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).
40 S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48
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
S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48 41
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
42 S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48
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.
S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48 43
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)).
44 S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48
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.
S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48 45
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.
46 S. Gadsden et al. / Energy and Buildings 35 (2003) 37–48
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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