Climatic and economic influences on residential electricity consumption

Pergamon

Energy Convers. Mgmt Vol. 39, No. 7, pp. 623-629, 1998 © 1998 Elsevier Science Ltd. All rights reserved P I h S0196-8904(97)10008-5 Printed in Great Britain 0196-8904/98 $19.00 + 0.00

CLIMATIC AND ECONOMIC INFLUENCES ON RESIDENTIAL ELECTRICITY CONSUMPTION J O S E P H C. LAM Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong (Received 24 September 1996) Abstract--We have performed regression and correlation analyses to investigate the relationships between residential electricity consumption and economic variables and climatic factors for Hong Kong. Economic and energy data for the 23-year period from 1971 to 1993 have been gathered and analysed. It has been found that both the seasonal and the yearly electricity use in the residential sector can be estimated based on the household income, household size, electricity price and cooling degreedays. © 1998 Elsevier Science Ltd. Electricity

Climate

Economics

Regression analysis

Residential sector

Hong Kong.

INTRODUCTION In Hong Kong, concern with the environment and the world ecosystem has recently been a major public issue, and there has been a growing concern about energy consumption and its implications for the environment. With rapid economic growth and improvement in living standard, there has been a substantial increase in energy consumption during the eighties. Hong Kong relies solely on the import of coal and oil products to meet its energy needs. Lam and Ng [1] reported that, during the 13-year period (1979-1991), the total primary energy requirement rose from 195.4 PJ (1 PJ = 1015 Joule) to 468 PJ, an increase of 140%. During the same period, the gross domestic product grew by 121% from HK$123.6 to HK$273.5 billion. In the residential sector, gas (town gas and LPG) and electricity are the two major forms of energy consumed in buildings. In subtropical Hong Kong, where winters are short and mild, heating is seldom required. Even on a few cold winter days, when heating is needed, the local population tends to use electric heaters. Gas is, therefore, mainly used for cooking and hot water. In terms of final energy consumption, the ratio of electricity to gas consumption is about 2.5:1 in 1993. In terms of primary energy, the ratio is much higher. Figure 1 shows the residential sector electricity consumption for the 23-year period from 1971 to 1993. Consumption rose from 1059 GWh in 1971 to 6692 GWh in 1993, an increase of 532%. It can be seen that there are two parts in the electricity consumption variation--seasonal and yearly. The former is mainly influenced by the prevailing climatic conditions and the latter by economic, social and demographic factors. Residential energy demand, particularly electricity, has been studied by a number of researchers [2-5]. These studies, however, have been conducted mainly for the U.S. and other developed and industrialised countries. Very little work on energy regression analysis for Hong Kong can be found in the international literature. This paper presents work on the climatic and economic influences on electricity consumption in the residential sector. It is hoped that the information presented in this paper will be of interest to members of the international energy community. CLIMATIC INFLUENCES AND SEASONAL VARIATIONS A significant proportion of the electricity consumption is for air-conditioning during the long, hot and humid summer in Hong Kong. Figure 1 shows that the peak summer months consume about twice as much electricity as the winter months. Electricity demand increases in May, peaks in July/August and then falls in October, mainly due to cooling for the thermal comfort 623

624

LAM:

RESIDENTIAL ELECTRICITY CONSUMPTION

1,000

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cO o.

a00

.m

E

6oo

.o t..

400

:3 ¢,.. 0 (3 >,,

o o >, c o

200

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93

Year Fig. l. Monthly electricity consumption in the residential sector (1971-1993).

requirement. A recent survey has found that occupants do operate their room air-conditioners during the six-month period from May to October, which is considered the cooling season for residential buildings [6]. Degree-day statistics and techniques involving key weather parameters have proved useful in the assessment of heating and cooling requirements and the modelling of the impact of weather on national electricity demand [7, 8]. These techniques were used in the present study to isolate weather-induced changes in electricity use in the residential sector. Previous work on typical weather data for building energy studies has identified three major climatic variables, namely temperature, moisture content and solar radiation, as the key factors affecting the thermal performance of buildings and, hence, the cooling requirement [9]. Thus, the degree-day climate variables selected are cooling degree-days (CDD), latent enthalpy-days (LED) and cooling radiation-days (CRD). CDD is a measure of the severity of summer conditions in terms of the outdoor dry-bulb air temperature, an indication of the sensible cooling requirement for a particular location. LED indicates the amount of energy required to remove excessive moisture from the outdoor air and lower the humidity to an acceptable indoor level. CRD is a measure of the building cooling load due to unwanted solar radiation during summer months [10]. The multiple regression technique was used to correlate the monthly electricity consumption in the residential sector with CDD, LED and CRD for each year from 1979 to 1993 (solar radiation data prior to 1979 are incomplete) as follows: E = a + bl x CDD + b2 x LED + b3 x CRD

(1)

where E is the monthly electricity consumption per household (GWh), CDD is the monthly cooling degree-day total (°C), LED is the monthly latent enthalpy-day total (kJ/kg), and CRD is the monthly cooling radiation-day total (M j/m2). Table 1 shows the regression statistics. The coefficient of determination R 2 ranges from 0.81 to 0.95, indicating that 81-95% of the seasonal variation in residential electricity consumption can be explained by the variations in CDD, LED and CRD. From the F-test, F values range from 11.0 to 52.1. The critical value of the F-distribution for a significant level of ~ = 0.01 is

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RESIDENTIAL ELECTRICITY CONSUMPTION

625

Table 1. Multiple regression statistics for monthly electricity consumption and climatic variables Regression coefficient Year

R2

a

bl

b2

1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

0.86 0.83 0.88 0.86 0.90 0.87 0.81 0.92 0.88 0.86 0.91 0.88 0.82 0.95 0.90

131 133 120 126 144 149 143 168 170 170 197 221 208 264 230

0.557 0.710 0.801 0.812 1.194 0.745 0.571 1.333 1.226 0.995 1.547 1.992 2.026 1.655 2.247

-0.007 -0.039 0.047 0.014 -0.002 -0.044 0.089 -0.167 -0.087 -0.065 -0.221 -0.213 -0.108 -0.471 -0.225

T b3

0.005 0.026 -0.121 -0.062 -0.159 0.039 -0.109 0.112 0.080 0.155 0.294 0.199 0.065 0.716 0.187

F

CDD

LED

16.1 12.8 20.0 16.8 24.2 18.3 11.0 30.0 19.6 16.8 28.3 19.2 12.0 52.1 24.6

2.07 1.67 3.20 2.50 2.58 1.89 1.16 4.35 2.14 1.41 3.06 3.18 1.65 3.78 3.03

-0.17 -0.56 0.65 -0.19 -0.03 -0.86 1.05 -2.32 -0.89 -0.45 -1.78 -1.23 -0.40 -4.16 -1.41

CRD 0.12 0.17 -1.06 -0.60 -0.77 0.38 -1.02 1.26 0.35 0.81 1.44 1.07 0.22 3.66 1.11

7.59, which is very much smaller than the calculated F values [11]. In other words, less than 1% of the time, by chance, is the null hypothesis of no linear regression between E and the three climate variables true. Hence, it is reasonable to assume that the regression equation is useful in describing the monthly residential electricity consumption as a function of these climate variables. The T-test results on CDD, LED and CRD are also shown in Table 1. The critical value for the one-sided test on bl bE and b3 (using a significant level of ~t = 0.05) is 1.86. For CDD, the T value is greater than 1.86 for most years. Hence, the null hypothesis that bl = 0 can be rejected at the 0.05 significance level. In other words, only 5% or less of the time does the CDD not have an important influence on the dependent variable E. For LED and CRD, the T values range from -4.16 to 1.05 and -1.06 to 3.66, respectively. The null hypothesis of bE=0 and b3 = 0 cannot be rejected at 0.05 or even higher significance level for most years in the analysis. For some of the years, the regression coefficients b 2 and b3 for LED and CRD have negative signs. This is contrary to the expectation that electricity consumption would increase with the moisture level and the amount of solar radiation. Similar characteristics have been observed using multiple log-linear regression with In E as the dependent variable. Both the linear and loglinear regression results indicate that CDD is the dominant variable, and the exclusion of LED and CRD from equation (1) would not have a significant effect on the regression correlation. For simplicity, a single linear regression between E and CDD is, therefore, proposed as follows: E = a + b x CDD

(2)

Table 2 shows the single linear regression statistics for the 23-year period. Both the F-test and T-test indicate that CDD has an important influence on E. The coefficient of determination R2 ranges from 0.74 to 0.93, indicating that 74-93% of the seasonal variation in the residential electricity consumption can be explained by CDD. The coefficient a can be regarded as the electricity consumption per household by lighting and other household electrical appliances except air-conditioners (i.e. the base load), and b is a measure of the weather-sensitive consumption for air-conditioning for every degree of CDD. Base consumption rose from 87 kWh in 1971 to 200 k w h in 1993, an increase of 130%. This growth is mainly due to the increase in the diffusion rate of household electrical appliances in the residential sector. The weather-induced consumption rose from 0.272 kWh/°C to 1.747 kWh/°C, an increase of 542%. This change is largely a result of increased installation of air-conditioners. The coefficient b may be regarded as an indication of the diffusion rate of air-conditioners in the residential sector. The fastest growth in ownership level occurred between 1986 and 1992, the period with the highest economic growth and pay rise. The levelling in 1993 indicates a slowing in the acquisition rate, and the air-conditioner ownership level may be approaching saturation. Similar findings have been observed in the survey of electricity end-use in households in 1993 [12].

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LAM: RESIDENTIAL ELECTRICITYCONSUMPTION Table 2. Single regressionstatistics for monthly electricityconsumption and CDD

Year

R2

1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

0.74 0.93 0.88 0.80 0.81 0.80 0.81 0.82 0.86 0.82 0.86 0.86 0.88 0.86 0.77 0.86 0.87 0.85 0.88 0.85 0.81 0.84 0.87

Regression coefficient a b 87 94 99 104 105 I11 119 127 130 127 122 126 138 143 151 149 154 167 177 192 193 213 200

0.272 0.353 0.445 0.399 0.381 0.452 0.459 0.575 0.541 0.608 0.633 0.671 0.675 0.671 0.664 0.877 1.081 1.204 1.420 1.630 1.702 1.746 1.747

F

T

29.1 59.3 71.9 39.3 42.4 40.4 43.0 44.2 60.0 45.6 62.4 59.2 75.3 61.8 33.1 61.8 64.5 57.6 72.8 58.1 44.0 53.2 69.1

5.3 7.7 8.5 6.3 6.5 6.4 6.6 6.7 7.7 6.8 7.9 7.7 8.7 7.9 5.8 7.9 8.0 7.6 8.5 7.6 6.6 7.3 8.3

ECONOMIC INFLUENCES AND YEARLY VARIATIONS Besides climatic influences, residential electricity consumption is affected by the social, economic and demographic conditions. The average monthly income per household rose from HK$1745 in 1971 to I-IK$16,351 in 1993, an increase of 837%. During the same period, the number of households increased from 0.857 to 1.683 million, an increase of 96.4%; while the population grew by 46.3% from 4.045 to 5.919 million. This indicates a decreased household size from 4.7 to 3.5 persons per household. To investigate how these factors will correlate with the electricity consumptions in the residential sector, multiple log-linear regression analysis was performed for the 23-year period from 1971 to 1993. The independent variables are the average monthly household income (/), household size (S), electricity price (P) and CDD. The resulting regression equation is as follows: l n E = - 0 . 1 4 2 + 0 . 4 6 7 × l n i + 0 . 5 9 × 1 n S - 0 . 1 5 5 × l n P + 0.004 × C D D

(3)

G o o d correlation is indicated by an R 2 of 0.9 and a large F value of 629 (the critical value of the F-distribution for ~t = 0.01 is 5). The t-statistics indicate that I and C D D have greater influences on the monthly electricity use than S and P. The t values for the monthly income and the C D D are 5.2 and 32.6, respectively, compared with the critical t value of 2.6 at a significance level of ~t = 0.01. Monthly electricity consumption data were used to investigate how well the regression equation developed [i.e. equation (3)] can be used to predict the monthly electricity consumption. Actual consumption data are compared with the predicted ones. Figure 2 shows the comparison for 1993. It can be seen that, for the non-cooling season (i.e. January to April, November and December), the predicted consumption is quite close to the actual electricity use, indicating that the modelled equation can give a good estimation of the base load. During the hot summer months (i.e. May t o October), the consumption for air-conditioning is 6ver-estimated for May and June and under-estimated for July to October. The mean bias error (MBE) is -8.7 GWh, which is only - 2 . 8 % of the mean monthly consumption, indicating a slight under-estimation of the annual consumption. The root-mean-square error (RMSE) is 55 G W h which is 18% of the mean monthly consumption. Figure 3 shows the MBEs and RMSEs for the 23-year period. There seems to be a cycle of over- and under-estimation of the annual electricity use.

LAM: RESIDENTIAL ELECTRICITYCONSUMPTION

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Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Month Fig. 2. Comparison of actual and predictedmonthly electricityconsumption for 1993. ANNUAL ELECTRICITY CONSUMPTION Summation of the monthly consumption will obviously give the annual electricity use. However, it will be interesting to find how well the annual electricity use can be correlated directly with the economic and climatic variables and to see whether any long-term characteristics can be observed. Multiple regression analysis was used to correlate the annual consumption (ZE) with the annual household income (2:/), S, P and the annual C D D (2:CDD) for the 25 BB% MBE ~]% RMSE ]

2O

2 3

10

(!) s

o =o

0 -5

-10

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 Year

Fig. 3. Percentagemean bias error (MBE) and root-mean-squareerror (RMSE) (1971-1993).

628

LAM: RESIDENTIALELECTRICITYCONSUMPTION

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t m ACTUAL W]PREDICTED ]

3000

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,< 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 Year

Fig. 4. Comparisonof actual and predictedannualelectricityconsumption(1971-1993). 23-year period, and the resulting regression equation is as follows: InY.E = 0.113 +0.533 x l n E I + 0 . 9 x l n S - 0 . 1 8 3 x lnP+0.222 x InS"CDD

(4)

The R 2 is 0.98, indicating that 98% of the variation in annual electricity use can be explained by the variations in the yearly economic and climatic variables, equation (4) can be interpreted as follows. The annual income elasticity is 0.533, and a 1% increase in income will result in 0.53% increase in the annual electricity use. The elasticity coefficient for the household size is 0.9, and a 1% increase will cause 0.9% rise in consumption. The price elasticity is -0.183, indicating a 0.18% reduction in electricity use with a 1% increase in electricity price. For the cooling requirement, a 1% increase in CDD will result in a 0.22% rise in the electricity consumption. The actual annual electricity consumptions during the 23-year period were compared with those predicted by equation (4), and the results are shown in Fig. 4. The cycle of over- and under-estimation observed in the %MBE shown in Fig. 3 is also present in the annual comparison. The difference between the actual and predicted consumption ranges from -202 GWh in 1992 to 161 GWh in 1971, representing -5.5 and 13% of the actual electricity use in 1992 and 1971, respectively. The smallest percentage difference is 0.8% in 1993. The MBE and RMSE for the 23-year period is -2.7 and 89.7 GWh, respectively. These represent -0.I and 4% of the mean annual electricity use over that period. When considering the weather-induced consumption in forecasting residential electricity use, it will be useful to know the long-term weather pattern and assess the likely deviation. To investigate the long-term weather pattern in terms of CDD, outdoor dry-bulb air temperatures from 1960 to 1993 were gathered and analysed. The 34-year long-term mean annual CDD is 684°C with a standard deviation of 55°C. During the 34-year period, 82% of the annual CDD falls within the standard deviation. The long-term mean CDD can be used in equation (4) to forecast the annual electricity use in the residential sector, with the standard deviation as an indicator of the possible variation in the annual consumption for air-conditioning due to varying weather conditions. Based on the 684°C long-term mean CDD and the 55°C standard deviation, the possible variation in consumption is plus and minus 1.8%. CONCLUSIONS Building stocks account for a significant proportion of the total primary energy requirement in Hong Kong. Buildings, energy and the environment have become some of the key issues that

LAM: RESIDENTIALELECTRICITYCONSUMPTION

629

building professionals and energy and economic planners have to address. Information on the inter-relationship between energy consumption, prevailing weather conditions and local socioeconomic factors are important to the success of any energy management and conservation development programme. Regression analysis techniques have been used to correlate residential electricity consumption with economic, demographic and climatic factors. We have developed regression equations to estimate the seasonal and yearly electricity use based on four independent variables, namely average household income, household size, electricity price and cooling degree-days. The first three variables relate to economic conditions and living standard and the last variable accounts for the seasonal variation due to the air-conditioning requirement during the hot summer months. Both the base load and weather-induced electricity use have been rising during the chosen period (i.e. 1971-1993) mainly due to steady economic growth and, hence, continuing improvement in income per household. During periods of low economic growth or a recession, these consumptions would be expected to level off or even decline. Also, changing life styles, saturation of electrical appliances (especially air-conditioners) and energy conservation (due to greater awareness of environmental implications) would make all energy-economic-weather relations difficult to model accurately. Nevertheless, it is believed that the regression equations developed in the present study can give a good indication of the monthly and yearly electricity consumption in the residential sector. Many cities in southern China have a climate and culture similar to Hong Kong. Furthermore, with rapid development and high economic growth, it is envisaged that these cities would soon have high-density residential development and life styles comparable to those in Hong Kong. It is hoped that information presented in this paper would not only give people some idea about the residential energy use situation in Hong Kong, but also some implications for future energy studies for southern China. Acknowledgements--The work presented in this paper was funded by a UGC Competitive Earmarked Grant (Project

No. 9040077). The author would like to thank C. C. Mak for his help with the data collection. REFERENCES

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