Dynamic energy-consumption indicators for domestic appliances:environment, behaviour and design

G. Wood*, M. NewboroughSchool of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, Scotland EH14 4AS, UK

Received 30 May 2002; received in revised form 21 October 2002; accepted 17 November 2002

Abstract

The literature concerning the application of information-feedback methods for saving energy in the home is reviewed. Particular attention is

given to electronic feedback via smart meters and displays, or ‘‘energy-consumption indicators’’ (ECI). Previous studies have not focused on

individual appliances, but this paper presents the findings of a UK field study involving 44 households which considered domestic cooking: it

compares the effectiveness of providing paper-based energy-use/saving information with electronic feedback of energy-consumption via

ECIs designed specifically for this investigation. Twelve Control Group households were monitored for a period of at least 12 months and this

revealed an average daily consumption for electric cooking of 1.30 kWh. Subsequently across a minimum monitoring period of 2 months, 14

out of 31 households achieved energy savings of greater than 10% and six of these achieved savings of greater than 20%. The average

reduction for households employing an ECI was 15%, whereas those given antecedent information alone reduced their electricity

consumption, on average, by only 3%. The associated behavioural changes and the importance of providing regular feedback during

use are identified. It is recommended that further attention be given to optimising the design and assessing the use of energy-consumption

indicators in the home, in order to maximise the associated energy-saving potential.

# 2002 Elsevier Science B.V. All rights reserved.

Keywords: Energy-consumption indicators; Home automation; Energy-saving potential

1. Introduction and background

Energy use in the home accounts for significant propor-

tions of total energy-consumption both in industrialised

and developing countries. The operation of most types of

domestic appliance, lighting and air conditioning relies

upon electricity and this results in substantial carbon dioxide

emissions per household. In the UK, domestic energy use

produces approximately 1.9 t C per annum per household

[1]. In order to stabilise atmospheric carbon dioxide con-

centrations, recent studies [2–5] have called for large

changes in the annual rates of CO2 emission associated with

energy use (e.g. by �60% between 2000 and 2050). To

achieve such targets wide-ranging action will be required

with respect to research, development and implementation

(i.e. some combination of technological change, fuel switch-

ing and behaviour modification). Reducing carbon depen-

dency by reducing energy-consumption per capita, or per

activity, is a particularly important method [4].

Simplistically, in the residential sector, there are three

general routes for reducing rates of energy-consumption

(and thereby CO2 emissions):

(i) replace the existing housing stock with low-energy

buildings designed primarily to minimise heating and

cooling loads;

(ii) develop, and achieve widespread replication for, low-

energy domestic equipment (e.g. appliances, lighting

and IT); and

(iii) promote and achieve ‘‘energy-conscious’’ behaviour

among end users.

Within the UK housing stock, rates of demolition, new-

build and refurbishment are very low (e.g. <1% per annum)

and so it will take several decades to achieve nationally-

significant energy savings. By comparison, most types of

Energy and Buildings 35 (2003) 821–841

Abbreviations: DEFRA, Department for the Environment, Food and

Rural Affairs; ECI, energy-consumption indicator; EU, European Union;

GHG, greenhouse gas; IPCC, Intergovernmental Panel on Climate Change;

ISO, International Organisation of Standardisation; LCD, liquid crystal

display; LCU, local collector unit; NVE, Norwegian Water and Power

Authority; OECD, Organisation for Economic Co-operation and Devel-

opment; PC, personal computer; PIU, performance and innovation unit;

PPD, peak power demand; TV, television; VCR, video recorder* Corresponding author. Tel.: þ44-131-451-8311;

fax: þ44-131-451-3129.

E-mail address: m.newborough@hw.ac.uk (G. Wood).

0378-7788/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0378-7788(02)00241-4

domestic equipment are replaced relatively frequently, but

raising the end-use efficiencies of new appliances and

upgrading the stock of household equipment is still quite

a slow process. Individuals often prefer to buy cheaper less-

efficient models, because there is usually a price increase

associated with the highest efficiency equipment [6], while

appliance manufacturers need to achieve high production

rates if sales prices are to be minimised. The application of

appliance energy labels is helping to raise interest in energy

performance at the points-of-sale, but once a decision has

been made to buy a new appliance, the energy-consumption

of the associated activity (e.g. home laundry or cooking) is to

a large extent pre-destined until such time that a replacement

appliance is purchased. For these reasons, it is considered

that alternative methods of reducing energy-consumption at

the points-of-use need to be researched, and new routes

established for realising behavioural change.

Household energy-consumption is transient and rates of

energy use vary dramatically with time of day and time of

year. Although one domestic appliance may use less than

1 kWh per day, appliance usage in general results in large

demands for electricity at peak times [7]. When demands

from several appliances occur at the same time they can

produce a peak demand of several kilowatts. The appliances,

which cause the largest peaks in demand within the home are

electric cookers/ranges. A cooking event can produce a peak

of up to 10 kW, while kettles, ovens and tumble dryers create

individual peak demands of only 2–2.5 kW (see Fig. 1). In

the UK, the peak power demand placed on the National Grid

grew by 33% from 37.7 GW in 1968 to 50.1 GW in 2000 and

domestic electricity use has been a major causal factor. It is

therefore desirable for the domestic sector to achieve a

smoother electrical demand profile in order to maximise

the overall efficiency of the electricity system [7].

The components of electricity consumption in the home

may be classified in broad terms as ‘‘predictable’’, ‘‘mod-

erately predictable’’ and ‘‘unpredictable’’. The former occur

when the building is unoccupied or the occupants are asleep

(small cyclic loads for example from refrigeration appli-

ances and steady loads from security lighting and items

on standby such as TVs, and VCRs). The remaining con-

sumption is affected by both occupancy and external influ-

ences (e.g. seasonal/weather variations). The ‘‘moderately

predictable’’ consumption relates to the habitual behaviour

patterns of the residents. For example, many people watch

TV programmes at regular times each day/week and switch

lights on/off each weekday morning as they rise and

then leave for work. Lastly ‘‘unpredictable’’ consumption

Fig. 1. Example of an electricity demand profile from an individual household recorded on a 1-min time base [7].

822 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

describes the majority of domestic energy use; it tends to be

irregular occurring at the users discretion, for example when

the occupant wants to cook food or operate the clothes- or

dish-washing machine.

These three types of consumption may be found in most

households, but this simple classification cannot explain

why energy-consumption and electrical load profiles are

so different between otherwise similar households. Varia-

tions between households are considered to result from

variations in micro-level activities, e.g. differences in the

length of time taken to do each activity, in cooking and home

laundry habits as well as in the availability of appliances [8].

Fechner [9] showed that up to a 50% variation in electricity

consumption occurred between six chefs, all cooking the

same meal with the same equipment. Studies in the United

States, the Netherlands and the UK have estimated that 26–

36% of in-home energy use is due to residents behaviour

[10–12]. Stern [6] argues that energy-consumption can be

reduced by providing the consumer with a more informed

choice about their energy-using practises. In general, chan-

ging energy-using behaviour has a promising potential for

energy conservation [13].

The population is a diverse group of people each having

distinct histories, attitudes, and socio-cultural demographics

(age, sex, education and wealth/income). People also show

differences in their physical/mental health, relationships

with family/friends and amounts of free time, which affect

their energy-using behaviour. Mansouri-Azar [14] estimates

that some 64% of the variation in household energy-con-

sumption can be attributed to socio-economic factors. How-

ever, this scale of variation will always exist in a population

and some causal factors will be very difficult to change. So

methods for reducing the population’s energy-consumption

must account for, and function effectively irrespective of,

these differences. Differences in lifestyle and behaviour can

occur in a relatively short period, compared with the life

expectancy of a domestic appliance, but ‘‘old habits die

hard’’.

To promote energy-conscious behaviour effectively

throughout the population requires a better understanding

of the interface between people and the equipment they use.

Thus, this paper considers (at the individual household level)

how we can reduce energy-consumption with existing end-

use equipment in the residential sector, rather than why

socio-cultural population demographics currently causes

substantial differences in rates of energy use. A particular

focus is placed on the interactive activity of home cooking.

1.1. Behavioural change

Given the diversity of people in the UK population, it

is almost certain that different households have different

levels of knowledge, about energy saving, different attitudes

and different energy-using/saving practices. There is also a

difference in the ability of consumers to carry out proven

energy-saving techniques. Kaiser et al. [15] identifies three

situations in which even though a person has a positive

attitude towards an ecological behaviour, they are prevented

from carrying out this behaviour. For example, people may

be economically constrained—one person may be able to

afford to replace an existing appliance prior to it failing with

a new low-energy version, while another may not and

instead opt for an old second-hand appliance. People may

also have the intent to carry out an ecological behaviour but

may be socially pressurised by other family members or

friends not to do so. Lastly people may be restricted by a lack

of opportunity to carry out an ecological behaviour (e.g. a

person may separate glass bottles from their general waste

but not have a means of transporting them to a bottle bank).

It therefore can be easier for one person or one household to

save energy relative to another. Because of these variations

in energy-saving potential and in energy-saving actions, it is

fundamentally difficult to both predict energy savings in a

given household and to optimise advice as to how best to

reduce energy-consumption. Hence, there is a danger of

over-generalising advice and being too prescriptive, and this

may decrease (rather than increase) interest in energy-saving

among consumers.

Energy consumers may be influenced by antecedent

(general) and consequence (feedback) information. Ante-

cedent information describes practical ways for reducing

energy-consumption and could be in the form of pamphlets

posted through the door, notices, TV programmes or Internet

sites. Consequence information relates directly to a con-

sumer’s behaviour, i.e. it is feedback that provides a user

with information about the action he/she has carried out (or,

more succinctly, knowledge of results) [16–18]. Within the

context of a household, feedback can be for an individual or

for the household as a whole.

2. Antecedent information

Dennis et al. [19] reports that significant energy savings

can be made by providing antecedent information about

methods of energy conservation and cites a 60% reduction in

unnecessary lighting use by putting signs near light

switches. Winnett et al. [20] also reports a 10% reduction

in energy-consumption after subjects had seen a 20-min TV

programme about energy saving. However, an adverse effect

often occurs in antecedent information studies: the Fallback

effect. Defined by Wilhite and Ling [21] as ‘‘the phenom-

enon in which newness of a change causes people to react,

but then that reaction diminishes as the newness wears off’’.

Hayes and Cone [22] found that information alone (a poster

which described ways to reduce electricity consumption

and the energy-consumptions of individual domestic appli-

ances) had a temporary effect in reducing electricity

consumption. Initially after the poster was distributed in

one unit of a student-housing complex, there was a 30%

reduction in electricity usage, but in a subsequent week the

savings had fallen to 9%.

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 823

Another common problem is that subjects may behave

differently because they know they are being studied. This is

known as the Hawthorne effect [23]. Stern [24] suggests that

the conclusions of research on the responsiveness of con-

sumers to general energy-saving information, is heavily

effected by the Hawthorne effect. Indeed in both of the

studies of Winnett et al. [20] and Dennis et al. [19] all

participants were fully aware that they were being observed

for their energy-saving habits. Thus, in any human study

where useful information is provided, care needs to be taken

to account for the Fallback and Hawthorne effect.

3. Feedback information

Feedback is the alternative way to inform people about

their energy using/saving techniques. Van Houwelingen and

Van Raaij [13] outlined three main functions of feedback.

1. Feedback has a learning function—subjects learn about

the connection between the amount of energy they use

and energy consuming behaviour.

2. Habit formation—subjects put the information they have

learnt into practice and may develop a change in a

routine habit.

3. Internalisation of behaviour—when people develop new

habits after a while they change their attitudes to suit

that new behaviour.

Several studies have considered feedback information, in

which the Hawthorne effect could also have played a large

part in generating reductions in energy-consumption. For

example, Seligman and Darley [25] carried out an early

study in New Jersey, USA in which feedback was tested as a

means of decreasing residential electricity consumption

across a period of 1 month. All subjects were aware that

they were taking part in an energy study. The experiment

was carried out on 29 physically identical three-bedroomed

houses, where the energy-using devices were powered by

electricity (except the hot water, cooker and clothes dryer).

In the 15 feedback group households, information was given

in the form of daily feedback of percentage of predicted

electricity use for that day. Predicted electricity use per

house was based on one previous months temperature-

corrected electricity meter readings. Thus, if the home-

owner’s predicted consumption rate was to use 10 units of

electricity and the household actually used 8 units, then the

display would read 80%. The percentage was displayed on a

board outside the kitchen window. A 10.5% average reduc-

tion in electricity usage was found in the groups that were

exposed to this feedback.

More recent feedback studies have concentrated on giving

informative feedback about actual energy-consumption on

an energy bill. Wilhite and Ling [21] carried out a 3-year

billing study in Oslo, to find out if graphics and increased

actual use billing (rather than averaged billing as used

with direct debits) would reduce electricity consumption.

One thousand two hundred and eighty-six participants

were assigned to one of four groups: a Control Group

who received bills as normal and three information groups.

The latter groups were identified as (1) receiving actual use

bills bi-monthly, (2) receiving graphic bills with ‘‘this year’’

and ‘‘last year’’ information on a bi-monthly basis, and (3) as

for Group 2 plus energy-saving tips on the bill. The base line

for this study lasted for 1 year where all experimental groups

were billed bi-monthly. It was then in the second year that

Groups 2 and 3 received graphical information and graphical

information plus tips, respectively. The average reduction in

electricity consumption for all experimental groups was

10% in the first year and 7.5% in the second year compared

to the Control Group. There were no statistical differences in

consumption between the experimental groups. Since this

study was undertaken, the Norwegian Water and Power

Authority have adopted new billing guidelines requiring

the incorporation of graphical historical feedback plus actual

use bills four times per year [26].

Darby [27] suggests that disseminating energy informa-

tion in a written format such as a bill is not an ideal solution

for reducing the energy-consumption of the UK population.

Firstly it may exclude large numbers of the population, as

approximately 20%1 of the adult UK population has diffi-

culties with basic reading and maths [28]. Also the use of

bills as a method of paying for electricity is becoming

unpopular as other more convenient payment methods

emerge such as direct debits, standing orders, etc. [21].

These tend to be payments in advance, rather than payments

after receiving a service and thus are potentially counter-

productive to stimulating the widespread development of

more energy-efficient practices.

Further studies have employed reinforcement techniques

such as monetary payments and social commendation to

improve feedback results. For example, Seaver and Patter-

son [29] hypothesised that providing information to con-

sumers specifically about their personal fuel–oil for home

heating would lead to lower fuel consumption. They also

considered that commendation, as well as personal feed-

back, would achieve a further reduction in fuel consumption.

The feedback was in the form of a feedback slip, which was

issued every time oil was delivered (see Fig. 2). The

commendation given was a label with the words ‘‘we are

saving oil’’ in red block letters. One hundred and twenty-two

households participated in a 4-month study, 42 of which

formed the Control Group and received no feedback. The

other 80 were split into two groups, one group (35) with just

the feedback and the other (45) with feedback and com-

mendation. The Control Group used 0.146 gal per day. The

Feedback-Only Group showed little reduction in oil usage

using 0.143 gal per day, but the Feedback-and-Commenda-

tion Group showed a significantly reduced consumption of

1 The International Adult Literacy Survey 1994–1998 states that 20% of

16–65-year-old in the UK would have difficulty determining the correct

amount of medicine to give a child from information printed on a package.

824 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

0.129 gal per day. Seaver and Patterson [29] suggested that

these savings are due to the social recognition of efforts to

save energy.

By studying the effects of different types of information

feedback, conclusions can be reached about which type of

feedback would be the most effective (even if all groups

were affected by the Hawthorne effect). Hayes and Cone

[30] carried out an experiment on four units of an 80 person

student-housing complex in West Virginia, USA. The

experiment tested which would be the most effective method

of reducing energy usage; monetary payments (which

increased in relation to the proportion saved), energy infor-

mation, or daily feedback on consumption. The experiment

lasted 90 days, the first 20 days being used to generate a

comparative baseline. In the remaining time, each of the

units was exposed to various experimental conditions in 1-

week periods. Payments produced immediate and lasting

reductions in consumption, the average reduction being

33%. Feedback also produced an average reduction of

18%. However, when subjects were given information alone,

there was an initial 30% reduction but this fell to a 9%

reduction after 2 weeks. This indicates that feedback is more

effective than antecedent information alone.

It can be seen from these examples that even with

infrequent written feedback, significant energy savings tend

to be achieved. Better results could conceivably be attained

with feedback that is immediate [31]. Importantly Ammons

[32] and Van Raaij and Verhallen [31] state that the most

effective feedback is that which more immediately follows

an action. Stern [24] also argues that it is not the time

difference between days, weeks and months that is impor-

tant, but that the feedback appears immediately after an

action, which attempts to save energy.

3.1. Electronic feedback

All homes have an electricity meter, which is effectively a

continuous energy-consumption indicator (ECI). Generally,

these meters are used by utility companies to measure

consumption and they have not traditionally been designed

as displays to encourage customers to view/monitor their

own energy-consumption. Conventional meters have rela-

tively crude displays, which tend to dissuade householders

from using them as energy-saving tools. A survey carried out

by Meyel [33] indicated the lack of knowledge and under-

standing people have about their energy meters. More than

50% did not know where their gas or electricity meters were

and 45% could not read them. Modern digital metering and

display technology offers a route for displaying energy-

consumption information in a much more understandable

form.

Although the majority of electricity meters are traditional

induction meters (as used for approximately 100 years in the

UK), ‘smart’ metering technologies have been developed

since the 1980s. A smart meter is fully electronic and

permits a variety of functions and displays. Their availability

has increased the potential of using utility meters for envir-

onmental applications [34]. The two smart meter predeces-

sors were the electromechanical Ferraris meter and the

electronic meter. Both meter types have a standard display

of current, power or energy (kWh) usage for the dwelling

and provide consumers with very limited functions and

information about their personal electricity usage [35].

An example of a smart metering technology being applied

to improve understanding of appliance usage patterns is the

POEM metering system, which measures the electricity

consumptions of up to 16 appliances within a home [36].

Data is transferred from the appliance to a main local

collector unit (LCU) using radio waves. Each LCU stores

a meter identity number and the current meter reading and

the central POEM unit collects data from the LCU. The data

is then sent down the telephone line for analysis elsewhere,

when the phone is not in use. This system is purely a data

collection system and the presentation of energy information

to the customer is not its primary function. Brandon and Day

[37] and Marvin et al. [34] suggest that smart meter tech-

nologies could potentially help consumers to use their

energy more effectively. The UK Smart Meter Working

Group concur that the application of smart metering tech-

nologies in-homes has potential to reduce gas and electricity

consumption and carbon emissions as well as domestic fuel

bills [38].

Fig. 2. The feedback slip given to oil consumers at time of oil delivery from Seaver and Patterson [29].

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 825

With appropriate display technology, it is feasible to

provide visible and comprehensive information during an

energy-using event. McClelland and Cook [39] carried out

one of the earliest studies in Polks Landing, Carrboro, North

Carolina, USA, by employing an electronic device to show

consumers electricity information. Monitors were placed

inside 25 new houses, as they were built and the participating

households were observed for 11 months without the knowl-

edge of the owners. The device was named the Fitch Energy

Monitor (FEM) and measured total electricity usage (kWh)

from the homes mains supply. The electricity usage was

displayed in cents per kWh (where electricity price could be

set and reset if needed) and was displayed alternately with

the time of day. It is unclear where the display panel was

placed inside the home or how frequently the display was

updated, but it appears that it was accessible throughout the

day. McClelland and Cook [39] reported 12% less electricity

usage in households with a FEM compared with the Control

Group of 76 houses without the FEM. This result is parti-

cularly encouraging as the participants were not advised or

otherwise encouraged to save energy and so the Hawthorne

effect was minimised in this study.

Dobson and Griffin [40] developed Residential Electricity

Cost Speedometer (RECS) software and installed it into the

PCs of 25 Canadian homes. The RECS system measured

household electricity consumption and provided cost

and electricity consumption displays for various end uses

(cooker, fridge, dishwasher, dryer, lights). The information

was displayed on a present end-use cost per hour, which was

updated every 0.6 s. The feedback was also presented

on an hourly, daily, monthly and annual basis (see Fig. 3).

Electricity consumption was measured for 60 days, and the

temperature-corrected results showed that, compared to a

Control Group of 75 homes, the average daily electrical

consumption was 12.9% less in the RECS group.

In the UK a study carried out by Brandon and Lewis [41]

also used a personal computer to help homeowners to

understand electricity usage. The PCs used in this study

were not automatically updated, but required the user to

input meter readings. These readings could then be plotted

on a graph and compared to previous consumptions. The PC

also offered a questionnaire and advice on energy saving.

During the 9-month study in Bath, 120 houses were sub-

divided into seven groups including one Control Group. A

PC group was compared to five other groups that were

provided with written information about their electricity

expenditures (self-versus-others, self-versus-self, leaflets,

money and environment). The PC group performed consis-

tently better than the other groups with respect to reducing

rates of energy-consumption. Brandon and Lewis [41]

reported that 80% of the households in the PC group reduced

their electricity consumption, whereas collectively only

55% of the households in the other experimental groups

reduced their energy-consumption. The average reduction in

consumption of the PC group was 15% compared with that

of the previous year [42].

Dennis et al. [19] argues that feedback in the form of

frequent billing or energy audits is inefficient, because

consumers do not know the relative energy costs of the

various energy using systems in their households. Kempton

and Neiman [43] liken this to shopping at a store, which has

no prices on individual items, and being presented with only

a total bill at the cash register. Baird and Brier [44] showed

that many people assume that the larger the volume of the

appliance the more energy it uses per hour. Mansouri-Azar

et al. [12] tested whether consumers knew which were the

first, second and third most energy-consuming electrical

appliances within their home. Apart from simply guessing,

the only reasonable basis for estimating this would be to

multiply the operating period per day/week by the average

power input for each appliance (the latter being almost

impossible to estimate for appliances with several sub-

systems and settings!). A large majority of respondents

chose the washing machine as the first, second or third most

energy-consuming appliance, while (if present) the top three

consumers in average UK homes are lighting, the freezer and

the dishwasher (see Table 1). Some 14% of respondents

commented that the cooker was one of the most costly

appliances and it is plausible that this indicates a link

between power rating and cost, because cooking appliances

cause the greatest peak power demands in UK homes.

Clearly consumers neither have a clear basis for estimating

the energy costs of appliances nor for prioritising energy-

saving actions if feedback of total consumption is provided

centrally in the home.

During tracking and control tasks, Senders and Cruzen

[46] showed that feedback is more effective if it relates to

individual parts of a control system. Hence, feedback could

be given during, or immediately after, the use of an indivi-

dual appliance or heating system. By disaggregating end

uses such feedback would enable the consumer to learn the

relative energy costs of various components of the house-

hold. If a feedback method were to be placed on an appliance

during an energy-consuming event, a feedback loop might

occur [16] such as that shown in Fig. 4. The feedback of how

much energy is being used whilst operating a domestic

appliance may be analogous to driving a car that is equipped

with a speedometer. Like a speedometer, feedback about

energy-consumption on an appliance is something a user

can employ to regulate their consumption. A consumption

threshold (set by the user), rather like a speed limit, might

be used to indicate when the user was ‘‘driving’’ his/her

appliance too hard (i.e. consuming too much electricity).

This form of ‘‘appliance-based’’ feedback would be distinct

from previous paper-based feedback studies, as it occurs

during and immediately after some energy-consuming

action. Also the approach differs from previous electronic

indicator studies, as the information would be displayed at

the appliance.

Interacting with a display device involves human percep-

tion, cognition and motor activity [47]. Several factors

influence the feedback process in a research study (Fig. 4):

826 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

Fig. 3. The residential electricity cost speedometer showing (a) electricity usage in the present hour and (b) electricity cost by end use for a complete day

(adapted from Dobson and Griffin [40]).

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 827

(a) The uncontrolled environment. For example, Warm

[17] states that when a subject is carrying out a primary

task (e.g. driving) detecting a signal on a display may

become more difficult if the subject is in a noisy

situation.

(b) The person/people who install the feedback display/

instrumentation may directly or indirectly affect the

consumer. Indirect effects may include the experimen-

ters aims being transferred to the consumer through the

design, e.g. if the experimenter thinks viewing energy

in pounds and pence will be effective then the

consumer will read a display and think about energy

in a monetary form [48]. Direct effects include personal

contact with the consumer.

From the literature concerning feedback, it is unclear how

best to achieve feedback in the home and several research

questions emerge. For example, how frequently to feedback

Table 1

Comparison of the annual electricity consumptions [45] and peak power ratings [7] of domestic appliances, with results from a consumer survey which asked

individuals to identify the first, second and third most energy-consuming appliance in their home [12]

Appliance ranking in terms

of energy-consumption

Approximate annual

energy-consumption for an

average household (kWh)

Typical peak power rating of

stated appliance (kW)

The three highest users of electricity

as indicated by stated proportion of

survey respondents (%)

Highest Lighting: 717 Cooker hob: 6.5 Washing machine (26.9)

Oven: 2.5 Cooker (14.7)

Grill: 2.5 Tumble dryer (13.1)

Second highest Fridge-freezer: 500 Kettle: 2.5 Washing machine (27.9)

Tumble dryer: 2.5 Dishwasher (12.7)

Cooker (8.1)

Third highest Dishwasher: 400 Washing machine: 2.2 Washing machine (12.7)

Dishwasher: 2.2 Freezer (9.6)

Dishwasher (8.9)

Fig. 4. Schematic representation of the feedback processes occurring when an ECI is applied to a domestic appliance.

828 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

the information; in what format to present the feedback (e.g.

as numbers, graphics, energy/cost/CO2 data); and whether

the feedback should be displayed centrally or at the points of

end use. Many of the early studies in the 1970s concentrated

on giving infrequent written feedback that was displayed

centrally in the home and was not end-use specific. From

the late 1970s onwards a few studies provided continuous

electronic displays, but it was not until the 1990s that PCs

were used to display continuous energy information that

showed the relative consumptions of different end uses.

Darby [49] concludes that electronic rather then paper-based

feedback is the most promising method of disseminating

feedback information about energy usage. However, there is

little published research on how best to display continuous

electronic information on energy-consumption, especially at

the level of an individual activity or appliance. Given the

technological feasibility of doing so and the energy savings

reported by previous investigators, it is considered that

research attention should be focused on identifying

preferred means for achieving electronic feedback at the

points-of-use.

4. Behaviour change in end use groups

Little research attention has been focussed on how best to

influence consumer behaviour when interacting with indi-

vidual electrical appliances or undertaking domestic activ-

ities (e.g. cooking and home laundry). In many respects,

energy-conscious behavioural practices are difficult to

define or not known in detail. Few people know the relative

costs of specific behaviours and the behavioural actions,

which can save energy [13,50]. Arguably energy-conscious

behaviour changes may be best self-taught in one’s own

home and relative to one’s past performance.

It is important for energy users to have accurate informa-

tion as a basis for action [6]. We can start by assuming the

best option would be to label ‘‘all the groceries in the store’’,

i.e. with all appliances indicating how much energy they are

using. However, although an ECI could be applied to each

appliance, the influence a user can have upon energy-con-

sumption varies with each type of appliance/activity.

4.1. Which appliance can consumers influence the most?

The number of energy-conscious behaviour options is

relatively limited when using some appliances (e.g. refrig-

erators), while much more scope exists when interacting

with other equipment (e.g. cooking appliances). The types of

behaviour that can be modified vary from simple changes to

developing more complex skill-orientated behaviours. Hin-

nells and Lane [51] and Lebot et al. [52] suggest that energy

savings can be achieved by locating ‘cold’ appliances wisely

(e.g. not adjacent to an oven) but once located little can be

gained by changing user behaviour. Locating an appliance

may be considered as a one-off energy-saving consideration

that is the least complex of the energy-saving techniques that

a user may develop subsequently.

With ‘wet’ appliances, consumers can choose different

wash temperatures and maximise their washing load per

cycle. With lighting, consumers can prefer a more efficient

luminaire or use timers/occupancy sensors but once the

hardware decisions have been made there are few options

for reducing consumption (e.g. simply remembering to

turn lights off). Thus, the way we can influence lighting

and wet appliances may involve on/off decisions and

slightly more complex choices about lighting levels and

wash temperatures.

While interacting with hot water and space heating sys-

tems consumers may need to modify misguided knowledge

about the heat-transfer/appliance function, sometimes

referred to as ‘folk theories’ [53]. A classic misconception

is that by increasing the set-point temperature of the room-

air thermostat, the rate of space heating will increase. But it

takes the same amount of time for the heating system to

respond—the room just becomes hotter when the air reaches

the set temperature [54]. Also, when a hot-water tap is turned

on, hot water is not necessarily issued due to the heat loss

characteristics of the pipe leading to the tap. Kempton and

Neiman [43] showed that low water volume events are very

inefficient. People may be turning on the hot tap when they

only really want cold water, or they do not know how long it

will take for the water to heat up and cannot wait until the

water becomes hot. Perhaps they only really want warm

water and turn the tap off before it gets too hot. Whatever the

reason for short duration hot-water events, Newborough and

Probert [55] argue that feedback and control of the tem-

perature of the water emerging from hot-water taps would

yield substantial energy and water savings.

Cooking is perhaps the most demanding among the

domestic activities. There are many ways, from easy to

complex, in which one can influence the energy-consump-

tion of a cooking process: using an alternative appliance

(e.g. using a microwave oven instead of a main oven or using

the toaster instead of the grill); planning the cooking more

effectively (e.g. switching off the oven ‘‘5–15 min’’ and the

hotplate off ‘‘2–3 min’’ before the end of the cooking

period); using only pans with flat bases, which provide good

thermal contact with hot plates [56] by simmering food

instead of boiling; and by using well-fitting lids on pans [57].

A research investigation which focuses on information-

feedback in the context of cooking was therefore given

the highest priority.

Detailed energy-consumption data for cooking activities

by type of meal or by appliance are very scarce. Around half

of UK households own an electric oven and hob [58] and

their operation accounts for an estimated 3.4 TWh and

3.0 TWh per annum, respectively [59]. The total annual

electricity consumption of ovens in the UK is amongst the

highest in Europe [60]. Kettles, microwave ovens and small

cooking appliances (toasters, food processors, electric frying

pans, etc.) account for 4.0, 1.6 and 0.9 TWh per annum,

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 829

respectively. Excluding small cooking appliances, a total of

around 12 TWh is used per annum by domestic electrical

cooking appliances.

5. Field investigation

A field investigation was carried out for electric cooking

to address how consumers would react to consumption

information being displayed electronically at the points-

of-use, and further whether the associated energy savings

would be significantly different from antecedent energy-

saving information [61]. The main objectives were to (i)

collect detailed energy-consumption data relating to cook-

ing behaviour; and (ii) to develop and assess three methods

of influencing end users cooking habits, namely:

(1) an appropriate paper-based ‘‘Information Pack’’;

(2) electronic ‘‘energy-consumption indicators’’ for elec-

tric cookers; and

(3) methods (1) and (2) simultaneously.

Suitable households were identified from a postal survey

of over 1000 homes. As the necessary energy monitoring

equipment could be applied relatively easily to free-standing

electric cookers, homes possessing these were identified

for further consideration. Of the 230 respondents, 44 were

willing and appropriately equipped to participate in the

study.

The participants were divided into groups of similar

number and household size and these were referred to as

(1) the Control Group, (2) the Information Pack Group, (3)

the Energy-Consumption Indicator Group, and (4) the Infor-

mation-plus-ECI Group. The monitoring phase spanned 18

calendar months. Group 1 homes were monitored across a

period of 12 months, while those in the three other groups

(associated with methods 1–3 above) were each monitored

for a minimum period of 4 months. For Groups 1–3, the

baseline consumption data was obtained across a period of 2

months prior to the introduction of the information-transfer

methods, and then monitoring was continued for a further

minimum period of 2 months.

The principal functions of Group 1 were to establish a

quantitative understanding of energy-consumption and to

provide a basis for assessing seasonal trends in electricity

use for cooking, while that of Group 2, 3 or 4 was to test an

information-transfer method. The respective groups con-

sisted of 12, 12, 10 and 10 households. Three households

with only one occupant were later excluded from Group 1, as

the electricity consumption for cooking in these three single-

person households was only 0.40 kWh per day (compared

with an average of 1.36 kWh per day for the other 9 houses)

and the number of days the cooker was not used amounted to

156 days per annum (compared with an average of 41 days

among the other nine households).

The study was designed to minimise Hawthorne effects—

the participants received no direct instructions to monitor or

save energy and were approached as little as possible at the

points of installation and downloading. Once the households

had been approached and had agreed to take part in the

monitoring programme, an initial visit was made to each

participating household with a qualified electrician. During

this, the logging equipment was installed, the resistances of

each heating circuit within the cooker were measured, and

monitoring was initiated. The electricity consumption of the

cooker was measured every 8 s, averages were logged every

3 min and the consumption data was downloaded to laptop

PC via further visits at two monthly intervals. Once the data

collection process was complete for that house, a final visit

(with the electrician) was made to return the electrical

connections to their original status.

After the data had been collected the people who had been

involved in the study were asked to comment on the study.

The two questions asked were (a) had they found the

information they received useful? and (b) how had they

reduced their energy-consumption/achieved their savings?

5.1. Design of the Information Pack

The Information Pack was based on the findings of a

literature review, a cooking behaviour questionnaire and a

laboratory evaluation to establish the typical energy require-

ments of food-preparation practices. The pack was presented

as a 17 page, laminated colour A4-sized booklet divided into

four sections.

The first section of the Information Pack concentrated on

information about the electricity consumption of cooking

appliances and how they worked. The introduction to Sec-

tion 1 described what a kWh was, how much electricity the

average household uses and what proportion of this elec-

tricity was used for cooking. Section 1 then went on to

explain in layman terms, how much energy is used by

different cooking appliances. Particular attention was given

to hotplates, electric ovens and microwave ovens. Consump-

tion information was given for various settings and cooking

times for these three appliances. Although ranges of appli-

ance power ratings were given, the reader would still have to

know the power rating of his/her appliance to estimate its

electricity consumption—the booklet could only serve as a

guide. The last part of Section 1 also mentioned briefly how

a toaster, grill, kettle, slow cooker and deep-fat fryer worked.

Section 2 was based on electricity saving tips. The tips

centred on which appliance used the least electricity (e.g.

‘‘use a toaster rather than a grill’’); ways to reduce time spent

cooking (e.g. ‘‘try switching off hotplates 2–3 min early’’);

matching the size of the food portion and the pan to the

‘ring’; not using unnecessary amounts of water in kettles and

pans; simmering food not boiling it; not opening the oven

while in use and ensuring the oven door had a good seal.

The third section identified electricity consumption values

for some typical meals/foods and tables presented kWh data

for five common types of cooking operation (roast chicken;

roast chicken, potatoes and carrots; potatoes only; chicken

830 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

stew casserole and ready-made chilled foods. The tables also

facilitated comparisons where alternative methods of cook-

ing might be applied (e.g. baking in a microwave oven and

baking in a conventional oven).

Lastly Section 4 provided a table, which gave the user

an opportunity to estimate the electricity consumption of

cooking appliances in their home. Power ratings of the

various cooking devices were shown alongside various

usage times and the corresponding electricity consumption

per operation. There was a space for subjects to make their

own estimation of total electricity consumption per week

(Table 2). (Table 2 demonstrates the difficulty in presenting

comprehensible written information about the energy-con-

sumption of various devices. Information cannot be pro-

vided that is specific to the householder’s appliance. Written

information can prompt a person to think about an energy-

using event, but there is a danger that by attempting to be

comprehensive about a single domestic activity the informa-

tion itself becomes too complex to hold the interest of the

average reader.)

5.2. Design of the energy-consumption indicator

There is a plethora of design guidelines for general

interface design much of which is based on cockpit design

[62] and PC interface design [63]. However, Lohr [64]

extracts three main principles from the available informa-

tion.

(a) Group the sections (interface elements) so that the

learner has an overall idea of what the environment is

like.

(b) Make the background and foreground distinctions clear

so that the learner will attend to the signal.

(c) Organise individual elements into comprehensible

sections.

When designing the energy-consumption indicator (ECI)

these general design objectives were considered. The display

had to be visible whilst cooking, attention grabbing and

easily comprehensible, well ordered yet practical and cost

effective. (The detailed design aspects of the ECI are

described in Appendix A.)

The most important information was expected to be the

consumption information for the current event that the

consumer was carrying out. There are perhaps three types

of interacting behaviour that consumers might adopt when

given an ECI:

1. A type of vigilance task, which occurs especially when

the user is still trying to learn or understand how the ECI

responds to his/her actions. For example, putting the

food on to cook and focusing mainly on the ECI display

to observe any changes that might occur.

2. Whilst carrying out a primary cooking task, the user

may occasionally carry out the secondary task of

glancing at the ECI.

3. Although people can for example drive and talk at the

same time it becomes very difficult to take in two stimuli

via the same sense, e.g. it is difficult to listen to two

conversations at the same time. Bainbridge [65] suggests

that it is therefore better to assume that people cannot do

two similar tasks at the same time (i.e. observe/prepare

food and watch the ECI). This might mean that some

Table 2

Section 4 of the Information Pack showing the power rating, typical usage (in min) and electricity consumption of various cooking appliances

Appliance Typical power

rating (W)

Typical usage

(min)

Electricity usage per

operation (kWh)

Electricity consumption

per operation

Typical usage in

your home per week

Your cooking electricity

consumption per week

Hot plates (ring)

Small ring 1000–1500 45 1.0

Large ring 1600–3000 15 0.6

Main oven

Low setting 1750–2500 120 1.2

High setting 90 2.0

Second/top-oven 1850–2400 60 1.5

Microwave oven

Low setting 1000–1500 15–45 0.1–0.4

High setting 10–45 0.02–0.9

Grill 1400–2900 15–30 0.4–1.5

Toaster 800–950 2–10 0.02–0.2

Electric kettle 1800–2400 3–5 0.2

Slow cooker 100–150 360–480 1.0

Deep fat fryer 1800–2000 10–30 0.8–1.5

Food processor 150–300 3–10 0.01–0.05

Electricity consumption of cooking appliances. For comparison, the approximate consumption figures, power ratings, and typical operating times for a range

of cooking appliances are given in the table above. Use this information, consider the typical usage patterns of cooking appliances in your home, and estimate

their weekly electricity consumption. The exact consumption will depend on the type of appliance, and the food being cooked.

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 831

people may choose to look at the display only after the

cooking event had finished. Similarly, it is plausible that

the user may simply want an infrequent feedback option

in order to observe daily or weekly consumption when

convenient to do so, and then analyse the energy-

consumption on a more systematic ‘accounting’ basis.

To cater for behaviours (1) and (2), a frequently updated

‘This Event’ display was incorporated which showed the

current electricity consumption. To facilitate behaviour (3),

the ECI needed to enable the user to note the final energy

usage of the task and then clear the screen for the next task.

Therefore, the This Event display was designed with a reset

button. This display was positioned towards the top left

region of the ECI as this was considered the most likely to

be read first. In addition, daily and weekly consumption

information was also provided by the ECI. It was termed

‘Today–Yesterday’ and ‘This Week–Last Week’, so that the

most recent data was positioned nearest the top of the ECI

(Fig. 5). The Today display (which was the running total for

that day) was placed directly under the This Event display

and could be compared to the Yesterday display (which

showed the previous day’s totals electricity consumption).

The This Week display (which total all electricity usage for

that week excluding today) was placed to the right of

the Today display and this could be compared to the Last

Week display (which identified the total electricity usage

for the previous week). A tan coloured line also linked the

This Event, Today and This Week displays. In this way, it

was considered that the user might be drawn to look at

further comparative displays after they had looked at energy

usage for the current event.

It was decided that the ECI should display energy-con-

sumption in kWh to two decimal places (see Appendix A).

Energy values were derived from a current transformer,

which was attached to the mains supply cable to the cooker

and connected to the ECI. A mean voltage of 240 V was

assumed in the energy calculation (E ¼ VIt), although in

practice the voltage will vary with location and fluctuate

with time. (Accordingly, the potential inclusion of ‘thou-

sandths’ of 1 kWh in the display was inappropriate as this

small amount of power would be spurious.)

The final ECI display unit is shown in Fig. 5. It is a plastic

20 cm � 14 cm inch display unit, with five identical LCD

displays (of 4.5 cm � 1.75 cm) each surrounded by yellow

borders. The displays indicate energy-consumption for This

Event, This Week, Last Week, Today and Yesterday. The

This Event display is the most prominent and has an adjacent

red button for zeroing purposes.

6. Results

For Group 1 (i.e. nine non-single-person households),

taking summer consumption as the base line, the average

daily consumption (for days that the cooker was used) was

Fig. 5. The energy-consumption indicator.

832 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

11, 9 and 22% higher in spring, autumn and winter, respec-

tively. The relative change in consumption with seasonal

quarter varied among the participating homes, but 8 of the 9

non-single-person households showed a significant increase

in winter.

Clearly, it is difficult to define exactly when the seasons

change and how external conditions influence users of

cooking appliances, but the average number of days per

season when the cooker was not used varied only slightly;

e.g. from 10 days (�11%) in winter to 14 days (�16%)

in summer. Overall, it appears that the average daily

energy-consumption fell into three broad categories

‘‘low’’ (July and August), ‘‘intermediate’’ (February until

June and October and November) and ‘‘high’’ (December

and January). For the intermediate level, the average daily

consumption was surprisingly consistent lying in the range

1.52–1.56 kWh for 7 of the 12 months.

The results for the three information transfer groups are

summarised in Table 3. For the Information Pack Group, the

‘seasonally adjusted’ consumption per day (for days that the

cooker was used) decreased in eight of the households.

In three cases the decrease was 12, 13 and 13%, while five

houses indicated savings of 1 to 4% and four houses showed

increases of 1–7%. For the ECI Group, the seasonally-

adjusted consumption per day (for days that the cooker

was used) decreased by more than 10% in seven of the

ten households, ranging from 11 to 39%. Only two of the

houses showed an increase of 6 and 9%, respectively. Hence,

a significant majority of Group 3 households were able to

save energy. It would appear that the ECI was preferable to

the Information Pack as an energy-saving tool.

For Group 4, the seasonally-adjusted energy saving ran-

ged from 27% to �31%. Four homes achieved savings of

15–27%. (Unfortunately, a corrupted set of data downloaded

Table 3

The recorded changes in electricity consumption for Groups 2–4

Household reference code and the

number of adults in the stated household

(with number of children indicated

in brackets)

Duration of baseline monitoring

period immediately prior to

the experimental phase

(number of days)

Duration of experimental

phase (number of days)

Recorded change in average daily

energy-consumption between baseline

monitoring and the experimental phase

for days that the cooker was used (%)

Group 2: Information Pack

H-11 2 (þ2) 72 68 �13

H-25 4 89 68 �13

H-2 3 74 68 �12

H-19 2 83 68 �4

H-29 2 78 68 �3

H-35 2 (þ3) 68 57 �3

H-34 3 (þ2) 56 67 �2

H-24 2 (þ2) 72 68 �1

H-6 2 73 63 þ1

H-1 2 (þ2) 72 68 þ2

H-20 2 (þ2) 73 68 þ5

H-8 2 73 68 þ7

Group 3: Energy-Consumption Indicator

H-5 2 82 56 �39

H-15 2 73 57 �26

H-30 2 69 83 �25

H-4 2 (þ2) 82 56 �23

H-17 2 70 62 �23

H-16 3 92 61 �15

H-7 3 71 64 �11

H-3 3 (þ2) 81 56 �5

H-13 2 72 56 þ6

H-10 2 (þ1) 58 84 þ9

Group 4: ECI and Information Pack

H-21 2 68 104 �27

H-9 2 74 69 �18

H-26 2 60 57 �17

H-23 2 68 57 �15

H-33 2 (þ1) 56 69 �5

H-31 2 (þ1) 57 68 �2

H-28 2 (þ1) 109 69 þ5

H-32 2 58 75 þ8

H-36 2 (þ2) 68 69 (þ31)

H-27 3 61 55 Excluded due to data corruption

during downloading

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 833

Fig. 6. Importance placed on stated part of the ECI display as reported by the participants at the end of the investigation.

Fig. 7. Energy-saving actions as reported by the participants at the end of the investigation.

834 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

from one house reduced this group to nine and the total of

the surveyed information-transfer households from 32 to 31.

Also the increase in consumption of 31% in one case

should be treated with caution as this was due to a distinct

lifestyle change that occurred during monitoring.) There

does not appear to have been a compound effect from

providing both ECIs and Information Packs to households

in this group.

The post-experimental survey of the participants pro-

duced many comments. Some participants said that they

had found the study useful but did not go into any detail

about what they did with the ECI or Information Pack.

For Group 2, 80% of respondents said that they used the

energy-saving tips the most. For Group 3, Fig. 6 shows the

number of specific comments about the displays on the ECI.

It can be seen that, in the ECI and Information-plus-ECI

Groups, the This Event display was mentioned the most. If

recall of this part of the ECI was high, then this may suggest

that the participants were attracted mostly to this display

and/or the learning function was greatest from this part of

the ECI.

Figs. 7 and 8 show the types of behaviour that people said

they had modified as a result of participating in the study.

It can be seen that the ECI Group recalled five methods of

behaviour change while the Information-plus-ECI Group

recalled four. However, the Information Pack Group

recalled the maximum number, i.e. eight ways in which

they changed their behaviour. The types of behaviour

specific to Group 2 were reduction of oven cooking time,

putting lids on pans and simmering water in pans instead

of boiling it.

The Information Pack Group seemed to be aware of more

complex energy-saving behaviours as well as those beha-

viours taken up by the other two groups. However, although

this group gained greater knowledge of energy-saving tech-

niques they did not put these behaviours into practice as

much as the other groups. This suggests that the ECI

increases motivation to carry out energy-saving behaviours

compared with antecedent information alone.

7. Conclusions and further recommendations

The literature review and the field study undertaken in this

investigation indicate the potential for applying information-

feedback for reducing rates of energy-consumption in the

home. The findings concerning cooking suggest that the use

of electronic feedback indicators deserves further attention

and optimisation for this domestic activity. The study raises

several questions for further research, but also reveals some

interesting data concerning energy use in cooking.

From Group 1 data, the main parameters influencing the

annual energy-consumptions for domestic cooking appear to

be household size, the relative use of microwave ovens, the

proportion of large meals (e.g. roast dinners) prepared and

the number of days per annum that the cooking equipment is

used. The average daily demand varies with time (e.g. it was

35% greater in winter than in summer and the average

Sunday value was nearly twice that of the average weekday

value). To better understand the relative importance of the

influencing factors, it is recommended that a monitoring

programme for a larger group of households (e.g. >100) be

undertaken for a period of at least 1 year.

From the data associated with Groups 2–4 it is clear that

significant proportions of households were able to reduce

electricity expenditures on cooking (see Table 3). Overall,

14 out of 31 households achieved energy savings of greater

than 10%, and six of these achieved savings of greater than

20%. In Group 2, three out of 12 households achieved

savings of greater than 10% and the participating households

indicated that they had attempted to change a variety of

micro-level practices.

The ECI appears to have been much more effective than

the Information Pack. In Group 3, seven out of 10 households

Fig. 8. Indication of how frequently each digit of an LCD display would change for a domestic electric cooker, if it were designed to indicate electricity

consumption in kWh (up to three decimal places). (The maximum and minimum time values correspond to the maximum and minimum consumptions

achievable.)

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 835

achieved energy savings of greater than 10% and half the

group, accomplished reductions of 23% or greater. The

maximum saving for household number 5 in this group

was 39%. Although the ECI Group showed a high level

of savings in electricity, the participants had knowledge of

fewer energy-saving micro-level practices than those in

Group 2. In Group 4, four out of nine households realised

savings of greater than 10% but only one household

achieved a saving of greater than 20%. Savings were greater

in the ECI Group compared to the Information-plus-ECI

Group. Knowledge about energy-saving methods was very

limited in Group 4, suggesting that participants had paid

little attention to the information booklet. The participants

may simply have preferred the ECI and/or been over-

whelmed by having both an ECI and a booklet.

In most cases, positive feedback was received from the

participating households regarding the ease of utilising the

ECI and/or the Information Pack. No one reported back that

they had tried to save energy but found the process irksome.

Instead it appears that individuals soon established the level

at which they wished to use or interact with the ECI or

Information Pack and then proceeded to do so. It is clear that

the three tested information-transfer methods can influence

significant proportions of consumers to save energy when

cooking. Moreover, they can achieve this without necessi-

tating the purchase of a new (i.e. more energy-efficient)

cooking appliance or requiring end users to engage in some

arduous energy-saving procedure.

Given the aforementioned peak power demands emanating

from domestic cooking, demand-side management as well

as energy-efficiency benefits may accrue from modifying

the ways in which consumers use cooking appliances. The

potential carbon reductions for UK households (assuming

12.2 million households own electric hobs and ovens) have

been estimated for each experimental group (Table 4). These

savings assume the UK household’s electricity consumption

on electric hobs and cooking amounts to 6.4 TWh per annum

[66] and that the emissions factor is 0.44 kg CO2/kWh [67].

A potential saving of 0.16 Mt C per year is estimated if ECIs

were employed with all domestic electric cookers. (However,

it should be noted that this estimate does not take into account

any additional energy-consumption due to increased use of

other small cooking appliances.)

Several questions remain about the human–display inter-

action that occurred in this study, and these need to be

addressed if the ECI approach is to be optimised and

replicated across the domestic sector. For example, how

frequently did people look at and interact with the ECI? To

what extent was the ECI used (i) during and (ii) after a

cooking event? Did users readily understand how the dis-

plays on the ECI related to each other? When people used

the This Event display during a cooking event, were they

distracted by the other displays? How did consumers use the

displays to assess their cooking methods? Might dials, rather

than digital displays, have been more effective? Would

consumers respond positively to being informed by the

ECI about more complex energy-saving tips rather than

being given information on paper? Is it best to develop a

simple design of ECI and to encourage individuals to

innovate their own energy-saving practices by experimenta-

tion rather than to provide detailed advice?

It is desirable to establish the effectiveness of: (a) incor-

porating ECIs within the user interface of domestic cookers;

and (b) utilising the ECI as a general diagnostics tool for

householders to gain a relative understanding of the con-

sumptions of individual appliances and how they may

reduce them. It is considered that a further detailed study

should be undertaken to better understand how consumers

interact with, and are influenced by, ECIs and hence how to

optimise their energy-saving potential. It is recommended

that the approach of this study be applied to other domestic

appliances/activities (e.g. home laundry and hot-water use)

in order to assess whether similar energy savings can be

achieved by point-of-use information-transfer methods.

In general, it is considered that further attention should be

given to assessing information-transfer methods that com-

bine the frequently-updated appliance-specific feedback

interface (to increase motivation to save energy) with fea-

sible energy-saving tips at the points-of-use (to increase

knowledge about energy-saving methods). For example,

energy-saving tips might be displayed on a drop down menu

whilst the user is new to the ECI, and then hidden by the user

once he/she knows about the tips or has modified certain

energy-use practises. This feature would play a secondary,

but potentially important, role to that part of the display that

indicates energy-consumption.

Overall, it is considered that the findings of this investi-

gation support its original premise that a major untapped

route for achieving energy savings in the domestic sector

is to identify and implement means for influencing end

users before/during/after they use appliances. It is therefore

recommended that policymakers develop and implement

Table 4

Comparison of the relative savings of the three tested information-transfer methods

Information-transfer

method

Number of

house holds

Average overall consumption change

(%; with greatest change per

household indicated in brackets (%))

Proportion of experimental

group that achieved

an electricity saving (%)

Average saving for

households that did

save electricity (%)

Potential annual savings

in the UK for electric

cookers (Mt C)

Information Pack 12 �3 (�13) 66 6.4 0.05

ECI 10 �15.2 (�39) 80 20 0.16

Information-plus-ECI 8 �8.9 (�27) 75 14 0.11

836 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

actions specifically focussed on the points-of-use alongside

those already applied at the points-of-sale (e.g. energy

labelling and minimum efficiency standards).

Appendix A. Detailed design considerationsfor the ECI

When designing a display there are several methods of

attracting the attention of a person who is carrying out a

task. Schneider and Shiffrin [67] define selective attention as

‘‘the control of information processing so that a sensory

input is perceived or remembered in one situation rather than

another’’. The memory system has a limited capacity for

information. Thus, people must select which bits of infor-

mation are important enough to store in their active mem-

ories [67]. Broadbent [68] theorised that information could

only be attended to from one source at any given time and

that when there are many incoming information sources

attention determines what information reaches the working

memory. However, it is known that a person can process

information from more than one stimulus source and thus

can undertake two different tasks at the same time. Some of

the key aspects of attracting attention to displays may be

summarised as follows.

� We may be drawn towards an unusually large piece of

text [64]. It is also important that text or numerals in a

display are legible from a comfortable viewing distance

[69].

� Something that moves or flashes will attract attention

more than something that is stationary. This is a common

technique used extensively on the World-Wide Web [63].

However, this flashing can quickly become annoying

[70].

� Highlighting can draw the user towards the information

[71].

� Colour spots against a light grey or muted field serve to

highlight data [63]. Colour also produces shorter search

time than other modes of encoding information, e.g.

shapes or numbers [72,73]. Colour coding can be used

if particular objects on the display are to be perceived as

being related [63].

� Number of colours—a trained colourist can distinguish

between 1,000,000 colours [74]. However, too many

colours on a display can increase search time [75] and

more than 20–30 colours proves detrimental to under-

standing the information presented [74]. The maximum

number of colours suggested for air traffic control dis-

plays is six [76] and up to four colours for a PC display

[63].

Appropriately designed ECIs should attract the attention

of end users during energy-consumption events, and enable

them to obtain the information needed to modify behaviour

and so reduce energy-consumption. If the main form of

interaction is likely to be an occasionally glance at the ECI,

then attention must be attracted to salient points on the

display in order to reduce search time for relevant data.

The final design of ECI had a dark background with five

digital displays; all informational displays were highlighted

using the same bright yellow colour surround. A red colour

was chosen to highlight the reset button for the This Event

display, while less salient aspects (such as the name of the

ECI) were surrounded by a dark green colour (see Fig. 6).

The total number of colours used was four. Lastly, it was

advantageous to have digits that were of sufficient size to

attract the user to the display. Clark and Corlett [69] state

that a comfortable viewing distance for a visual display is

400–700 mm, but it was considered here that the digits on

the ECI should be readable from a distance of up to 2 m so

that the end user could observe the ECI whilst carrying out

other food-preparation activities in the kitchen. A width to

height ratio of 0.7:1 was used for the numerals as recom-

mended for optimum legibility and detection [69].

A.1. Comprehension

There were two major design aspects to consider in order

to ensure easy comprehension by the consumer. Firstly,

which would be the most understandable units that would

convey electricity consumption (e.g. kW, cumulative kWh,

pence and pounds, grams of CO2). Secondly, what was the

display symbology to be (e.g. dials, digital numbers, smiles,

flashing lights, graphics)? These were the main character-

istics that had to be considered within the constraints of

practicality and cost for the field trial.

The units that the ECI displayed were central to the user

being able to understand the display. Although saving

money is often the primary motivator, if energy were to

be displayed in pence and pounds the user may have been

dissuaded from saving energy due to the small financial

savings. For example, using a single hob for 1 h costs the

consumer only a few pence and so an energy-saving action

may well save only in the region of 1 penny/h. If the

consumer observes that they are only saving pennies per

day then they may be demotivated, rather than motivated, to

save energy. Alternatively, the display could have been in

grams of CO2 (associated with fossil-fuel derived electri-

city). However, this would be very difficult to implement

accurately, because the proportion of fossil-fuel generated

electricity and the overall system efficiency vary with time

of day and geographical location. With a conventional

electric cooker, it is feasible to simply display the power

demand (in kW). Unfortunately, initial trials indicated that

this form of display was difficult to understand, because the

user has no direct control over power during a single cooking

event. The heating elements are controlled thermostatically

or on a duty cycle, and once the desired setting has been

reached the power input will cycle. Thus, during a single

operation, zero would be displayed intermittently on the

ECI and so a kilowatt display would tend to confuse the user.

G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841 837

By comparison, displaying kWh on a cumulative basis over-

comes most of these problems; it was therefore preferred in

this study.

When interacting with displays users engage in three

types of reading:

(1) Quantitative reading: This is when the subject obtains

or searches for an exact numerical value from the

display.

(2) Check reading: The subject observes a rate of change

of the value on the display.

(3) Setting: The subject detects a direct relationship

between a control setting and a value on a display.

For each of these there is an optimal type of display, which

is most suitable for the reading task. However, it was not

known which type of reading the subjects would carry out

and so an inclusive approach was adopted. A moving scale

with a static pointer was rejected as a display option as it was

only fair-poor for all reading types (see Table 5). It was

considered that a dial or digital display would be more

comprehensible for the This Event display, as users would

probably be just checking to see how fast the energy was

being used during a cooking event rather than obtaining

exact numerical values. Although visually distinct and

available in a variety of colours, light-emitting diodes

(LEDs) are of relatively high power input, which for the

field study would have necessitated employing a large

battery or connecting the ECI to the mains supply. It was

desirable to keep the ECI as user-friendly as possible and

was considered inappropriate to make it subject to power

cuts or the participant disconnecting the mains supply. So

liquid crystal displays (LCD) and a small battery were

employed, which permitted the ECI to operate in the field

without attention for several weeks.

When deciding how many decimal places for each display

of energy-consumption, a range of practical, informative

and human factors had to be considered. The use of a

cooker can generate a wide range of energy-consumption

values per day. Suppose the subject uses all the hotplates, the

oven and the grill while preparing meals for several guests.

Potentially, the ECI display might need to present a con-

sumption of greater than 10 kWh. Conversely, one hotplate

used briefly for one event may only require 0.1 kWh or less.

Thus, the ECI has to exhibit consumption from tenths of a

kWh to greater than 10 kWh per day.

As mentioned previously a subject who is using a new

display or learning a new skill may stare at the display for

relatively long periods. This could be likened to a vigilance

task—the subject will attend to the display, which will

present digits at a regular update rate, and learn to expect

a regular update in consumption (an increase in the digital

value). There is an optimal signal detection rate for a user

during a vigilance task. Baker [80] observed people in a

clock watching experiment to see if changing the signal rate

influenced the number of times the subjects detected a

signal. In this test a black second-hand swept continuously

around the face of a clock. The critical signals were brief

stops of the second-hand for 200, 300, 400, 600 or 800 ms.

Baker found that the failure to notice a critical signal was

more pronounced with brief signals of 200 ms than for those

of longer duration 600–800 ms. Warm [17] carried out a

similar search experiment and found that his subjects had

100% correct detections when the signal lasted for 4–8 s.

Jerison and Pickett [81] reported that with an event rate of

once every 2 s the subjects showed only a 20–60% critical

signal detection rate, whereas when the event rate was

slower at one event every 12 s the critical signal detection

rate was 80–100%. In both cases, the number of critical

signals was one every 15 s. Further studies have confirmed

that the detection probability of critical signals decreases at

small time intervals [82,83].

It was necessary for the subjects to detect an unusual

increase in electricity consumption (a critical signal) amidst

a regularly changing readout of kWh (event rate). The results

of Warm [17] and Jerison and Pickett [81] suggest 8–12 s as

a guideline for the signal rate (i.e. change in kWh display).

The event rate on the ECI was set at one update per hour in

the tens of kilowatt–hours space. The range of possible

signals was assessed by studying the typical power inputs

required by some standard electric cookers. Of 12 common

Table 5

The suitability of displays in relation to the type of reading a user may undertake [77,78]

Methods of use Counter Moving scale with static pointer

(circular and linear)

Static scale with moving pointer

(circular and linear)

Quantitative reading Good Fair Fair

Qualitative reading Poor Poor Good

Setting Good Fair Good

Carveth and Adams [79] state that a static circular scale with a moving pointer should be preferred for quantitative reading as opposed to a linear scale with a

moving pointer.

838 G. Wood, M. Newborough / Energy and Buildings 35 (2003) 821–841

electrical cookers, the lowest average input for one hob

was 1.2 kW while the greatest was 10.9 kW for four hobs, a

grill and an oven in simultaneous use. This indicates the

range of rates that needed to be shown on the display.

With only one hotplate operating, the update rate of the

display digits will be the minimum update rates shown in

Fig. 8. With all four hobs, the oven and the grill energised

the update rates would become the maximum values shown

in Fig. 8.

Miller’s investigation in 1956 [84] is perhaps the most

quoted paper, which addresses the issue that people have

only a limited capability for storing chunks of information.

He suggests that we can store 7þ/�2 chunks of information

at a time. MacGregor [85] suggests that our working mem-

ory has a limit of four to six items. Lecompt [86] also

disagrees with Miller [84] and suggests that for interface

design a 7þ/�2 capacity is an overestimate and a poor

heuristic for design. A more useful guideline for the limit to

working memory capacity is approximately three informa-

tion bits [85]. This number of items will almost always be

remembered, even by individuals with certain types of brain

damage [87]. In order to follow the Lecompt [86] 3-bit rule,

the maximum number of changing digits that should be

allowed was three. Thus, for each display on the ECI, digit

spaces of ‘‘ones’’, ‘‘tenths’’ and ‘‘hundredths’’ of 1 kWh were

supported. The ‘‘tens’’ of kWh digit, which was normally

expected to change at intervals of several days, was also

included on the ECI but this was not considered important

in terms of the user’s working memory.

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