of

occupants using a relationship in BREDEM-8. Hence, the

likely hot water consumption can be determined. Dwelling

size and value are secondary indicators of income which in

turn can be used to indicate the ability to pay. Occupancy-

related information in relation to individual dwellings is

variable and needs to be reviewed on a periodic basis.

As the targeting process is only intended to provide an

indication of the likelihood that householders will install a

solar water heating system, it lends itself well to the use of

fuzzy logic techniques. The rules described above have been

implemented as fuzzy rule sets. The fuzzy logic system

analyses the available data and performs expert reasoning to

produce an index of the suitability of solar water heating for

each dwelling. The system indicates the degree of confi-

dence that can be placed on its conclusions and permits

inspection of its reasoning process in each case. The planner,

therefore, has access to built-in expertise to aid the decision

making in an area where they may lack specific knowledge.

Using this suitability index, the targeting process produces a

subset of dwellings for which solar water heating is con-

sidered most viable and these dwellings should be targeted

first. Dwellings not included in this subset are considered

less viable and thus there is less chance of a solar water

heating system being installed. Such dwellings could, how-

ever, be targeted at a later date if circumstances change.

4.2.3. Stage 3: detailed solar potential calculation

Following the first two stages, calculations can be per-

formed to quantify the solar water heating potential of the

targeted dwellings (Fig. 3). An approach based on a method

presented in BS5918, the British Standard code of practice

for solar heating systems for domestic hot water [28], has

been implemented. It predicts the energy supplied by a solar

water heating system. Modifications have been carried out to

improve the original British Standard model from an annual

to a monthly calculation. A monthly calculation is advanta-

geous as it highlights the wide variation in solar energy

supplied throughout the year and the periods in the year

when auxiliary water heating is required. The monthly solar

radiation incident on the solar collector is determined using

the approach described in Section 4.1. This approach is more

easily implemented in software as, unlike the original

method, it does not require surface orientation and inclina-

tion factors to be determined from graphs. This is an

important consideration in a methodology designed for ease

of use at the planning level that makes few assumptions

about the level of expertise required. Fig. 5 is an example of

the results output available from the SEP system. It shows

the CO2 emissions from a test dwelling and the potential

reduction if a solar water heating system is installed.

During testing of the model, it became apparent that

misleading results can be obtained if one or more of the

input parameters are given values outside the expected range

of operation. For example, consider a typical domestic solar

water heating system with 4 m2 of flat plate collectors. If the

system specification is kept constant but the collector area is

doubled to 8m2, the energy supplied by the solar water

heating system does not double. This is to be expected as the

volume of water being heated and stored has remained the

same. The increased collector area would only heat the water

faster. Therefore, during peak summer months, the collector

would be redundant for a considerable amount of time.

Under present financial conditions, such a system would

be extremely uneconomical as the added cost of the

increased collector area would not be balanced by the small

increase in energy savings. As such, it would not be sensible

for a planner to perform calculations using a system which is

unlikely to be installed in practice. To overcome this pro-

blem, some key input parameters in the model have been

linked together using standard design ‘rules of thumb’ to

ensure that any change in the value of one parameter results

in a corresponding change in the other parameters.

The daily mean hot water requirement, obtained from

BREDEM-8, is multiplied by 1.5 to estimate the pre-heat

storage tank volume according to ‘rules of thumb’ described

in the following references [26,31,32]. Relationships also

exist to link the type of solar collector (flat plate, evacuated

tube, etc.) with the area required to heat a certain volume of

water [23,27,33]. For example, 1 m2 of flat plate solar

collector is required for every 40 l of daily hot water

demand. The solar collector is selected from a database

of existing panels. With these rules in place, it is not possible

for the planner to specify a system with an unrealistic design.

If the economic situation was to change and solar collectors

became considerably cheaper, the constraints placed on the

calculation model could be revised.


Although the method assumes that these stages will be

applied sequentially, it is possible to go directly from stage 1

to stage 3 or to terminate the analysis at the end of stage 1 or

stage 2 where circumstances make this desirable.

4.3. Photovoltaic systems

In the UK, PV systems are not yet economically feasible

for dwellings. This situation could, however, quickly change

if the Government’s plans to follow the lead of other

countries come to in to effect. For example, Germany

launched a 5-year publicly financed market introduction

programme in 1999 to install 100,000 rooftop PV systems

[34]. Similar programmes also exist in Switzerland, Norway,

the Netherlands, Italy, Japan and the USA [34]. If the UK

Government implemented similar policies, PV systems

would rapidly become more competitive. It is, therefore,

important to consider PV systems in the overall methodol-

ogy to allow planners to estimate their future potential.

The three-stage approach outlined earlier for solar water

heating systems provides a useful framework for analysing

PV systems. If it is assumed that most domestic PV systems

will be installed on the roof (a reasonable assumption given

that there is likely to be less overshadowing problems on a

roof than a vertical facade), the filtering approach described

in Section 4.2.1 can be directly applied to PV systems. If,

and when, the cost of PV systems falls to a more affordable

level, the targeting approach described in Section 4.2.2 can

also be included in the methodology. The calculation pro-

cedure used to predict the electrical yield from a PV system

is a model based on a method presented in [35,36,37]. This

model calculates the hourly electrical yield from a PV

system. The solar radiation incident on the PV array is

calculated using the approach described for passive solar

design (Section 4.1). The system user must specify a PV

module from a database of existing modules containing all

the technical data required to perform the calculation.

Simulations are performed for the mean day in every month

and electrical yield is presented on both a monthly and an

annual basis.

As with solar water heating, possible roof shading pro-

blems can be identified during a rapid site survey. The

performance of a PV system is, however, extremely sensitive

to even slight overshadowing. Therefore, the rapid site

survey is an important part of the approach to identify

suitable dwellings for installing PV systems. Where a dwell-

ing meets all the filtering and targeting criteria, a calculation

can be performed to estimate electrical yield with the

proviso that a site survey is performed.

5. Planning scenarios

As the SEP system is capable of visualising the effects of

energy saving measures, it has considerable scope for influ-

encing both policy makers and public attitude. This section

indicates how the system might be used once delivered.

Local authorities could decide to promote the use of solar

water heating systems on the back of a grant-funded initia-

tive or simply as an exercise to increase the awareness of

Fig. 5. Screen grab showing results output from the SEP system.


global environmental problems. The filtering and targeting

processes could be applied over the whole city to identify

those dwellings which are viable for solar water heating.

Invitations could be arranged for interested householders to

respond and at a subsequent meeting, the detailed calcula-

tion could be performed to provide energy savings, reduc-

tions in CO2 emissions, etc. In the future, a similar approach

can be envisaged for PV systems but under present financial

circumstances it is likely that the analysis of PV will only be

carried out to provide an indication of the potential electrical

yield available in a city.

Some new housing developments occur on land owned by

local authorities. In such cases, as stated in Section 4.1, the

local authority is able to specify stringent energy consump-

tion targets which proposed developments must meet. The

SEP system could be used to analyse proposed estate layouts

to ensure they meet the specified targets. Where targets are

not met, passive solar design improvement measures can be

considered using the parametric calculation engine. The

planner, therefore, has control over the design of the layout

and confidence that the energy targets are being achieved. In

some cases, renewable energy targets are also specified and

the SEP system could be used to ensure that these have also

been reached by considering solar water heating and PV

systems.

Energy departments could also use the system for purely

projective purposes to fulfil requirements placed on them by

policy makers or to investigate energy issues stemming from

local authority initiatives. Many previous studies have been

carried out at the local level using apportioned data entirely

collected at regional and national levels. Therefore, a SEP

model constructed using even the system’s most generic

level of data provision could be used with advantage to

provide estimates of solar energy potential. With the greater

but still relatively modest investment of resources needed to

satisfy the data requirements at an intermediate level, much

more accurate forecasts could be available on an urban scale.

6. Further work and concluding remarks

Now that the methodology is fully defined, the SEP

system will be applied to a small case study area in Leicester.

The area will contain a wide mix of dwellings with various

levels of data available to show the different modes of

operation of the tool. This case study will serve as a

demonstration of the concept and illustrate the usefulness

of the system to planners and energy advisers. It is also

intended to carry out extensive trials of the system with its

intended users through collaboration with the local author-

ity. These trials will enable the tool to evolve to fit user

needs. In this way, it is hoped to make real headway towards

the goal of truly practical tools for making cities more

sustainable.

The SEP system could also be extended in the future to

consider other building sectors. For example, commercial

buildings, especially offices, have good potential for redu-

cing energy consumption by using daylighting to offset

electricity consumption for artificial lighting. Furthermore,

suitable buildings could be identified for the installation of

PV systems to generate the electrical base load. Reducing

CO2 emissions from non-domestic buildings has an impor-

tant role to play in averting climate change.

This paper has presented work in progress on the devel-

opment of the SEP system. The methodology described for

predicting the baseline energy consumption of dwellings

and determining the potential for reducing this using pas-

sive solar design, solar water heating and PV systems gives

an impression of its conceptual framework, design, imple-

mentation and end-use. The integration of solar energy

within cities is an important step in reducing the reliance

on fossil fuels and tackling climate change and it is hoped

that the SEP system will play a useful role in informing this

process.

Acknowledgements

This research is funded by the Engineering and Physical

Sciences Research Council (EPSRC), grant number GR/

L05372. We should also like to acknowledge the help of the

following organisations: Leicester City Council; Leicester

Energy Efficiency Advice Centre; The CAD Centre, School

of Architecture, De Montfort University.

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