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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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164 M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174
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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M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174 165
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
0.4
0.5
0.6
0.7
0.8
0.9
1
-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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166 M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174
(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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M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174 167
0
100
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1300
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Time
EnergyC
onsumption(W)
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erature(C)
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T emperature (C)
0
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1 0 0 0
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1 2 0 0
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0 :0 0 2 :0 0 4 :0 0 6 :0 0 8 :0 0 1 0: 00 1 2: 00 1 4:0 0 1 6 :0 0 1 8 :0 0 2 0: 00 2 2: 00
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nsumption(W)
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erature(C)
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Temperature ( C)
(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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168 M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174
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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M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174 169
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
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170 M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174
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)
EnergyConsumption(Wh/day)
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)
EnergyConsumption(Wh/day)
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.
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M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174 171
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
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172 M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174
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
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M. Hart, R. de Dear / Energy and Buildings 36 (2004) 161174 173
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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