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Energy and Buildings 36 (2004) 161174

Weather sensitivity in household appliance energy end-use

Melissa Hart, Richard de Dear

Division of Environmental and Life Sciences, Macquarie University, Sydney, NSW 2109, Australia

Received 17 July 2002; accepted 31 October 2003

Abstract

Data from a Residential Energy Study (RES) were used to examine the weather sensitivity of various household appliances located in

householdswithin the Sydney metropolitanarea. Thermal environmental indices effective temperature (ET), standard effective temperature

(SET) and simple air temperature degreedays were used to quantify the dependence of household appliance energy consumption on

outdoor weather. Specific appliances included: room air-conditioners, room heaters, refrigerators, freezers and domestic hot-water systems,all of which exhibited some degree of weather sensitivity, particularly space heating and cooling devices. Probit regression techniques were

used to predict the degreeday values at which households tend to switch on heating and cooling appliances. All appliances demonstrated

weather sensitivity to varying degrees, and this was universally stronger during the cooling season (summer) than during the heating season

(winter). The outdoor SET version of the degreeday index demonstrated a stronger statistical association with space-cooling energy

consumption than conventional air temperature degreedays. The mean daily temperature associated with minimum heating and cooling

energy consumption for Sydney indicated that a temperature of 18 C was the most appropriate base temperature for calculation of both

heating and cooling degreedays.

2003 Published by Elsevier B.V.

Keywords: Degree-day; Weather sensitivity; Household appliance; Residential Energy Study (RES)

1. Introduction

Australian electricity suppliers Pacific Power and Sydney

Electricity carried out a Residential Energy Study (RES) in

New South Wales (NSW) during 19931994 [1]. The study

involved directly metering the energy end-use of household

appliances in a sample of houses across NSW. A prelimi-

nary study on the database commissioned by the Australian

Greenhouse Office (AGO) examined the patterns of us-

age and energy consumption of the appliances [1]. Whilst

weather sensitivity was not the focus of that study, it found,

inter alia, significant summer and winter peaks in energy

consumed by many of the appliances and this prompted

the present study. The present paper specifically examines

weather sensitivity in electricity consumed by a selection

of the household appliances monitored in that study within

the Sydney metropolitan region. The data collected dur-

ing the RES was analysed alongside concurrent weather

data collected in close proximity to the households being

studied. Statistical relationships observed between outdoor

weather and individual appliance energy consumption for

Corresponding author. Tel.:+61-2-9850-7582; fax: +61-2-9850-8420.

E-mail addresses: [email protected] (M. Hart),

[email protected] (R. de Dear).

an 18-month sample period are used to quantitatively definethe weather sensitivity of appliance energy consumption.

1.1. Weather sensitivity of electricity consumption

In most electricity systems the residential sector is one of

the main contributors to system peaks [2]. Usage patterns of

many household appliances such as heating, ventilation and

air-conditioning (HVAC) are expected a priori to be affected

by outdoor weather variations. Energy consumption will

depend on building envelope characteristics and occupant

behaviour. The latter is subject to myriad influences includ-

ing householders subjective comfort preferences [3,4], their

socio-demographic characteristics [5,6], subtle cognitive

factors [7] and even cultural dimensions [3,8,9]. Yet, despite

the plethora of human factors impinging on energy consump-

tion, the presence of thermostats in certain appliances such

as domestic hot-water systems and refrigerators establishes

their sensitivity to weather to varying extents, depending on

building envelope thermal performance. Obviously, rates of

heat exchange between these appliances and their indoor

environment are also affected by the heating, ventilation and

air-conditioning (HVAC) system in operation at the time.

Fourteen different electrical appliance types were moni-

tored in the original 19931994 Residential Energy Study

0378-7788/$ see front matter 2003 Published by Elsevier B.V.

doi:10.1016/j.enbuild.2003.10.009


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162 M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174

(RES) database upon which the present study is based. Of

these, room air-conditioners, room heaters, refrigerators,

stand-alone freezers and domestic hot water were selected

for analysis in this study. These decisions were made partly

on the basis of the a priori logic outlined in preceding

paragraphs, but were also informed by preliminary pilot

tests on the RES database. The RES involved an initialtelephone survey of 600 households, as part of a house

selection process and to establish the broad demographic

and socio-economic characteristics of typical households

in NSW. For households involved in data collection, eight

channel data loggers were used with the last channel used to

monitor total household load. The duration of data available

for each household ranged from a few days to the entire

monitoring period of 18 months. Of the appliances moni-

tored only 8% had complete records without any apparent

errors or missing half-hourly readings [1].

The present study is the first of its kind in Australia

where directly monitored energy consumption is analysed

alongside concurrent weather observations. Awareness ofthe weather sensitivity in energy end-use can provide more

information on actual in-use energy consumption for com-

parison with laboratory measurements on domestic appli-

ances. This knowledge may also provide practical benefits

in relation to the implementation of testing procedures

for Australias Minimum Energy Performance Standards

(MEPS) and energy efficiency programs such as star rating

energy labels. Similarly, results from this research may be

of use to international energy efficiency programs such as

the European Energy Performance of Buildings Directive.

The relationship between heating and cooling energy con-

sumption and outdoor weather, along with threshold tem-peratures at which occupants begin to heat and cool their

house may potentially be useful for power utility companies

in predicting system spikes and peaks in the Sydney market.

It has been mandatory in Australia for all refrigerator,

freezers and electric hot-water systems manufactured since

1999, and air-conditioners manufactured since 2001 to meet

MEPS. The introduction of more energy efficient appliances

into many Australian households since the original RES

monitoring programme may have an influence on current

total energy consumption of appliances; however, the corre-

lation coefficients between energy consumption and outdoor

weather discussed in this paper, the weather sensitivity of the

appliances, will have no change. Australian households con-

sume a large amount of standby energy, eight percent of resi-

dential greenhouse gas emission are contributable to standby

power [10]. This level of standby power, which generates

greater internal heat during both seasons, will have increased

since the original RES. This increase in internal heat may

have a slight influence on appliance energy consumption, or

may have a slight influence on the temperature at which oc-

cupants decide to heat or cool, however this influence would

be small and once again these changes would not affect the

overall sensitivity between appliance energy consumption

and outdoor weather which is the focus of this paper.

1.2. Literature review of electricity end-use weather

sensitivity

Previous studies have tended to focus on either total

household energy consumption or heating and cooling en-

ergy consumption patterns. A study in Canada [11] analysed

the impacts of appliance efficiency and fuel substitutionon residential end-use energy consumption. But rather than

directly measuring appliance electricity usage, Ugursal and

Fung relied mainly on numerical simulations. A similar

Residential Energy Consumption Survey [6] by the US

Energy Information Administration (EIA) surveyed con-

sumption patterns with questionnaires directed to end-users,

including information on the energy-related characteris-

tics of building envelopes, household characteristics and

behaviour patterns. A computer program developed by

Princeton University, PRISM (PRInceton Scorekeeping

Method) [12] uses input of monthly energy billing data and

average daily temperatures to produce a weather-adjusted

index of energy consumption. The program returns a heat-ing reference temperature for heating or cooling, that is the

average outside temperature at which the buildings heating

and cooling systems commence. Previous studies have also

looked at broad climatic zones [6,13] or used climatological

normals [14] from historical weather data, rather than con-

current weather observations. Despite these methodological

differences from the present study, key findings include dif-

ferences in space heating and cooling energy consumption

within different climatic zones [6,13]. The effect of each

of the weather variables: air temperature, relative humidity

and wind speed, on electricity consumption was found to

differ across climate zones [14].In a similar monitoring program to Pacific Powers Aus-

tralian RES underpinning the present study, the Household

Energy End-Use Project (HEEP) in New Zealand directly

monitored individual appliances in over 100 households for

a period of 4 years to date, analysis is ongoing. The anal-

ysis included building and socio-demographic characteris-

tics of the households [5]. However, emphasis in the anal-

ysis and discussion was placed on individual appliances

energy consumption in relation to indoor, rather than out-

door temperature. Actual household energy use and temper-

ature measurements are correlated to an energy use model:

ALF3, an annual loss factor model used for calculating heat-

ing energy requirements of buildings in different climate

zones [15].

A domestic electricity end-use measurement campaign

undertaken in France [16] over 12 months in 1994/1995,

monitored eight appliances every 10 min in 94 households

for a duration of 1 month for each household. Appliance

energy consumption was compared by season, winter and

summer, but the authors did not analyse their data along-

side concurrent weather observations. Because the research

design was cross-sectional with a short sample duration of

just 1 month, comparisons of individual appliance energy

consumption could not be made on either synoptic weather


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M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174 163

or seasonal climatic time scales. The study did not include

electrical space heating and cooling nor domestic hot-water

appliances. Results included a breakdown of residential elec-

tricity consumption by end-use and an estimate of the annual

running cost of each end-use, taking into account different

hourly electricity tariffs.

More recent end-use campaigns in Europe have assessedpotential reductions in energy consumption by replacing

regular household appliances with the most energy efficient

examples available on the European market. The French

ECODROME project monitored appliance electricity con-

sumption in 20 households over 2 years [17]. Current plug

loads and the electric light circuit consumption were moni-

tored for the first year. At the beginning of the second year

all appliances and light bulbs were replaced by the most

efficient alternatives available on the European market. Re-

sults indicated that the average annual energy saving per

house from the use of efficient equipment was 1800 kWh

per year. A more recent project, EURECO, followed a sim-

ilar methodology and approach to ECODROME. However,rather than replacing individual appliances, the measured

consumption of currently installed appliances was compared

to that of the most energy efficient model of similar capacity

and function, and duration of usage [18]. Appliances were

monitored in 100 households in each of Denmark, Italy,

Portugal and Greece with 10 min intervals over 1 month.

Based on analysis for all four countries, annual savings were

calculated to vary from 1000 to 1200 kWh per year (space

heating, water heating and cooking appliances were not

included).

1.3. Aims of the project

(1) Quantify the dependence of residential electrical appli-

ance energy consumption on the outdoor atmospheric

environment in Sydney.

(2) Define the most appropriate outdoor thermal climate

index affecting energy consumption for residential space

heating and cooling.

(3) Empirically define appliance usage threshold tempera-

ture and whether they hold true to the current heating

and cooling degreeday base temperatures.

2. Methods

2.1. Study location

Sydneys climate is mild, humid subtropical. The greater

metropolitan Sydney region was split into two zones, one

coastal and another inland, each found to be climatically ho-

mogeneous [19], and well represented by one of two auto-

matic weather stations operated by the Australian Bureau of

Meteorology (Sydney and Bankstown Airports). Sixty-three

households were located within the Sydney Airport AWS

coastal climatic zone, and a further 73 within Bankstown

Airports inland zone, giving a sample of 136 households in

total for the present study.

2.2. Calculation of degreedays

Half-hourly outdoor temperature data from two automatic

weather stations within the Greater Sydney metropolitan re-gion were used to calculate degreedays for each of the

568 days of the study period. The stations were operated by

the Australian Bureau of Meteorology and were selected to

represent the distinctive coastal and inland climatic regions

within the city. A base temperature of 18 C was used in

the following project, as in some earlier Australian research

[20], although there is no universal agreement on this. A

common approach in the energy sector has been to use 18 C

for heating degreedays and 24 C for cooling degreedays

[21]. The presumption behind this practice is that there is

negligible energy consumed for domestic heating or cooling

purposes between mean daily temperatures of 18 and 24 C.

An empirical resolution of the question would be to observe

the mean daily temperature associated with minimum heat-

ing and/or cooling energy consumption, but to date there has

been no such analysis in Australia. However, results from

this research will show that both minimum heating and cool-

ing energy consumption occurs at a temperature of 18 C,

hence this seems to be the most appropriate base tempera-

ture for both heating and cooling degreedays in Sydney.

In this study degreedays were calculated from degree

half-hours in order to match the temporal resolution of the

plug-load data. Initial pilot analyses were conducted at this

30 min temporal resolution, including correlation and regres-

sion models of average half-hourly energy consumption cal-culated across all households. However, the signal-to-noise

ratio at that scale of analysis was too weak to produce any

meaningful trends. Analysis was undertaken on a daily ba-

sis (hence the need for degreeday calculations) with the

exception of a selection of individual appliance case stud-

ies with an hourly temporal resolution. Degree half-hours

were averaged to obtain a degreeday, as summarised

in Eq. (1):

degreedays =1

48

48

i=1

(degree half hours)i (1)

where (degree half-hours)i = Touti 18C, i = 1, 2, . . . ,

48, and where Touti is the average ith half-hourly outside

temperature in degrees Celsius.

Since the same base temperature of 18 C is used in

summer and winter throughout this report, a negative

degreeday in our analyses denotes a heating degreeday

and a positive degreeday, a cooling degreeday. This sign

convention centred on 18 C has the advantage of allowing

heating and cooling season results to be plotted on the same

graph.

Degreedays and degree half-hours were also calculated

using composite thermal comfort indices, namely standard


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effective temperature (SET) and effective temperature

(ET) in place of Touti in Eq. (1) above. SET and ET

are indices used widely in the heating and air-conditioning

industry to combine the effects of temperature, thermal

radiation, humidity, wind speed, clothing insulation and

metabolic rate on human thermal comfort [22]. ET primar-

ily takes into account the effects of air temperature, thermalradiation and humidity on human thermal comfort, with the

remaining variables in the comfort equation set as constants.

SET extends the index to include the effects of clothing

insulation, metabolic rate and wind speed, but with clo val-

ues and met rates set to constant values in the present study

(0.6clo and 1.2mets, respectively). Wind data collected

from the Bureau of Meteorologys Automatic Weather Sta-

tions were measured from an anemometer placed on a 10 m

mast in accordance with standard World Meteorological

Organisation guidelines. Wind speed in the boundary layer

is largely controlled by the momentum exchange (fric-

tional drag) imposed by the surface roughness, causing a

decrease in mean wind speed closer to the surface. To take

into account wind speeds that will be experienced by peo-

ple, these wind speeds were downscaled using the power

law [23].

Fig. 1. The relationship between air-conditioner average daily energy consumption in Wh per day on the y-axis and degreedays on the x-axis for (a)

the entire year, (b) the heating season and (c) the cooling season (empirically defined by the average daily temperature in relation to 18 C). Regression

models (solid curves) and 95% confidence intervals (dashed curves) were fitted with second-order polynomials. Energy consumption was averaged over

47 households in the cooling season and 41 in the heating season.

3. Results

3.1. Space heating and cooling

Thirty-two percent of NSW households currently own

air-conditioners, either reverse cycle (heat pump) or cooling

only. Most households have some type of space heating de-vice with 42% having electric room heaters (22% of house-

holds have gas heating, 15% wood and 4% oil heaters) [24].

3.1.1. Air-conditioners

Daily average energy consumption was calculated across

all houses within each of the two Sydney climatic zones, for

each of the 48 half-hourly time steps. Sample-wide averages

(across all households) for energy consumption (Wh per day)

were modelled in relation to degreedays and thermal com-

fort indices SET degreedays and ET degreedays. Fig. 1

presents the relationship between degreedays and energy

consumption over the entire study period, split by season.

The relationship is stronger in the cooling season (Fig. 1c)when 56% of the variance in day-to-day energy consumption

was explained by degreedays, compared to only 35% in the

heating season (Fig. 1b). The choice between straight-line,


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quadratic and exponential regression models was made sim-

ply on the basis of maximising the variance in energy data

being explained by degreedays (maximising R2 criterion).

The regression analysis of all-year energy consumption in

Fig. 1a produced a parabolic equation with a turning point

at 0.25 C degreedays (the point at which the derivative

of the function is zero). This point shows the degreedayvalue when neither heating nor cooling is required, or when

they are least in demand.

The SET thermal index combines the comfort effects of

air and radiant temperatures, relative humidity, wind speed,

clothing insulation and metabolic rate into a single variable.

Since clo values and metabolic rates were held constant at

0.6 and 1.2, respectively, in this application, the SET in-

dex compared to ET mainly reflects the impact of wind

speeds being included in the calculations by the underlying

two-node physiological model. In both ET and SET calcu-

lations we assumed a shade condition (i.e. ignored short- and

long-wave radiation effects) by setting mean radiant tem-

perature to equal air temperature. The amount of day-to-dayvariance in air-conditioner energy consumption explained

by SET degreedays was marginally greater than with ei-

ther simple air temperature or ET degreedays (combining

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-30 -25 -20 -15 -10 -5 0 5 10 15 20

Degree-days (C)

Proportionofapp

liancesswitchedon

95% Fiducial Limits

Observed

Model

50% threshold temperature =

-7.2C degree-days

0

0.1

0.2

0.3

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-15 -10 -5 0 5 10 15 20 25

Degree-days (C)

Proportionofappliancesswitchedon

95% Fiducial Limits

Observed

Model

50% threshold temperature =

5.5C degree-days

(b)

(a)

Fig. 2. Air-conditioner probit regression results between a continuous explanatory variable (degreedays) and a binary response variable (whether or not

the appliance was switched on at any time during the day). Analysis is performed separately for (a) heating season and (b) cooling season.

the effect of temperature and relative humidity into one vari-

able). Forty-five percent of the variance in air-conditioner

energy consumption over both seasons was explained by

SET degreedays, compared to only 41% by air temper-

ature degreedays and 39% by ET degreedays. As with

simple air temperature and ET degreedays, the cooling

season produced the stronger relationship between applianceenergy consumption and SET degreedays (R2 = 0.59)

compared to 0.35 for the heating season.

Probit analysis is a statistical technique that models the

percentage of a sample responding to various levels of

exposure to an environmental agent [25]. In the context

of the present project the technique was used to fit sig-

moidal response functions between the daily temperature

or degreeday (stimulus) and the percentage of sample

households with their air-conditioners (either in heating or

cooling mode) switched on. The decision to turn a heating

or cooling system on or off is based on thermal discomfort;

probit analysis has been widely used in thermal comfort

research [26]. During the heating season probit regressionbetween degreedays and the probability of appliances be-

ing switched on (Fig. 2a) produced a small chi-square test

statistic for goodness of fit and therefore a large P-value


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(2 = 15.5, d.f. = 20, P = 0.8), indicating a good fit

by the probit model.1 The probit models 50% threshold

temperature was 7.2 C degreedays (i.e. a mean daily

temperature of 10.8 C), with narrow fiducial limits from

7.6 to 6.8 C degreedays (fiducial limits are to probit

models what confidence limits are to regular regression

models). This 50% threshold average daily temperature (ordegreeday) is the point at which 50% of occupants had

their appliance switched on at some stage during the day

and 50% had it switched off all day. This is also the point at

which the greatest number of households changed their de-

cision from off to on. On the coldest of Sydney days in

the study period only 65% of households were observed to

have their air-conditioners switched on for heating (Fig. 2a).

Fig. 2b shows the results of cooling season analysis in

which the probit model produced a large chi-square test

statistic and a low P-value (2 = 34.2, d.f. = 20, P = 0),

indicating there was a relatively weak match between the

probit model and the data, but this was compensated for

by widening the fiducial limits. The 50% threshold temper-

ature for cooling season analysis was 5.5 C degreedays

with 95% fiducial limits of 5.1 and 6.0 C degreedays. It

should be noted that the implementation of the probit tech-

nique that we used [24] SAS was able to compensate for

failed goodness of fit tests by widening the fiducial limits

surrounding its estimate of the 50% threshold temperature.

On the evidence presented in Fig. 2, the warmest days

in the study period had up to 90% of households using

their air-conditioner to cool. On a zero degreeday (i.e.

mean daily temperature of 18 C) 20% of households still

had their air-conditioners switched on, and the same of

minimum-usage level was found in the heating season anal-yses as well. That is, a minimum of about 20% of house-

holds used their air-conditioners either in heating or cooling

mode, all-year-round.

On extending the weather sensitivity analysis by using

the ET and SET thermal comfort indices instead of air

temperature during the cooling season, the probit regression

model of air-conditioner usage and SET degreedays pro-

duced the best results in terms of explained variance and

maximised the goodness of fit test (2 = 7.5, d.f. = 16, P

= 0.9). The 50% threshold temperature came in at 3.3 C

SET degreedays with tightly defined 95% fiducial limits

at 3.03.6

C SET

degreedays.Load profiles were calculated for the air-conditioners in

the Sydney sample in order to more closely examine the

diurnal variability of energy consumption. Mean hourly en-

ergy consumption and concurrent outdoor air temperature

observations were averaged across all households for each

hour of the day. Due to the two modes of reverse cycle

air-conditioners (heating and cooling), separate load profiles

1 In the context of this particular statistical method, failing the 2

goodness of fit test at the 0.05 level indicates that the data were well

approximated by the probit model.

were produced for heating and cooling seasons, i.e. averag-

ing across all days in each of the two seasons.

Fig. 3 shows the diurnal distribution (time uncorrected for

daylight savings) of air-conditioner energy consumption and

corresponding outdoor air temperature for the heating and

cooling seasons (a) and (b), respectively. The salient fea-

ture of the seasonal comparison in Fig. 3 is the mean dailyenergy peak in winter is twice that for summer. During the

cooling season (Fig. 3b) energy consumption begins to rise

at 9 a.m., peaking at 4 p.m. and then rapidly decreasing to a

minimum at 6 a.m., closely tracking the diurnal outdoor tem-

perature cycle. Assuming the causal link between summer

temperature and energy consumption extended to the heat-

ing season one might expect the winter diurnal load profile

to be a mirror image of the summers, but that appears in

Fig. 3a to not be the case. Winter heating energy consump-

tion has two peaks: one at 8 a.m. and the other at 9 p.m.,

the periods at which houses are most likely to be occupied.

During the heating season the daily minimum temperature

occurs between 6 a.m. and 7 a.m., but this coincides with thetime of minimum, not maximum heating energy consump-

tion. Heating season load profiles for room heaters produced

a similar twin-peak pattern to that of air-conditioners during

the heating season.

The previous analyses involved aggregation and averag-

ing across a sample of 47 air-conditioners in the cooling

season and 41 during the heating season, thereby masking

the intricacies of individual householder behaviour. In order

to examine just what was lost in the averaging process we

performed a case study on an individual household. Hourly

probit analysis of this single reverse cycle air-conditioner

indicates that usage during the cooling season conformed tothe probit model quite closely (Fig. 4b), although the good-

ness of fit test on the probit model suggests otherwise (2 =143.9, d.f. = 19. P = 0). The 50% threshold temperature

was 27.1 C, and at this daily mean temperature, the house-

hold in question had a 50:50 chance of having the cooler on

at some stage during the day. In the heating season (Fig. 4a)

the probability of the appliance being switched on increased

with decreasing outdoor temperature, reaching a peak at 6 C

but then decreasing with further reductions in outdoor air

temperature. The chi-square statistic for the goodness of fit

test is highly significant in the heating season (2 = 239.3,

d.f. = 17, P = 0), confirming what is apparent in Fig. 4a,

namely that the probit model is inappropriate for this set of

data. A similar analysis using room heater data produced

similar patterns to these air-conditioner results.

3.1.2. Room heaters

For the purposes of this study room heaters are defined

as portable electrical heating devices, usually used to heat

only one room of a house at a time. Common examples in

Sydney are the oil column radiator, the bar radiator and the

fan-forced convective heater. Degreedays defined in terms

of simple air temperature predicted room heater energy con-

sumption quite well (Fig. 5), producing an R2 of 0.63. Room


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erature(C)

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erature(C)

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(a)

(b)

Fig. 3. The diurnal distribution of mean hourly air-conditioner energy consumption and mean hourly outdoor temperature for (a) the heating season and

(b) the cooling season. Energy consumption is averaged across 47 appliances for the cooling season and 41 appliances for the heating season.

heater energy consumption increases gradually with a de-

crease in degreedays below a value of+2.8 C degreedays

(i.e. mean daily temperature of 20.8

C).Room heater probit analysis produced a significant dif-

ference between observed data and the probit model (2 =

35.9, d.f. = 26, P= 0) indicating a sub-standard goodness

of fit between data and model. The 50% threshold tempera-

ture for room heaters was 7 C degreedays, with widened

95% fiducial limits (8.1, 6.3 C degreedays) to com-

pensate for the poor goodness of fit. On the coldest of days

observed during the entire study, barely two thirds (63%)

of households had their electrical room heaters switched on

(Fig. 6).

3.2. Refrigerators/freezers

Refrigerator and freezer penetration rates in NSW cur-

rently stand at 1.3 and 0.4 appliances per household, respec-

tively [24]. Refrigerator energy consumption has a positive,

linear dependence on degreedays (Fig. 7a). There appears

to be two distinct sets of data in Fig. 7a; a cloud of data

points appearing just above the fitted regression line and an-

other just below it. There also appears to be a slight change

in gradient moving from heating season (ve degreedays)

to cooling season (+ve degreedays).

According to the coefficients of determination (R2) in

Fig. 7a, outdoor temperature bears a closer relationship to

refrigerator energy consumption during summer than in win-

ter (R2 = 0.31 and 0.06, respectively). An increase of one

degreeday in summer was associated with energy consump-tion increase of 95 Wh per day, whereas the same increase

in winter increased energy consumption by barely half that

amount (42 Wh per day on average).

Freezer daily energy consumption also increases linearly

with an increase in degreedays (Fig. 7b). The relation-

ship between energy consumption and degreedays is quite

strong with two thirds (67%) of the day-to-day variance in

energy consumption being explained by the degreeday in-

dex. This relationship is stronger than that for refrigerators

(R2 = 0.42). Fig. 7b depicts the relationship, split by season.

As with refrigerators the relationship is stronger in summer

(R2 = 0.51) than in winter (R2 = 0.36). In summer an in-

crease of one degreeday was associated with an increase

energy consumption of 75 Wh per day whereas in winter

energy consumption only increased by 53 Wh per day per

heating degreeday; in effect about 30% less weather sen-

sitive than in summer.

The single house case study in Fig. 8 explores the relation-

ship between degreedays and refrigerator energy consump-

tion, and includes the effects of space heating and cooling in

the household. Energy consumption data for the households

reverse cycle air-conditioner were used to define the outdoor

temperatures at which the occupants of this particular house

began to heat or cool. Refrigerator energy consumption was


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-20 -15 -10 -5 0 5 10 15 20

Temperature (C)

Probabilityofappliancebeingswitchedon

Model

95% Fiducial Limits

Observed

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10 15 20 25 30 35 40

Temperature (C)

Probabilityofappliancebeingswitch

edon

Model

95% Fiducial Limits

Observed

(a)

(b)

Fig. 4. Single-household case study of a reverse cycle air-conditioner. Hourly probit analysis examines the relationship between mean hourly temperature

and the fraction of occurrences, for each temperature bin, of the appliance being switched on at anytime during each hour of analysis, during (a) the

heating season and (b) the cooling season.

y = 28.275x2 - 349.28x + 809.36

R2 = 0.63

0

2000

4000

6000

8000

10000

12000

-12 -10 -8 -6 -4 -2 0 2 4

Degree-Days (C)

EnergyConsumption(Wh

/day)

Fig. 5. Average daily room heater energy consumption in Wh per day as functions of degreedays. Regression models (solid curve) and 95% confidence

intervals (dashed curve) were fitted with second-order polynomials. Seventy-one room heaters (i.e. households) were included in the analysis.


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0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-30 -25 -20 -15 -10 -5 0 5 10 15 20

Degree-days (C)

Proportionofapplia

ncesswitchedon Model

95% Fiducial Limits

Observed

50% threshold temperature

= 7 C degree-days

Fig. 6. Room heater probit regression results between a continuous explanatory variable (degreedays) and binary response variable (whether or not the

appliance was switched on at any time during the day).

split into three sections depending on whether the house-

hold was being artificially heated, cooled, or free-running.

On days below 2 C degreedays this particular household

tended to heat, while on days above 4 C degreedays the

household tended to cool. On days between the threshold

temperatures of2 and 4 C degreedays the house was as-

sumed to be free-running, neither heated nor cooled.

When the household was free-running, that is the build-

ing is not using energy for either heating, nor cooling [27],

the relationship between refrigerator energy consumption

Heating Seasony = 42.259x + 2800.8

R2 = 0.06

Cooling Season

y = 95.443x + 2982.2

R2 = 0.31

0

1000

2000

3000

4000

5000

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12

Degree-Days (C)

EnergyConsumption(Wh/da

y)

Heating Season

y = 52.547x + 1807.1

R2 = 0.36

Cooling Season

y = 75.254x + 1776.3

R2 = 0.51

0

1000

2000

3000

4000

5000

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12

Degree-Days (C)

EnergyConsumption(Wh/

day)

(a)

(b)

Fig. 7. The relationship between daily energy consumption and degreedays, split by season (dashed model fitted to the heating season; solid model fitted

to the cooling season) for (a) refrigerators and (b) freezers. Ninety-three refrigerators and 39 freezers (i.e. households) were included in the analysis.

and outdoor temperature was strongest (R2 = 0.42) and the

gradient (weather sensitivity) was greatest (Fig. 8). These

points show a change in weather sensitivity of refrigerator

energy consumption, which tends to plateau once the house

is artificially heated or cooled (Fig. 8).

3.3. Domestic hot-water systems

Domestic hot-water analysis was performed separately for

the different energy rates available to consumers (anytime


10/14


0

200

400

600

800

1000

1200

1400

1600

1800

-10 -8 -6 -4 -2 0 2 4 6 8 10

Degree-Days (C)

EnergyCon

sumption(Wh/day)

Cooling R2=0.23

Neither R2=0.42

Heating R2=0.26

4C Degree Day

Point at w hich

house begins to

occasionally cool

-2C Degree-day

Point at which

household tends

to heat

Fig. 8. A single house case study of refrigerator energy consumption from a household located in coastal Sydney. Refrigerator energy consumption was

split into three sections depending on whether the household was being artificially heated, cooled, or free-running.

Heating Season

y = -139.39x + 8806.4

R2= 0.05Cool ing Season

y = -326.59x + 8254.7

R2 = 0.30

0

5000

10000

15000

20000

25000

30000

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12

Degree-days (C)

EnergyConsumption(Wh/day)

Heating Season

Cool ing Season

Heat ing Seas on

y = -261.25x + 12792

R2 = 0.05 Cool ing Season

y = -331.05x + 10838

R2 = 0.17

0

5000

10000

15000

20000

25000

30000

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12

Degree-days (C)


Heat ing Seas on

Cool ing Season

Heating Seas on

y = -182.85x + 9646.4

R2 = 0.10Cool ing Season

y = -293.96x + 8955.8

R2= 0.31

0

5000

10000

15000

20000

25000

30000

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12

Degree-Days (C)


Heating Seas on

Cooling Season

(A)

(B)

(C)

Fig. 9. The relationship between hot-water energy consumption and degreedays, split by season, for (A) anytime, (B) off-peak and (C) night-rate systems.


11/14


heating, off-peak heating and night-rate heating). Anytime

hot water refers to systems that heat water whenever re-

quired, regardless of tariff. Off-peak systems switch on the

water heater between 10 p.m. and 8 a.m. and 11 a.m. and

4 p.m. Night-rate hot water is switched on between 12 a.m.

and 7 a.m. As expected, for all three types of electric

hot-water system the relationship with outdoor temperaturewas negatively linear, with an increase in degreeday (air

temperature) causing a decrease in energy consumption

(Fig. 9). The energy consumption of night-rate hot-water

systems was affected by outdoor weather most directly

(R2 = 0.48) (Fig. 9C), off-peak and anytime hot-water

systems showing diminished weather sensitivity with R2 =0.37 and 0.38, respectively (Fig. 9A and B). Energy con-

sumption of all three types of domestic hot-water systems

was affected more by outdoor weather during summer than

in winter.

4. Discussion

4.1. An appropriate degreeday base temperature for

Sydney

The severity of climate can be characterised concisely

in terms of degreedays. The degreeday base temperature

is generally regarded as the outdoor temperature at which

neither artificial heating nor cooling is required. Heating

degreedays, or degreehours, calculated with respect to a

base temperature of 18 C are widely used in Australia [20].

For cooling degreedays however, the base temperature is

not so unanimously agreed. During the heating season thetotal heat loss coefficient of a building does not change as

windows are closed and air exchange rate is fairly constant.

However, during the cooling season heat gains can be par-

tially regulated and the onset of artificial cooling can be

postponed by increasing ventilation rates, e.g., opening win-

dows and doors to the exterior [28]. Often different base

temperatures are used for cooling degreedays, depending

on the building type and ventilation rate. This base tempera-

ture represents the transition point between comfort achieved

purely through natural ventilation and comfort achieved by

compressor-based cooling. Presumably, houses with poor or

limited cross-ventilation potential make the transition from

ventilation to refrigerated cooling at lower outdoor tem-

peratures than houses with good climatic design. Unfortu-

nately the design features and ventilation capabilities were

not recorded for the houses in the present study, so a constant

cooling degreeday base temperature of 18 C was applied

across the entire sample.

The results of reverse-cycle air-conditioner energy

consumption versus degreedays in Fig. 1 indicate the

parabolic minimum occurred at 0.25 C degreedays,

so a degreeday base temperature of 18 C seems to be

confirmed by these data from the Sydney context. The rela-

tionships for both seasons demonstrated that, at an average

temperature above 18 C in Sydney, householders start us-

ing their coolers more intensively, and with an average daily

temperature below 18 C, householders start making more

use of their air-conditioners in heating mode. The non-zero

energy consumption minimum in Fig. 1a indicates that

some households in our sample were cooling their houses

on days cooler than a mean of 17.75

C and heating theirhouses on days warmer than that base temperature.

4.2. Space heating and cooling

Probit analysis of the likelihood that air-conditioners

were switched on at anytime during the day, as a function of

the degreeday index revealed that, during the heating sea-

son on the coldest of Sydney days, only 65% of households

were switching on their air-conditioners in heating mode.

However, during the cooling season, on the warmest of Syd-

ney days, about 90% of households switched on their air-

conditioners. Those houses not using their air-conditioners

during extreme days may not have been occupied. Unfor-tunately occupancy could not readily be diagnosed from

the raw data collected in the RES. The database contained

appliance energy audit data but nothing on occupant be-

haviour or demographics. The relatively large proportion of

households choosing not to use their air-conditioners to heat

on Sydneys coldest days may also indicate that they were

using alternative forms of heating (i.e. not reverse cycle air-

conditioning). Many Sydney households have several space

heating devices of various energy types and use them in-

terchangeably and/or simultaneously, making it difficult to

automatically monitor total heating demand [20]. For exam-

ple, 55% of households monitored in the Residential EnergyStudy had at least one gas appliance, which could quite

possibly include a portable, unflued natural gas heater of the

type that is still quite popular in New South Wales despite

the indoor air quality problems posed by their combustion

products.

The relationship between outdoor weather and air-

conditioner energy consumption was consistently stronger

in summer than in winter (R2 = 0.56 for summer, compared

to 0.35 for winter). Reverse cycle air-conditioner load pro-

files indicate that peak energy consumption occurs in the late

afternoon during summer and in the evening during winter.

Outdoor temperature minima occur in the early morning

and maxima in the mid afternoon. Consequently, during the

heating season at the coldest time of the day, occupants are

sleeping and, as a result, energy consumption is minimal,

the mild climate experienced in Sydney allows for these

behaviours, where heating appliances are switched on only

when occupant are home and awake. The lack of heating

during this coldest time of the day undoubtedly weakened

the statistical relationship between daily energy consump-

tion and heating degreedays, reinforcing the importance of

time-of-day in the prediction of space heating energy con-

sumption. This point was confirmed by the single appliance

case study. Probit analysis of the proportion of time the


12/14


appliance was switched on peaked at an outdoor tempera-

ture of 7 C, but the proportion of hours the appliance was

switched on decreased sharply as temperature decreased

below 7 C, presumably because the householders were

asleep. Further reinforcing this interpretation is the obser-

vation that, during the cooling season (summer) when air-

conditioner load peaks in the afternoon (coinciding with thewarmest time of the day), house occupants were more likely

to be awake and therefore more likely to respond to the heat

by turning on their air-conditioners, thus explaining why the

statistical relationship between daily energy consumption

and degreedays was stronger in summer than in winter.

One of the key outputs from the probit regression tool is a

50% threshold temperature which can be interpreted as the

temperature at which the largest proportion of households

change their appliance from off to on. These results,

along with load profiles, and the direct relationships pro-

duced between energy consumption and outdoor weather

may potentially be useful for power utility operations in

predicting system spikes and peaks in the Sydney market.At the time of writing a commercially available service,

called eWeather Online [29] provides eight-day weather

(from CSIROs Division of Atmospheric Research) and

electricity demand forecasts (from the National Electricity

Market Management Companys (NEMMCO), Short-Term

Projected Assessment of System Adequacy (STPASA) for

wholesale electricity sellers and buyers. The information

is used to forecast possible spikes in electricity demand,

across all sectors [30]. The present paper presents a detailed

analysis of the weather sensitivity of electricity end-use in

the residential sector. Since the residential sector in general

and space heating and cooling appliances in particular havea large impact on electricity peak loads [2], the weather

sensitivity results reported here, along with appliance pene-

tration rates, are potentially useful in relating systems peaks

to individual end-uses in the residential sector.

4.3. Refrigerators/freezers

Outdoor temperature has a stronger influence on refriger-

ator energy consumption in summer than winter (R2 = 0.31

for summer, compared to only 0.06 for winter). One possi-

ble explanation may be the fact that the householders were

heating the rooms in which the refrigerators were located

during winter months. Modifying the interior temperature

breaks, or at least weakens the relationship between out-

door temperature and refrigerator energy consumption, as

demonstrated in the single-household case study in Fig. 8.

At the time of the Residential Energy Study (1993/1994)

Sydneys peak in electricity consumption typically occurred

in winter [1], suggesting that households were choosing to

artificially heat more readily than cool, further explaining

refrigerator/freezer energy consumptions relative indepen-

dence from outdoor temperature.

Current refrigerator test procedures for use in Mini-

mum Energy Performance Standards (MEPS) and energy

efficiency labelling schemes are performed using a single

reference ambient temperature inside a climate chamber.

This ambient temperature differs world wide, from 30 C

in Korea, Japan and Chinese Taipei, to 32 C in USA,

Australia and New Zealand. The International Organisa-

tion for Standardisation (ISO) specifies 25 C for temperate

climates and 32

C for tropical climates [31]. In realityrefrigerators operate under a range of ambient tempera-

tures, and energy consumption and performance will vary

under these non-steady-state conditions, as demonstrated

in the results of this study. The aim of energy labelling

is to encourage customers to purchase the appliance that

uses least energy during actual use. A test procedure un-

dertaken at a single temperature will not demonstrate the

appliances performance under realistic ambient conditions.

For example, the French Domestic Measurement End-use

Campaign [16] discovered that in situ energy consump-

tion in refrigerators is lower than the energy consumption

measured under laboratory test conditions. The linear rela-

tionship between an average of ninety-three refrigeratorsdaily energy consumption and degreedays in the present

study could potentially inform the design of a more realistic

and representative refrigerator test protocol for sub-tropical

Australian conditions.

Daily freezer energy consumption also increases linearly

with an increase in degreedays. The relationship be-

tween energy consumption and degreedays is moderately

strong with two thirds of the day-to-day variation in energy

consumption being explained by concurrent degreedays.

This relationship between freezer energy consumption and

degreedays is stronger than that for refrigerators (R2 =

0.42 for refrigerators, 0.67 for freezers), possibly due tofreezers not being accessed by the householders as often as

refrigerators, resulting in fewer door openings (heat gains)

and changes to food load.

4.4. Domestic hot water

Domestic hot-water energy consumption showed a

stronger relationship to outdoor temperature in summer

than in winter (for anytime hot-water systems, R2 = 0.30

in summer and 0.05 in winter). Depending on the mode of

the hot-water system between 30 and 48% of the day-to-day

variance in energy consumption was explained by changes

in outdoor weather. In the case of outdoor hot-water storage

systems, an increase in outdoor temperature will decrease

the amount of heat lost to the ambient environment. Some

of the unexplained variance in hot-water energy consump-

tion in this study could be due to the variety of installation

locationsespecially indoors versus outdoors. Unfortu-

nately, this hypothesis could not be investigated any further

because the necessary information on installation location

was not recorded in the original RES database [1]. Other

factors, apart from weather, affecting hot-water energy con-

sumption include the nature of other hot-water consuming

devices such as dishwashers and washing machines, the


13/14


nature (insulation status) of the distribution pipes, and occu-

pant behaviour. The weather sensitivity of hot-water energy

consumption may further be increased in summer due to

occupants thermal comfort preferences. For example an

increase in outdoor temperature may prompt occupants to

prefer cooler showers that require less hot water than is the

case during cooler weather.

5. Conclusions

Statistical models were established between outdoor

weather and energy consumption for the following appli-

ances: room air-conditioners, room heaters, refrigerators,

stand-alone freezers and domestic hot-water systems. All

appliances exhibited some form of weather sensitivity. The

relationship between outdoor weather and individual ap-

pliance energy consumption was consistently found to be

stronger in the cooling season than the heating season for

all appliances included in the analysis. The thermal comfortindex SET was found to be the most useful of predictor of

space-cooling energy consumption, indicating that outdoor

wind speed and relative humidity, as well as air temperature

affect occupants thermal comfort during summer which, in

turn, determines space-cooling demand. These relationships

provide actual in-use energy consumption rather than over-

simplified laboratory consumption, and this in turn may be

used to design more realistic and representative appliance

test protocols (often used in the implementation of MEPS).

Probit regression was found to be a useful statistical

technique in predicting the degreeday values at which

households tend to heat and cool. Probit models of spaceheating and cooling appliance usage patterns can predict the

probability of the appliances being switched on under vari-

ous outdoor weather conditions. These relationships, along

with load profiles and the direct relationships between en-

ergy consumption and outdoor weather, have the potential

to assist in the prediction of system spikes and peaks.

By examining the mean daily temperature associated with

minimum heating and cooling energy consumption for Syd-

ney, a degreeday base temperature of 18 C was found to

be the appropriate base temperature for the calculation of

both heating and cooling degreedays. This report is the first

empirical conformation that 18 C is the most appropriate

degreeday base temperature for Sydney, all-year-round.

The inclusion of the following parameters in the original

RES would have been useful in giving a more comprehen-

sive picture of electricity consumption in Sydney: informa-

tion about the type, location and age of the appliance being

monitored, details of the building envelope, e.g., thermal

performance, solar aspect, proportion of building shaded by

vegetation, and socio-demographic details of occupants.

Suggestions for future research arising from the present

project include a more thorough application of the ET

and SET indices of thermal climate to modelling outdoor

thermal conditions. In particular, instead of assuming mean

radiant temperature equals air temperature (i.e. shade condi-

tion), it is feasible to explicitly calculate the impacts of short-

and long-wave radiation on the human heat balance and to

equate these impacts to the temperature of an isothermal en-

closure that would be required to exert the same net radiation

impact [32], i.e. mean radiant temperature. This avenue of

further research would enable a quantitative assessment ofthe impacts of solar radiation variations on energy end-use.

Acknowledgements

The authors would like to thank Tony Marker and Shane

Holt at the Australian Greenhouse Office for presenting us

with this research problem and facilitating our access to the

NSW residential energy survey dataset. Lloyd Harrington

of Energy Efficient Strategies is thanked for his critiques on

the various drafts throughout this project. Observed weather

data used in this project were purchased from the Australian

Bureau of Meteorology.

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