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Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong
Applied Energy 87 (2010) 2321–2327
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Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong Tony N.T. Lam, Kevin K.W. Wan, S.L. Wong, Joseph C. Lam * Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
a r t i c l e
i n f o
Article history: Received 28 July 2009 Received in revised form 2 November 2009 Accepted 3 November 2009 Available online 26 November 2009 Keywords: Commercial sector Electricity use Air conditioning Global warming
a b s t r a c t Past and future trend of electricity use for air conditioning in the entire commercial sector in subtropical climates using 1979–2008 measured meteorological data as well as predictions for 2009–2100 from a general circulation model (MIROC3.2-H) was investigated. Air conditioning consumption showed an increasing trend over the past 30 years from 1979 to 2008. Principal component analysis (PCA) of measured and predicted monthly mean dry-bulb temperature, wet-bulb temperature and global solar radiation was conducted to determine a new climatic index Z for 1979–2008 and future 92 years (2009–2100) based on two emissions scenarios B1 and A1B (low and medium forcing). Through regression analysis, electricity use in air conditioning for the 92-year period was estimated. For low forcing, average consumption in 2009–2038, 2039–2068 and 2069–2100 would be, respectively, 5.7%, 12.8% and 18.4% more than the 1979–2008 average, with a mean 12.5% increase for the entire 92-year period. Medium forcing showed a similar increasing trend, but 1–4% more. Standard deviations of the monthly air conditioning consumption were found to be smaller suggesting possible reduction in seasonal variations in future years. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction Recent reports by the inter-governmental panel on climate change (IPCC) had raised public awareness of energy use and the environmental implications [1]. Hong Kong has no indigenous energy of her own and relies entirely on imported fuels. Over the past three decades, Hong Kong has seen significant increase in energy consumption, especially during economic expansion in the 1980s and early 1990s. Primary energy requirements (PER) rose from 195405 TJ in 1979 to 571851 TJ in 2008, representing an average annual growth rate of about 4% [2]. Most of the PER (coal, natural gas and oil products) was used for electricity generation, which accounted for 64% of the total PER in 2008. The commercial sector (office, restaurant, retail, hotel, education, health, storage and other miscellaneous commercial or public services) was the largest component, accounting for 61% of the total electricity consumption in 2008. Fig. 1 shows the monthly electricity consumption in the commercial sector during the past 30 years [3]. Two variations can be observed – yearly and seasonal. Annual electricity consumption in the commercial sector rose from 3823 GW h (13763 TJ) in 1979 to 27131 GW h (97672 TJ) in 2008, representing an average rate of increase of about 7% per year, a few percents higher than the PER growth rate. Rising demands for electricity * Corresponding author. Tel.: +852 2788 7606; fax: +852 2788 7612. E-mail address: [email protected] (J.C. Lam). 0306-2619/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2009.11.003
in this sector continued unabated, even during the economic downturn (the Asian financial crisis) in the late 1990s and early 2000s. A significant proportion of this consumption was due to the ever growing demand for better thermal comfort, especially in terms of air conditioning during the hot, humid summer months [4,5]. Earlier work on long-term ambient temperatures had revealed an overall trend of slight temperature increase during 1961–2000 [6]. More recently, it was found that summer discomfort showed an increasing trend over the past 41 years from 1968 to 2008, and mean heat stress in future years (2009–2100) would be about one-third higher than the 1968–2008 long-term mean [7]. The increasing trend of summer discomfort would result in more cooling demand, and more electricity use for air conditioning would lead to larger emissions, which in turn would exacerbate climate change and global warming. It is, therefore, important to have an idea about the likely changes in electricity use for air conditioning. There had been a number of studies on the impact of climate change on energy use. These studies were largely building-specific adopting either degree-days method (e.g. in the US [8] and Switzerland [9]) or building energy simulation technique using modified typical meteorological/reference year (TMY/TRY) hourly weather data (e.g. in the US [10], Australia [11,12], and London [13]). Archived predictions from general circulation models (GCMs), however, contain mostly monthly and/or daily data (e.g. the WCRP CMIP3 multi-model dataset [14]). Attempts were made
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3000
Monthly electricity use (GWh)
Total 2500 2000 1500
Air conditioning
1000 500 0 1979
2008
Month Fig. 1. Monthly electricity use in the commercial sector (1979–2008).
to generate future hourly data based on the archived daily values from these climate models [15,16]. An alternative, simpler approach would be to correlate energy use directly with daily/ monthly weather data. Although empirical or regression-based models using degree-days data tend to show good correlations between energy use and the prevailing weather conditions, most of them either consider only one weather variable (e.g. dry-bulb temperature), or do not adequately remove the bias in the weather variables during the multiple linear regression analysis [17]. Our earlier work on seasonal variations in sector-wide electricity consumption in subtropical climates had shown that regressions models based on principal component analysis (PCA) of key monthly climatic variables could give a good indication of the corresponding monthly and annual electricity use for the entire residential and commercial sectors [18]. The objective of the present work was, therefore, to investigate the past and future trends of electricity consumption for air conditioning in the local subtropical climates using PCA technique based on measured meteorological data and predictions from general circulation models. 2. Electricity use for air conditioning (1979–2008) Monthly electricity consumption in the commercial sector shown in Fig. 1 indicated distinct seasonal variations. In subtropical Hong Kong, winter is short and mild and summer is long, hot and humid. For commercial premises (office, hotel, shopping centre, etc.) with high internal heat loads, air conditioning operates all year round, though the main cooling season is from late March to early November [4,5,19,20]. It was found that about 10% of the total electricity consumption was for air conditioning outside the main cooling period. Based on this assumption, monthly electricity use for air conditioning was determined and is also shown in Fig. 1. Air conditioning consumption rose from 1120 GW h in 1979 to 7646 GW h in 2008 (nearly sixfold increase) and accounted for about 30% of the total electricity use in the commercial sector. This is consistent with the 29–32% published in the Hong Kong Energy End-use Data 2008 [21]. 3. Principal components analysis (PCA) of major meteorological variables In the analysis of long-term meteorological variables, it is often desirable to group key weather variables directly affecting energy use. PCA is a multivariate statistical technique for analysis of the dependencies existing among a set of inter-correlated variables
[22,23]. Because of its ability to categorise the complex and highly inter-correlated set of meteorological variables as one or more cohesive indices, PCA tends to give a better understanding of the cause/effect relationship. PCA is conducted on centred data or anomalies, and is used to identify patterns of simultaneous variations. Its purpose is to reduce a data set containing a large number of inter-correlated variables to a data set containing fewer hypothetical and uncorrelated components, which nevertheless represent a large fraction of the variability contained in the original data. These components are simply linear combinations of the original variables with coefficients given by the eigenvector. A property of the components is that each contributes to the total explained variance of the original variables. The analysis scheme requires that the component contributions occur in descending order of magnitude, such that the largest amount of variance of the first component explains the largest amount of variance of the original variables, the second the next largest, and so on. Initially five climatic variables were considered, namely drybulb temperature (DBT, in °C), wet-bulb temperature (WBT, in °C), global solar radiation (GSR, in MJ/m2), clearness index and wind speed [24]. DBT affects the amount of heat gain/loss through the building envelope and hence energy use for the corresponding sensible cooling/heating requirements, whereas WBT dictates the amount of humidification required during dry winter days and latent cooling under humid summer conditions. Information on solar radiation is crucial, especially in tropical and subtropical climates where solar heat gain is often a significant component of the air conditioning load [25]. Clearness index indicates the prevailing sky conditions while wind speed affects natural ventilation and the external surface resistance and hence U-values of the building envelope [26]. Contributions to the principal components from the clearness index and wind speed, however, were found to be small (at least one order of magnitude smaller) compared with DBT, WBT and GSR [27]. For instance, the coefficients for the clearness index and wind speed were 0.678 and 0.169, respectively. These were smaller than the coefficients for DBT, WBT and GSR shown in Eqs. (1) and (2). Furthermore, the clearness index varied between 0 and 1 and the wind speed was usually less than 5 m/s, which were very much smaller than the double digits values for DBT, WBT and GSR. These two climatic variables were, therefore, not considered. Weather conditions in future years were obtained from the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project Phase 3 (CMIP3) multi-model dataset [14]. Altogether, there were five GCMs that had archived monthly mean DBT, moisture content and GSR. Predictions from these five GCMs were downloaded and analysed. These GCMs included the BCCR-BCM2.0 (Norway), GISS-AOM (USA), INM-CM3.0 (Russia), MIROC3.2-H (Japan) and NCAR-CCSM3.0 (USA). They covered predictions for the past 10 decades (1900–1999) based on known emissions and future years (2000–2099 for NCAR-CCSM3.0 and BCCR-BCM2.0, and 2001–2100 for GISS-AOM, INM-CM3.0 and MIROC3.2-H) based on different emissions scenarios [28]. To get an idea about how well these GCMs could predict the temperature, humidity and solar radiation, predictions for the 21-year period (1979–1999) from these five GCMs were gathered and analysed. To compare like with like, predicted moisture content was converted to WBT. The predicted DBT, WBT and GSR were compared with the corresponding measured monthly mean data. A summary of the error analysis is shown in Table 1. In general, all five GCMs tended to have better predictions in temperature and humidity than in solar radiation. As for DBT, MIROC3.2-H had the smallest MBE (0.3%) and RMSE (7.6%). As for WBT, GISS-AOM had the smallest MBE (0.9%) while MIROC3.2-H had the smallest RMSE (8.5%). BCCR-BCM2.0 had the smallest error in GSR (MBE 12.3% and RMSE 22%). Performance (in terms of the percentage error) of the five
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T.N.T. Lam et al. / Applied Energy 87 (2010) 2321–2327 Table 1 Error analysis of predicted dry-bulb temperature (DBT), wet-bulb temperature (WBT) and global solar radiation (GSR) (1979–1999). DBT
WBT
MBE
MBE
RMSE
MBE
Overall average rank
RMSE
°C
%
Rank
°C
%
Rank
°C
%
Rank
°C
%
Rank
MJ/m2
%
Rank
MJ/m2
%
Rank
1.3 0.6 2.9 0.1 1.0
5.7 2.6 12.7 0.3 4.3
4 2 5 1 3
2.1 2.6 3.6 1.8 2.6
9.2 11.3 15.7 7.6 11.4
2 3 5 1 4
0.4 0.2 2.5 0.7 0.8
1.7 0.9 12.0 3.4 4.1
2 1 5 3 4
1.8 2.0 3.1 1.7 2.6
9.0 9.7 15.2 8.5 12.8
2 3 5 1 4
2.5 6.2 5.5 5.1 3.6
12.3 30.2 26.6 24.8 17.3
1 5 4 3 2
4.5 7.2 7.0 6.4 4.6
22.0 35.3 34.2 31.1 22.4
1 5 4 3 2
GCMs was ranked and a summary is also shown in Table 1. Apparently, MIROC3.2-H tended to perform well in temperature and humidity but only average in solar radiation among the five models. Its overall average ranking was 2, same as BCCR-BCM2.0. In this study, MIROC3.2-H was selected for two reasons. Firstly, temperature and humidity greatly affect air conditioning load, particularly latent cooling in subtropical climates. Secondly, our recent work on human bioclimates had found that MIROC3.2-H tended to show the best agreement between measured data and model predictions [7]. Predictions from the MIROC3.2-H GCM were used in the PCA for future years from 2009 to 2100 for two scenarios [14,28] – SRES B1 (low forcing, rapid change toward a service and information economy, peak global population in mid-21st century and decline thereafter, introduction of clean and resource-efficient technologies, and emphasis on global solutions to economic social and environmental sustainability), and SRES A1B (medium forcing, very rapid economic growth, same population trends as B1, convergence among regions with increased cultural and social interactions, and technological emphasis on a balanced mix of fossil and non-fossil energy resources). A data set consisting of 30-year (1979–2008) measured data and 92-year (2009–2100) predictions was established for each emissions scenario. Altogether 122 12 3 monthly data were considered in each PCA. Table 2 shows the coefficients of the three principal components and the relevant statistics from the PCA. The eigenvalue is a measure of the variance accounted for by the corresponding principal component. The first and largest eigenvalue accounts for most of the variance, and the second largest amounts of variance, and so on. The percentage is given by the ratio of the individual eigenvalue to the trace of the correlation matrix, and calculation of all possible eigenvalues (i.e. considering all principal components) would account for all of the variance of the original variables. Principal components can be ranked according to their ability to explain variance in the original data set. A common approach is to select only those with eigenvalues equal to or greater than one (eigenvalues greater than one implies that the new principal components contain at least as much information as any one of the original climatic variables [29]) or with at least 80% cumulative explained variance [30]. These criteria were adopted for this study. From Table 2 the first principal component had an eigenvalue greater than one with a
2.0 3.2 4.7 2.0 3.2
cumulative explained variance exceeding 81% (i.e. a one-component solution accounted for more than 81% of the variance in the original climatic variables). The first principal component was, therefore, retained, and a new set of monthly variable, Z, calculated as a linear combination of the original three climatic variables as follows:
For SRES B1 ðlow forcingÞ : Z ¼ 0:974 DBT þ 0:956 WBT þ 0:772 GSR
ð1Þ
For SRES A1B ðmedium forcingÞ : Z ¼ 0:973 DBT þ 0:957 WBT þ 0:765 GSR
ð2Þ
Measured data for the three climatic variables were analysed and the monthly values of Z determined for the 30-year period from 1979 to 2008. It was found that monthly values of Z determined from Eq. (2) were very close to those from Eq. (1). For simplicity, only one set of monthly Z for 1979–2008 based on Eq. (1) was considered. Fig. 2 shows the monthly values of Z during the 30-year period. The principal component profiles show distinct seasonal variations. Z tended to be at its lowest during the winter
75 70 65
Principle component Z
BCCR-BCM2.0 GISS-AOM INM-CM3.0 MIROC3.2-H NCAR-CCSM3.0
RMSE
GSR
60 55 50 45 40 35 30 25 Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Month Fig. 2. Monthly profiles of principal component Z (1979–2008).
Table 2 Summary of principal component analysis. Scenario
Principal component
Eigenvalue
Cumulative explained variance (%)
Coefficient DBT
WBT
GSR
SRES B1 (low forcing)
1st 2nd 3rd
2.457 0.536 0.006
81.9 99.8 100.0
0.974 0.220 0.057
0.956 0.290 0.054
0.772 0.636 0.005
SRES A1B (medium forcing)
1st 2nd 3rd
2.447 0.547 0.005
81.6 99.8 100.0
0.973 0.224 0.053
0.957 0.287 0.051
0.765 0.644 0.004
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T.N.T. Lam et al. / Applied Energy 87 (2010) 2321–2327 Table 3 Regression statistics and error analysis of electricity use for air conditioning in the commercial sector.
70
SRES B1 (low forcing)
Annual average Z
65 Future: slope = 0.053 per year average Z = 55.4
60 55 50 45
Past: slope = 0.06 per year average Z = 52.1
40 1979
1999
2019
2039
2059
2079
2100
Year 70
SRES A1B (medium forcing)
Annual average Z
65 Future: slope = 0.076 per year average Z = 55.8
60 55 50 45
Past: slope = 0.06 per year average Z = 52.1
40 1979
1999
2019
2039
2059
2079
2100
Year Fig. 3. Long-term trends of annual average principal component Z (1979–2100).
months (December, January and February) and peaked in the summer (June–August). Likewise, predictions from the GCM were used to determine the monthly Z for the low and medium forcing scenarios during 2009–2100 using Eqs. (1) and (2), respectively. To have a better understanding of the underlying trend, annual average Z were determined, and the 30-year (1979–2008) and 92-year (2009–2100) long-term trends are shown in Fig. 3. Both the past and future years show a clear (though slightly) increasing trend. Not surprisingly, medium forcing in future years has a larger slope of 0.076 per year than the past trend of 0.06. Average Z in future years would be 55.4 and 55.8 for low and medium forcing, respectively, representing an increase of 6.3% and 7.1% over the average Z of 52.1 during 1979–2008. 4. Correlation between electricity use for air conditioning and principal component The aim was to correlate air conditioning consumption with Z and develop a simple regression model. To account for the difference in the number of days in a calendar month, monthly electricity use in the commercial sector was divided by the number of days in that month for each of the 30 years. Ideally, all 30 years (i.e. 30 12 data) should be considered together in the regression analysis. But the varying magnitude (e.g. mean monthly air conditioning consumption in 2008 was 637 GW h, about six times larger than the 93 GW h in 1979) due to yearly economic and population growth made inter-year regression inappropriate. Regression analysis was thus conducted for each year, and a summary is shown in
Year
a
b
R2
MBE (GW h)
NMBE (%)
RMSE (GW h)
CVRMSE (%)
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
3.40 3.94 5.41 6.20 4.95 5.33 6.71 7.52 9.28 10.11 11.78 12.21 13.76 13.17 13.78 15.66 16.30 16.52 19.38 20.01 22.29 22.33 27.30 26.68 25.88 26.58 23.28 31.96 33.83 20.81
0.12 0.15 0.18 0.23 0.21 0.22 0.26 0.30 0.33 0.34 0.39 0.42 0.46 0.46 0.49 0.54 0.57 0.58 0.70 0.69 0.71 0.75 0.81 0.85 0.82 0.86 0.86 1.02 1.03 0.80
0.84 0.79 0.83 0.78 0.91 0.89 0.86 0.90 0.78 0.89 0.85 0.86 0.85 0.90 0.84 0.83 0.87 0.86 0.79 0.88 0.86 0.86 0.89 0.84 0.88 0.89 0.90 0.96 0.95 0.90
0.26 0.11 0.09 0.23 0.24 0.28 0.16 0.27 0.40 0.33 0.09 0.84 0.08 0.46 0.29 0.30 0.72 1.05 0.16 0.91 0.54 0.92 0.36 0.16 0.07 0.24 1.13 0.52 0.76 0.89
0.28 0.10 0.08 0.14 0.14 0.16 0.08 0.11 0.17 0.15 0.04 0.29 0.03 0.15 0.08 0.08 0.19 0.25 0.03 0.18 0.12 0.18 0.08 0.03 0.01 0.04 0.17 0.08 0.11 0.14
16.8 26.7 25.4 38.9 27.0 30.7 38.0 36.5 52.4 40.1 52.8 54.9 59.5 52.6 72.4 72.7 73.5 79.3 99.8 82.0 86.1 98.8 82.4 106.1 95.9 94.2 96.6 61.3 69.0 91.4
18.0 23.1 21.5 23.5 15.8 17.7 19.4 14.8 21.7 18.2 21.0 18.9 19.0 16.9 20.4 19.2 19.0 18.6 19.8 16.3 18.4 19.0 17.4 18.7 17.4 16.2 14.9 9.2 10.4 14.3
Table 3. The coefficient of determination R2 varied from 0.78 in 1982 and 1987 to 0.96 in 2006, indicating reasonably strong correlation between electricity use for air conditioning in the commercial sector and the corresponding principal component Z. An increasing trend can also be observed for the coefficients ‘a’ and ‘b’ from 1979 to 2008. The mean bias error (MBE) and root mean square error (RMSE) between the measured consumption and the regression-predicted values were determined. In order to conduct inter-year comparison, normalised MBE (NMBE) and coefficient of variation of RMSE (CVRMSE) were also determined (dividing by the corresponding mean consumption). The NMBE was very small ranging from 0.01% (under-estimation) in 2003 to 0.28% (over-estimation) in 1979, and the CVRMSE from 9.2% in 2006 to 23.5% in 1982. This suggests that predicted annual consumption could be very close to the measured value (less than 0.3%), but individual monthly data could differ by up to 23.5%. The results suggested that a more accurate annual energy consumption prediction could be made than that of the monthly consumption. This was probably due to some cancellations between over- and underestimations among individual monthly values. We believe that any of the 30 regression models shown in Table 3 could be used to assess the long-term trend of annual air conditioning consumption due to varying weather conditions. To test this hypothesis, four sets of long-term (1979–2008) annual air conditioning consumption were determined based on the 1979, 1989, 1999 and 2008 regression models, and a summary is shown in Fig. 4. The 1979 long-term trend showed the variations in annual air conditioning consumption due to varying weather conditions (i.e. different monthly Z in each of the 30 years) based on the 1979 consumption, and 1989 trend based on the 1989 consumption, and so on. Apart from showing the long-term trends (including yearly changes due to climatic variations), the idea was to also highlight the substantial increases in annual energy consumption for air conditioning over the past 30 years due largely to the pre-
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10000 Slope = 17.28 GWh/year
2008
Slope = 15.40 GWh/year
1999
8000
4000
Slope = 8.38 GWh/year
2000
Slope = 2.68 GWh/year
1989
1979
12000
SRES B1 (low forcing) 11000
Slope = 15.28 GWh/year Mean air conditioning consumption = 8521 GWh
10000 9000 8000 7000 6000
0
2009 2019 2029 2039 2049 2059 2069 2079 2089 2100
2008
SRES A1B (medium forcing) 11000
Slope = 22.19 GWh/year Mean air conditioning consumption = 8626 GWh
10000 9000 8000 7000 6000 2009 2019 2029 2039 2049 2059 2069 2079 2089 2100
Fig. 5. Long-term trends of predicted annual electricity use for air conditioning (2009–2100).
10000
8000
6000
4000
2009-2100
The aim was to estimate the likely changes in air conditioning consumption in future years based on the 2008 consumption, and the approach was to use the 2008 regression model and the monthly Z determined for the 92-year period from 2009 to 2100. The air conditioning consumption determined would not be the actual consumption in future years because of the likely changes in socio-economic factors (e.g. economic and population growth), which would certainly affect the structure and scale of the commercial sector. Nonetheless, the finding would certainly give a good indication of the underlying trend of any increase/decrease in air conditioning requirement due to climate change in future years. Fig. 5 shows the predicted annual electricity use for air conditioning during 2009–2100 for the two emissions scenarios. A distinct increasing trend can be observed for both emissions scenarios. As expected, medium forcing would result in bigger air conditioning requirement. The slope (rate of increase) would be 15.28 and 22.19 GW h per year for low and medium forcing, respectively. The 92-year average annual consumption for medium forcing would be 8626 GW h, only 1.2% larger than that for low forcing. To have a better understanding of the likely changes in air conditioning consumption due to varying weather conditions, average annual consumption over a 30-year period was analysed and a summary is shown in Fig. 6. All the consumption determined
12000
2069-2100
5. Changes in electricity use for air conditioning in future years (2009–2100)
Electricity use for air conditioning (GWh)
vailing socio-economic factors (e.g. economic and population growth), which affected the structure and scale of the commercial sector. It can be seen that all four regression models showed an increasing trend of air conditioning consumption over the 1979– 2008 period. It seems that the 2008 trend had a much larger slope and bigger inter-year variations. A closer examination, however, revealed that they all showed similar trends. The main difference was the magnitude (i.e. if the 1979 consumption was magnified or increased to that of 2008 level, then the slopes would be close to each other). Standard deviations of the yearly air conditioning consumption were also determined for the four long-term trends. These were 37.6, 117.5, 216.2 and 242.6 GW h for 1979, 1989, 1999 and 2008 trends, respectively, and when divided by their respective mean annual consumption, they were all within 3–4%. These findings (i.e. similar slope and inter-year variations) tended to indicate an almost equal ability to estimate long-term trend of air conditioning consumption due to vary weather conditions.
Air conditioning consumption (GWh)
Fig. 4. Underlying trends of annual electricity use for air conditioning (1979–2008) based on 1979, 1989, 1999 and 2008 consumption.
2039-2068
2004
2009-2038
2000
1979-2008
1996
Year
2009-2100
1992
2069-2100
1988
2039-2068
1984
2009-2038
1980
1979-2008
6000
Electricity use for air conditioning (GWh)
Annual electricity use for air conditioning (GWh)
T.N.T. Lam et al. / Applied Energy 87 (2010) 2321–2327
2000
0
SRES B1 (lowing forcing)
SRES A1B (medium forcing)
Fig. 6. Average annual electricity use for air conditioning in the commercial sector during 1979–2008, 2009–2038, 2039–2068, 2069–2100 and 2009–2100.
was based on the 2008 air conditioning consumption. It can be seen that there would be steady increases from one period to another. For low forcing, average consumption in 2009–2038, 2039–2068, 2069–2100 and 2009–2100 would be 5.7%, 12.8%, 18.4% and 12.5% more than the 1979–2008 average, respectively. Medium forcing showed a similar increasing trend, but 1–4% more. It is interesting to see that average air conditioning consumption
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40
2009-2100
2069-2100
2039-2068
2009-2038
1979-2008
2009-2100
2069-2100
2039-2068
20
2009-2038
30
1979-2008
Normalised standard deviation (%)
50
10
0
SRES B1 (lowing forcing)
SRES A1B (medium forcing)
Fig. 7. Normalised standard deviations of air conditioning consumption in the commercial sector during 1979–2008, 2009–2038, 2039–2068, 2069–2100 and 2009–2100.
under the medium forcing scenario would be smaller than low forcing during 2009–2038, but would pick up more rapidly thereafter. To investigate whether climate change would affect seasonal variations, standard deviations of the monthly air conditioning consumption for each of the 122 years (1979–2100) were determined. It was found that the standard deviations would be smaller in future years, from 276 GW h in 1979–2008 to about 234 GW h and 238 GW h in 2009–2100 for low and medium forcing, respectively. To better reflect the significance and magnitude of seasonal variations, standard deviation was normalised by its corresponding mean monthly air conditioning consumption, and a summary of the average values during the 1979–2008, 2009–2038, 2039– 2068, 2069–2100 and 2009–2100 periods is shown in Fig. 7. It is interesting to see that the normalised standard deviation would be reduced steadily through the different 30-year periods, from 43.8% to 30.7% for both emissions scenarios. This suggests that there would be less seasonal variations in the electricity consumption for air conditioning in the commercial sector in future years as a result of possible climate change. The 2008 regression model showed an over-estimation of 0.89 GW h, 0.14% of the 2008 electricity use for air conditioning (see Table 3). It is envisaged that predicted energy consumption based on the regression model used would tend to have biases with similar magnitude (in percentage terms), and the results should, therefore, be interpreted in such context. The long-term trends shown in Fig. 5, however, would not be significantly affected. 6. Conclusions There had been a sixfold increase in electricity use in the commercial sector (office, restaurant, retail, hotel, education, health, storage and other miscellaneous commercial or public services) during the 30-year period from 1979 to 2008. Air conditioning accounted for about 30% of the total sector-wide consumption. A new climatic index Z was determined using principal component analysis of key meteorological variables. Regression analysis of monthly electricity use for air conditioning in the entire commercial sector and the corresponding Z showed an increasing trend of air conditioning consumption during 1979–2008, probably due to warmer weather in recent years. Monthly mean dry-bulb temperature, moisture content and global solar radiation predictions from a general circulation model (MIROC3.2-H) were used to determine principal component Z and hence air conditioning consumption for
future years (2009–2100) based on two emissions scenarios SRES B1 (low forcing) and SRES A1B (medium forcing). All the consumption determined was based on the 2008 air conditioning consumption. For low forcing, average consumption in 2009–2038, 2039– 2068, 2069–2100 and 2009–2100 would be 5.7%, 12.8%, 18.4% and 12.5% more than the 1979–2008 average, respectively. Medium forcing showed a similar increasing trend, but 1–4% more than the low forcing scenario. It was found that the standard deviations of the monthly air conditioning consumption would be smaller in future years, from 276 GW h in 1979–2008 to about 234 GW h and 238 GW h in 2009–2100 for low and medium forcing, respectively, suggesting possible reduction in seasonal variation. It is hoped that these findings would give building professionals, and energy/environmental policy makers an idea about the likely increase in air conditioning consumption due to climate change in future years, and help them to consider energy efficiency measures required (e.g. tighter building energy code, raising the energy efficiency of equipment currently adopted in air conditioning and refrigeration systems) that could alleviate the impact on the environment. Although this study only considered subtropical Hong Kong, we believe the technique developed could be applied to other locations with similar or different climates. Acknowledgements T.N.T. Lam and K.K.W. Wan were supported by City University of Hong Kong Studentships. Measured weather data were obtained from the Hong Kong Observatory of the Hong Kong SAR. We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. References [1] Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, et al., editors. Climate change 2007: the physical science basis, contribution of the working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press; 2007. [2] Hong Kong Energy Statistics Annual Report. Census and Statistics Department, Hong Kong SAR, China; 1979–2008.. [3] Hong Kong Monthly Digest of Statistics. Census and Statistics Department, Hong Kong SAR, China; 1979–2008. . [4] Lam JC, Li DHW. Electricity consumption characteristics in shopping malls in subtropical climates. Energy Convers Manage 2003;44:1391–8. [5] Lam JC, Chan RYC, Tsang CL, Li DHW. Electricity use characteristics of purposebuilt office buildings in subtropical climates. Energy Convers Manage 2004;45:829–44. [6] Lam JC, Tsang CL, Li DHW. Long-term ambient temperature analysis and energy use implications in Hong Kong. Energy Convers Manage 2004;45:315–27. [7] Lam JC et al. Long-term trends of heat stress and energy use implications in subtropical climates. Appl Energy 2010;87:608–12. [8] Belzer DB, Scott MJ, Sands RD. Climate change impacts on US commercial building energy consumption: an analysis using sample survey data. Energy Sources 1996;18:177–201. [9] Frank T. Climate change impacts on building heating and cooling energy demand in Switzerland. Energy Build 2005;37:1175–85. [10] Scott MJ, Wrench LE, Hadley DL. Effects of climate change on commercial building energy demand. Energy Sources 1994;16:317–32. [11] Sheppard R, de Dear RJ, Rowe D, McAvaney B. The impact of climate change on commercial building energy consumption: the Sydney region. AIRAH J 1997;51:20–5. [12] Guan L. Implication of global warming on air-conditioned office buildings in Australia. Build Res Inform 2009;37:43–54. [13] Day AR, Jones PG, Maidment GG. Forecasting future cooling demand in London. Energy Build 2009;41:942–8. [14] Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, et al. The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 2007;88:1383–94. [15] Chow DHC, Levermore G. New algorithm for generating hourly temperature values using daily maximum, minimum and average values from climate models. Build Serv Eng Res Technol 2007;28:237–48.
T.N.T. Lam et al. / Applied Energy 87 (2010) 2321–2327 [16] Guan L. Preparation of future weather data to study the impact of climate change on buildings. Build Environ 2009;44:793–800. [17] Hadley DL. Daily variations in HVAC system electrical energy consumption in response to different weather conditions. Energy Build 1993;19:235–47. [18] Lam JC, Tang HL, Li DHW. Seasonal variations in residential and commercial sector electricity consumption in Hong Kong. Energy 2008;33:513–23. [19] Lam JC. Building envelope loads and commercial sector electricity use in Hong Kong. Energy 1995;20:189–94. [20] Lam JC, Wan KKW, Cheung KL. An analysis of climatic influences on chiller plant electricity consumption. Appl Energy 2009;86:933–40. [21] Hong Kong Energy End-use Data. Electrical and Mechanical Services Department, Hong Kong SAR, China; 2008.. [22] Wilks DS. Statistical method in the atmospheric sciences: an introduction. San Diego: Academic Press; 1995. [23] Storch HV, Zwiers FW. Statistical analysis in climate research. Cambridge: Cambridge University Press; 1999.
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong Tony N.T. Lam, Kevin K.W. Wan, S.L. Wong, Joseph C. Lam * Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
a r t i c l e
i n f o
Article history: Received 28 July 2009 Received in revised form 2 November 2009 Accepted 3 November 2009 Available online 26 November 2009 Keywords: Commercial sector Electricity use Air conditioning Global warming
a b s t r a c t Past and future trend of electricity use for air conditioning in the entire commercial sector in subtropical climates using 1979–2008 measured meteorological data as well as predictions for 2009–2100 from a general circulation model (MIROC3.2-H) was investigated. Air conditioning consumption showed an increasing trend over the past 30 years from 1979 to 2008. Principal component analysis (PCA) of measured and predicted monthly mean dry-bulb temperature, wet-bulb temperature and global solar radiation was conducted to determine a new climatic index Z for 1979–2008 and future 92 years (2009–2100) based on two emissions scenarios B1 and A1B (low and medium forcing). Through regression analysis, electricity use in air conditioning for the 92-year period was estimated. For low forcing, average consumption in 2009–2038, 2039–2068 and 2069–2100 would be, respectively, 5.7%, 12.8% and 18.4% more than the 1979–2008 average, with a mean 12.5% increase for the entire 92-year period. Medium forcing showed a similar increasing trend, but 1–4% more. Standard deviations of the monthly air conditioning consumption were found to be smaller suggesting possible reduction in seasonal variations in future years. Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction Recent reports by the inter-governmental panel on climate change (IPCC) had raised public awareness of energy use and the environmental implications [1]. Hong Kong has no indigenous energy of her own and relies entirely on imported fuels. Over the past three decades, Hong Kong has seen significant increase in energy consumption, especially during economic expansion in the 1980s and early 1990s. Primary energy requirements (PER) rose from 195405 TJ in 1979 to 571851 TJ in 2008, representing an average annual growth rate of about 4% [2]. Most of the PER (coal, natural gas and oil products) was used for electricity generation, which accounted for 64% of the total PER in 2008. The commercial sector (office, restaurant, retail, hotel, education, health, storage and other miscellaneous commercial or public services) was the largest component, accounting for 61% of the total electricity consumption in 2008. Fig. 1 shows the monthly electricity consumption in the commercial sector during the past 30 years [3]. Two variations can be observed – yearly and seasonal. Annual electricity consumption in the commercial sector rose from 3823 GW h (13763 TJ) in 1979 to 27131 GW h (97672 TJ) in 2008, representing an average rate of increase of about 7% per year, a few percents higher than the PER growth rate. Rising demands for electricity * Corresponding author. Tel.: +852 2788 7606; fax: +852 2788 7612. E-mail address: [email protected] (J.C. Lam). 0306-2619/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2009.11.003
in this sector continued unabated, even during the economic downturn (the Asian financial crisis) in the late 1990s and early 2000s. A significant proportion of this consumption was due to the ever growing demand for better thermal comfort, especially in terms of air conditioning during the hot, humid summer months [4,5]. Earlier work on long-term ambient temperatures had revealed an overall trend of slight temperature increase during 1961–2000 [6]. More recently, it was found that summer discomfort showed an increasing trend over the past 41 years from 1968 to 2008, and mean heat stress in future years (2009–2100) would be about one-third higher than the 1968–2008 long-term mean [7]. The increasing trend of summer discomfort would result in more cooling demand, and more electricity use for air conditioning would lead to larger emissions, which in turn would exacerbate climate change and global warming. It is, therefore, important to have an idea about the likely changes in electricity use for air conditioning. There had been a number of studies on the impact of climate change on energy use. These studies were largely building-specific adopting either degree-days method (e.g. in the US [8] and Switzerland [9]) or building energy simulation technique using modified typical meteorological/reference year (TMY/TRY) hourly weather data (e.g. in the US [10], Australia [11,12], and London [13]). Archived predictions from general circulation models (GCMs), however, contain mostly monthly and/or daily data (e.g. the WCRP CMIP3 multi-model dataset [14]). Attempts were made
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3000
Monthly electricity use (GWh)
Total 2500 2000 1500
Air conditioning
1000 500 0 1979
2008
Month Fig. 1. Monthly electricity use in the commercial sector (1979–2008).
to generate future hourly data based on the archived daily values from these climate models [15,16]. An alternative, simpler approach would be to correlate energy use directly with daily/ monthly weather data. Although empirical or regression-based models using degree-days data tend to show good correlations between energy use and the prevailing weather conditions, most of them either consider only one weather variable (e.g. dry-bulb temperature), or do not adequately remove the bias in the weather variables during the multiple linear regression analysis [17]. Our earlier work on seasonal variations in sector-wide electricity consumption in subtropical climates had shown that regressions models based on principal component analysis (PCA) of key monthly climatic variables could give a good indication of the corresponding monthly and annual electricity use for the entire residential and commercial sectors [18]. The objective of the present work was, therefore, to investigate the past and future trends of electricity consumption for air conditioning in the local subtropical climates using PCA technique based on measured meteorological data and predictions from general circulation models. 2. Electricity use for air conditioning (1979–2008) Monthly electricity consumption in the commercial sector shown in Fig. 1 indicated distinct seasonal variations. In subtropical Hong Kong, winter is short and mild and summer is long, hot and humid. For commercial premises (office, hotel, shopping centre, etc.) with high internal heat loads, air conditioning operates all year round, though the main cooling season is from late March to early November [4,5,19,20]. It was found that about 10% of the total electricity consumption was for air conditioning outside the main cooling period. Based on this assumption, monthly electricity use for air conditioning was determined and is also shown in Fig. 1. Air conditioning consumption rose from 1120 GW h in 1979 to 7646 GW h in 2008 (nearly sixfold increase) and accounted for about 30% of the total electricity use in the commercial sector. This is consistent with the 29–32% published in the Hong Kong Energy End-use Data 2008 [21]. 3. Principal components analysis (PCA) of major meteorological variables In the analysis of long-term meteorological variables, it is often desirable to group key weather variables directly affecting energy use. PCA is a multivariate statistical technique for analysis of the dependencies existing among a set of inter-correlated variables
[22,23]. Because of its ability to categorise the complex and highly inter-correlated set of meteorological variables as one or more cohesive indices, PCA tends to give a better understanding of the cause/effect relationship. PCA is conducted on centred data or anomalies, and is used to identify patterns of simultaneous variations. Its purpose is to reduce a data set containing a large number of inter-correlated variables to a data set containing fewer hypothetical and uncorrelated components, which nevertheless represent a large fraction of the variability contained in the original data. These components are simply linear combinations of the original variables with coefficients given by the eigenvector. A property of the components is that each contributes to the total explained variance of the original variables. The analysis scheme requires that the component contributions occur in descending order of magnitude, such that the largest amount of variance of the first component explains the largest amount of variance of the original variables, the second the next largest, and so on. Initially five climatic variables were considered, namely drybulb temperature (DBT, in °C), wet-bulb temperature (WBT, in °C), global solar radiation (GSR, in MJ/m2), clearness index and wind speed [24]. DBT affects the amount of heat gain/loss through the building envelope and hence energy use for the corresponding sensible cooling/heating requirements, whereas WBT dictates the amount of humidification required during dry winter days and latent cooling under humid summer conditions. Information on solar radiation is crucial, especially in tropical and subtropical climates where solar heat gain is often a significant component of the air conditioning load [25]. Clearness index indicates the prevailing sky conditions while wind speed affects natural ventilation and the external surface resistance and hence U-values of the building envelope [26]. Contributions to the principal components from the clearness index and wind speed, however, were found to be small (at least one order of magnitude smaller) compared with DBT, WBT and GSR [27]. For instance, the coefficients for the clearness index and wind speed were 0.678 and 0.169, respectively. These were smaller than the coefficients for DBT, WBT and GSR shown in Eqs. (1) and (2). Furthermore, the clearness index varied between 0 and 1 and the wind speed was usually less than 5 m/s, which were very much smaller than the double digits values for DBT, WBT and GSR. These two climatic variables were, therefore, not considered. Weather conditions in future years were obtained from the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project Phase 3 (CMIP3) multi-model dataset [14]. Altogether, there were five GCMs that had archived monthly mean DBT, moisture content and GSR. Predictions from these five GCMs were downloaded and analysed. These GCMs included the BCCR-BCM2.0 (Norway), GISS-AOM (USA), INM-CM3.0 (Russia), MIROC3.2-H (Japan) and NCAR-CCSM3.0 (USA). They covered predictions for the past 10 decades (1900–1999) based on known emissions and future years (2000–2099 for NCAR-CCSM3.0 and BCCR-BCM2.0, and 2001–2100 for GISS-AOM, INM-CM3.0 and MIROC3.2-H) based on different emissions scenarios [28]. To get an idea about how well these GCMs could predict the temperature, humidity and solar radiation, predictions for the 21-year period (1979–1999) from these five GCMs were gathered and analysed. To compare like with like, predicted moisture content was converted to WBT. The predicted DBT, WBT and GSR were compared with the corresponding measured monthly mean data. A summary of the error analysis is shown in Table 1. In general, all five GCMs tended to have better predictions in temperature and humidity than in solar radiation. As for DBT, MIROC3.2-H had the smallest MBE (0.3%) and RMSE (7.6%). As for WBT, GISS-AOM had the smallest MBE (0.9%) while MIROC3.2-H had the smallest RMSE (8.5%). BCCR-BCM2.0 had the smallest error in GSR (MBE 12.3% and RMSE 22%). Performance (in terms of the percentage error) of the five
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T.N.T. Lam et al. / Applied Energy 87 (2010) 2321–2327 Table 1 Error analysis of predicted dry-bulb temperature (DBT), wet-bulb temperature (WBT) and global solar radiation (GSR) (1979–1999). DBT
WBT
MBE
MBE
RMSE
MBE
Overall average rank
RMSE
°C
%
Rank
°C
%
Rank
°C
%
Rank
°C
%
Rank
MJ/m2
%
Rank
MJ/m2
%
Rank
1.3 0.6 2.9 0.1 1.0
5.7 2.6 12.7 0.3 4.3
4 2 5 1 3
2.1 2.6 3.6 1.8 2.6
9.2 11.3 15.7 7.6 11.4
2 3 5 1 4
0.4 0.2 2.5 0.7 0.8
1.7 0.9 12.0 3.4 4.1
2 1 5 3 4
1.8 2.0 3.1 1.7 2.6
9.0 9.7 15.2 8.5 12.8
2 3 5 1 4
2.5 6.2 5.5 5.1 3.6
12.3 30.2 26.6 24.8 17.3
1 5 4 3 2
4.5 7.2 7.0 6.4 4.6
22.0 35.3 34.2 31.1 22.4
1 5 4 3 2
GCMs was ranked and a summary is also shown in Table 1. Apparently, MIROC3.2-H tended to perform well in temperature and humidity but only average in solar radiation among the five models. Its overall average ranking was 2, same as BCCR-BCM2.0. In this study, MIROC3.2-H was selected for two reasons. Firstly, temperature and humidity greatly affect air conditioning load, particularly latent cooling in subtropical climates. Secondly, our recent work on human bioclimates had found that MIROC3.2-H tended to show the best agreement between measured data and model predictions [7]. Predictions from the MIROC3.2-H GCM were used in the PCA for future years from 2009 to 2100 for two scenarios [14,28] – SRES B1 (low forcing, rapid change toward a service and information economy, peak global population in mid-21st century and decline thereafter, introduction of clean and resource-efficient technologies, and emphasis on global solutions to economic social and environmental sustainability), and SRES A1B (medium forcing, very rapid economic growth, same population trends as B1, convergence among regions with increased cultural and social interactions, and technological emphasis on a balanced mix of fossil and non-fossil energy resources). A data set consisting of 30-year (1979–2008) measured data and 92-year (2009–2100) predictions was established for each emissions scenario. Altogether 122 12 3 monthly data were considered in each PCA. Table 2 shows the coefficients of the three principal components and the relevant statistics from the PCA. The eigenvalue is a measure of the variance accounted for by the corresponding principal component. The first and largest eigenvalue accounts for most of the variance, and the second largest amounts of variance, and so on. The percentage is given by the ratio of the individual eigenvalue to the trace of the correlation matrix, and calculation of all possible eigenvalues (i.e. considering all principal components) would account for all of the variance of the original variables. Principal components can be ranked according to their ability to explain variance in the original data set. A common approach is to select only those with eigenvalues equal to or greater than one (eigenvalues greater than one implies that the new principal components contain at least as much information as any one of the original climatic variables [29]) or with at least 80% cumulative explained variance [30]. These criteria were adopted for this study. From Table 2 the first principal component had an eigenvalue greater than one with a
2.0 3.2 4.7 2.0 3.2
cumulative explained variance exceeding 81% (i.e. a one-component solution accounted for more than 81% of the variance in the original climatic variables). The first principal component was, therefore, retained, and a new set of monthly variable, Z, calculated as a linear combination of the original three climatic variables as follows:
For SRES B1 ðlow forcingÞ : Z ¼ 0:974 DBT þ 0:956 WBT þ 0:772 GSR
ð1Þ
For SRES A1B ðmedium forcingÞ : Z ¼ 0:973 DBT þ 0:957 WBT þ 0:765 GSR
ð2Þ
Measured data for the three climatic variables were analysed and the monthly values of Z determined for the 30-year period from 1979 to 2008. It was found that monthly values of Z determined from Eq. (2) were very close to those from Eq. (1). For simplicity, only one set of monthly Z for 1979–2008 based on Eq. (1) was considered. Fig. 2 shows the monthly values of Z during the 30-year period. The principal component profiles show distinct seasonal variations. Z tended to be at its lowest during the winter
75 70 65
Principle component Z
BCCR-BCM2.0 GISS-AOM INM-CM3.0 MIROC3.2-H NCAR-CCSM3.0
RMSE
GSR
60 55 50 45 40 35 30 25 Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Month Fig. 2. Monthly profiles of principal component Z (1979–2008).
Table 2 Summary of principal component analysis. Scenario
Principal component
Eigenvalue
Cumulative explained variance (%)
Coefficient DBT
WBT
GSR
SRES B1 (low forcing)
1st 2nd 3rd
2.457 0.536 0.006
81.9 99.8 100.0
0.974 0.220 0.057
0.956 0.290 0.054
0.772 0.636 0.005
SRES A1B (medium forcing)
1st 2nd 3rd
2.447 0.547 0.005
81.6 99.8 100.0
0.973 0.224 0.053
0.957 0.287 0.051
0.765 0.644 0.004
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T.N.T. Lam et al. / Applied Energy 87 (2010) 2321–2327 Table 3 Regression statistics and error analysis of electricity use for air conditioning in the commercial sector.
70
SRES B1 (low forcing)
Annual average Z
65 Future: slope = 0.053 per year average Z = 55.4
60 55 50 45
Past: slope = 0.06 per year average Z = 52.1
40 1979
1999
2019
2039
2059
2079
2100
Year 70
SRES A1B (medium forcing)
Annual average Z
65 Future: slope = 0.076 per year average Z = 55.8
60 55 50 45
Past: slope = 0.06 per year average Z = 52.1
40 1979
1999
2019
2039
2059
2079
2100
Year Fig. 3. Long-term trends of annual average principal component Z (1979–2100).
months (December, January and February) and peaked in the summer (June–August). Likewise, predictions from the GCM were used to determine the monthly Z for the low and medium forcing scenarios during 2009–2100 using Eqs. (1) and (2), respectively. To have a better understanding of the underlying trend, annual average Z were determined, and the 30-year (1979–2008) and 92-year (2009–2100) long-term trends are shown in Fig. 3. Both the past and future years show a clear (though slightly) increasing trend. Not surprisingly, medium forcing in future years has a larger slope of 0.076 per year than the past trend of 0.06. Average Z in future years would be 55.4 and 55.8 for low and medium forcing, respectively, representing an increase of 6.3% and 7.1% over the average Z of 52.1 during 1979–2008. 4. Correlation between electricity use for air conditioning and principal component The aim was to correlate air conditioning consumption with Z and develop a simple regression model. To account for the difference in the number of days in a calendar month, monthly electricity use in the commercial sector was divided by the number of days in that month for each of the 30 years. Ideally, all 30 years (i.e. 30 12 data) should be considered together in the regression analysis. But the varying magnitude (e.g. mean monthly air conditioning consumption in 2008 was 637 GW h, about six times larger than the 93 GW h in 1979) due to yearly economic and population growth made inter-year regression inappropriate. Regression analysis was thus conducted for each year, and a summary is shown in
Year
a
b
R2
MBE (GW h)
NMBE (%)
RMSE (GW h)
CVRMSE (%)
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
3.40 3.94 5.41 6.20 4.95 5.33 6.71 7.52 9.28 10.11 11.78 12.21 13.76 13.17 13.78 15.66 16.30 16.52 19.38 20.01 22.29 22.33 27.30 26.68 25.88 26.58 23.28 31.96 33.83 20.81
0.12 0.15 0.18 0.23 0.21 0.22 0.26 0.30 0.33 0.34 0.39 0.42 0.46 0.46 0.49 0.54 0.57 0.58 0.70 0.69 0.71 0.75 0.81 0.85 0.82 0.86 0.86 1.02 1.03 0.80
0.84 0.79 0.83 0.78 0.91 0.89 0.86 0.90 0.78 0.89 0.85 0.86 0.85 0.90 0.84 0.83 0.87 0.86 0.79 0.88 0.86 0.86 0.89 0.84 0.88 0.89 0.90 0.96 0.95 0.90
0.26 0.11 0.09 0.23 0.24 0.28 0.16 0.27 0.40 0.33 0.09 0.84 0.08 0.46 0.29 0.30 0.72 1.05 0.16 0.91 0.54 0.92 0.36 0.16 0.07 0.24 1.13 0.52 0.76 0.89
0.28 0.10 0.08 0.14 0.14 0.16 0.08 0.11 0.17 0.15 0.04 0.29 0.03 0.15 0.08 0.08 0.19 0.25 0.03 0.18 0.12 0.18 0.08 0.03 0.01 0.04 0.17 0.08 0.11 0.14
16.8 26.7 25.4 38.9 27.0 30.7 38.0 36.5 52.4 40.1 52.8 54.9 59.5 52.6 72.4 72.7 73.5 79.3 99.8 82.0 86.1 98.8 82.4 106.1 95.9 94.2 96.6 61.3 69.0 91.4
18.0 23.1 21.5 23.5 15.8 17.7 19.4 14.8 21.7 18.2 21.0 18.9 19.0 16.9 20.4 19.2 19.0 18.6 19.8 16.3 18.4 19.0 17.4 18.7 17.4 16.2 14.9 9.2 10.4 14.3
Table 3. The coefficient of determination R2 varied from 0.78 in 1982 and 1987 to 0.96 in 2006, indicating reasonably strong correlation between electricity use for air conditioning in the commercial sector and the corresponding principal component Z. An increasing trend can also be observed for the coefficients ‘a’ and ‘b’ from 1979 to 2008. The mean bias error (MBE) and root mean square error (RMSE) between the measured consumption and the regression-predicted values were determined. In order to conduct inter-year comparison, normalised MBE (NMBE) and coefficient of variation of RMSE (CVRMSE) were also determined (dividing by the corresponding mean consumption). The NMBE was very small ranging from 0.01% (under-estimation) in 2003 to 0.28% (over-estimation) in 1979, and the CVRMSE from 9.2% in 2006 to 23.5% in 1982. This suggests that predicted annual consumption could be very close to the measured value (less than 0.3%), but individual monthly data could differ by up to 23.5%. The results suggested that a more accurate annual energy consumption prediction could be made than that of the monthly consumption. This was probably due to some cancellations between over- and underestimations among individual monthly values. We believe that any of the 30 regression models shown in Table 3 could be used to assess the long-term trend of annual air conditioning consumption due to varying weather conditions. To test this hypothesis, four sets of long-term (1979–2008) annual air conditioning consumption were determined based on the 1979, 1989, 1999 and 2008 regression models, and a summary is shown in Fig. 4. The 1979 long-term trend showed the variations in annual air conditioning consumption due to varying weather conditions (i.e. different monthly Z in each of the 30 years) based on the 1979 consumption, and 1989 trend based on the 1989 consumption, and so on. Apart from showing the long-term trends (including yearly changes due to climatic variations), the idea was to also highlight the substantial increases in annual energy consumption for air conditioning over the past 30 years due largely to the pre-
2325
10000 Slope = 17.28 GWh/year
2008
Slope = 15.40 GWh/year
1999
8000
4000
Slope = 8.38 GWh/year
2000
Slope = 2.68 GWh/year
1989
1979
12000
SRES B1 (low forcing) 11000
Slope = 15.28 GWh/year Mean air conditioning consumption = 8521 GWh
10000 9000 8000 7000 6000
0
2009 2019 2029 2039 2049 2059 2069 2079 2089 2100
2008
SRES A1B (medium forcing) 11000
Slope = 22.19 GWh/year Mean air conditioning consumption = 8626 GWh
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Fig. 5. Long-term trends of predicted annual electricity use for air conditioning (2009–2100).
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The aim was to estimate the likely changes in air conditioning consumption in future years based on the 2008 consumption, and the approach was to use the 2008 regression model and the monthly Z determined for the 92-year period from 2009 to 2100. The air conditioning consumption determined would not be the actual consumption in future years because of the likely changes in socio-economic factors (e.g. economic and population growth), which would certainly affect the structure and scale of the commercial sector. Nonetheless, the finding would certainly give a good indication of the underlying trend of any increase/decrease in air conditioning requirement due to climate change in future years. Fig. 5 shows the predicted annual electricity use for air conditioning during 2009–2100 for the two emissions scenarios. A distinct increasing trend can be observed for both emissions scenarios. As expected, medium forcing would result in bigger air conditioning requirement. The slope (rate of increase) would be 15.28 and 22.19 GW h per year for low and medium forcing, respectively. The 92-year average annual consumption for medium forcing would be 8626 GW h, only 1.2% larger than that for low forcing. To have a better understanding of the likely changes in air conditioning consumption due to varying weather conditions, average annual consumption over a 30-year period was analysed and a summary is shown in Fig. 6. All the consumption determined
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vailing socio-economic factors (e.g. economic and population growth), which affected the structure and scale of the commercial sector. It can be seen that all four regression models showed an increasing trend of air conditioning consumption over the 1979– 2008 period. It seems that the 2008 trend had a much larger slope and bigger inter-year variations. A closer examination, however, revealed that they all showed similar trends. The main difference was the magnitude (i.e. if the 1979 consumption was magnified or increased to that of 2008 level, then the slopes would be close to each other). Standard deviations of the yearly air conditioning consumption were also determined for the four long-term trends. These were 37.6, 117.5, 216.2 and 242.6 GW h for 1979, 1989, 1999 and 2008 trends, respectively, and when divided by their respective mean annual consumption, they were all within 3–4%. These findings (i.e. similar slope and inter-year variations) tended to indicate an almost equal ability to estimate long-term trend of air conditioning consumption due to vary weather conditions.
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Fig. 4. Underlying trends of annual electricity use for air conditioning (1979–2008) based on 1979, 1989, 1999 and 2008 consumption.
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Fig. 6. Average annual electricity use for air conditioning in the commercial sector during 1979–2008, 2009–2038, 2039–2068, 2069–2100 and 2009–2100.
was based on the 2008 air conditioning consumption. It can be seen that there would be steady increases from one period to another. For low forcing, average consumption in 2009–2038, 2039–2068, 2069–2100 and 2009–2100 would be 5.7%, 12.8%, 18.4% and 12.5% more than the 1979–2008 average, respectively. Medium forcing showed a similar increasing trend, but 1–4% more. It is interesting to see that average air conditioning consumption
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Fig. 7. Normalised standard deviations of air conditioning consumption in the commercial sector during 1979–2008, 2009–2038, 2039–2068, 2069–2100 and 2009–2100.
under the medium forcing scenario would be smaller than low forcing during 2009–2038, but would pick up more rapidly thereafter. To investigate whether climate change would affect seasonal variations, standard deviations of the monthly air conditioning consumption for each of the 122 years (1979–2100) were determined. It was found that the standard deviations would be smaller in future years, from 276 GW h in 1979–2008 to about 234 GW h and 238 GW h in 2009–2100 for low and medium forcing, respectively. To better reflect the significance and magnitude of seasonal variations, standard deviation was normalised by its corresponding mean monthly air conditioning consumption, and a summary of the average values during the 1979–2008, 2009–2038, 2039– 2068, 2069–2100 and 2009–2100 periods is shown in Fig. 7. It is interesting to see that the normalised standard deviation would be reduced steadily through the different 30-year periods, from 43.8% to 30.7% for both emissions scenarios. This suggests that there would be less seasonal variations in the electricity consumption for air conditioning in the commercial sector in future years as a result of possible climate change. The 2008 regression model showed an over-estimation of 0.89 GW h, 0.14% of the 2008 electricity use for air conditioning (see Table 3). It is envisaged that predicted energy consumption based on the regression model used would tend to have biases with similar magnitude (in percentage terms), and the results should, therefore, be interpreted in such context. The long-term trends shown in Fig. 5, however, would not be significantly affected. 6. Conclusions There had been a sixfold increase in electricity use in the commercial sector (office, restaurant, retail, hotel, education, health, storage and other miscellaneous commercial or public services) during the 30-year period from 1979 to 2008. Air conditioning accounted for about 30% of the total sector-wide consumption. A new climatic index Z was determined using principal component analysis of key meteorological variables. Regression analysis of monthly electricity use for air conditioning in the entire commercial sector and the corresponding Z showed an increasing trend of air conditioning consumption during 1979–2008, probably due to warmer weather in recent years. Monthly mean dry-bulb temperature, moisture content and global solar radiation predictions from a general circulation model (MIROC3.2-H) were used to determine principal component Z and hence air conditioning consumption for
future years (2009–2100) based on two emissions scenarios SRES B1 (low forcing) and SRES A1B (medium forcing). All the consumption determined was based on the 2008 air conditioning consumption. For low forcing, average consumption in 2009–2038, 2039– 2068, 2069–2100 and 2009–2100 would be 5.7%, 12.8%, 18.4% and 12.5% more than the 1979–2008 average, respectively. Medium forcing showed a similar increasing trend, but 1–4% more than the low forcing scenario. It was found that the standard deviations of the monthly air conditioning consumption would be smaller in future years, from 276 GW h in 1979–2008 to about 234 GW h and 238 GW h in 2009–2100 for low and medium forcing, respectively, suggesting possible reduction in seasonal variation. It is hoped that these findings would give building professionals, and energy/environmental policy makers an idea about the likely increase in air conditioning consumption due to climate change in future years, and help them to consider energy efficiency measures required (e.g. tighter building energy code, raising the energy efficiency of equipment currently adopted in air conditioning and refrigeration systems) that could alleviate the impact on the environment. Although this study only considered subtropical Hong Kong, we believe the technique developed could be applied to other locations with similar or different climates. Acknowledgements T.N.T. Lam and K.K.W. Wan were supported by City University of Hong Kong Studentships. Measured weather data were obtained from the Hong Kong Observatory of the Hong Kong SAR. We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. References [1] Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, et al., editors. Climate change 2007: the physical science basis, contribution of the working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press; 2007. [2] Hong Kong Energy Statistics Annual Report. Census and Statistics Department, Hong Kong SAR, China; 1979–2008.
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