- Email: [email protected]
Generation of typical weather years with identified standard skies for Hong Kong
Building and Environment 56 (2012) 321e328
Contents lists available at SciVerse ScienceDirect
Building and Environment journal homepage: www.elsevier.com/locate/buildenv
Generation of typical weather years with identified standard skies for Hong Kong S.L. Wong*, Kevin K.W. Wan, Danny H.W. Li, Joseph C. Lam Building Energy Research Group, Department of Civil and Architectural Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 November 2011 Received in revised form 2 April 2012 Accepted 3 April 2012
In subtropical Hong Kong, buildings consume the most of the electricity use. Computer simulation technique is a useful tool for predicting building thermal and energy performance. Simulation computer programs require weather data input to drive the mathematical models within the simulation tools for comparative studies and annual energy estimation. Typical weather year data representing the long-term climatic conditions are often used. Recently, there has been an increasing interest in incorporating daylight in the architectural and building designs to reduce building energy consumption. However, daylight parameters are not always available. In 2003, the International Commission on Illumination (CIE) adopted a range of 15 standard skies covering probably the whole spectrum of the usual skies in the world. Once the skies have been identified, the basic solar irradiance and daylight illuminance at various inclined planes can be obtained. This paper presents the work on the development and evaluation of two typical weather datasets, namely Typical Meteorological Year (TMY) and Test Reference Year (TRY). Measured hourly weather data for the 30-year period from 1979 to 2008 were analysed. Building energy simulations were conducted to evaluate the two weather datasets. Daylight parameters were derived using luminous efficacy approach and the sky conditions were categorized based on common climatic variables. Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved.
Keywords: Typical weather year Solar radiation Standard sky Daylight illuminance Dry-bulb temperature
1. Introduction Typical weather year data representing the long-term climatic conditions are essential for computer simulation studies and numerical analysis of building energy performances and daylighting schemes. Weather conditions vary from one year to another. There is a need to establish more comprehensive datasets on the basis of more recent and long period weather information for building system applications at acceptable risk levels. For comparative studies and annual building energy estimations, a yearly representative of the average climatic condition is often used [1e4]. The longer the period of records and the more recent the weather data are, the better and more representative the results will be. Weather data based on not less than a 30-year period are conservatively stable for building designs [5] and the typical weather dataset should be periodically reviewed to reflect the climatic trend and variation [6]. Solar radiation is an important climatic element for active solar conversion system analyses [7], passive energy-efficient building designs [8] and built environment studies [9]. Daylighting is an essential issue in modern architecture. * Corresponding author. Tel.: þ86 852 34424312; fax: þ86 852 34420425. E-mail address: [email protected] (S.L. Wong).
Reliable daylight data particularly on vertical surfaces facing various orientations are required for analyses and evaluations. The International Commission on Illumination (CIE) [10] has adopted a set of 15 standard skies covering the whole probable spectrum of skies in the world. A number of researchers have reported that the 15 CIE Standard General Skies provide a good overall framework for representing the actual sky conditions [11,12]. It indicates that all daytime sky conditions can be classified into various CIE Standard Skies [13,14]. Skies of a given category have the same well-defined sky luminance pattern [15] and complicated expressions that contain numerous parameters and coefficients to model the sky distributions are not required. In Hong Kong, hourly global radiation measurement was initiated in 1979 by the Hong Kong Observatory (HKO) [16] and hourly weather databases of more than 30 years are now available. There is very little information on developing a typical year for building energy and daylight analysis in terms of monthly and annual energy consumption predictions. This paper presents the work on the development and analysis of two typical weather datasets, namely Test Reference Year (TRY) [17] and Typical Meteorological Year (TMY) [18] containing identified standard skies. Measured weather parameters for the 30-year period from 1979 to 2008 were analysed. The building energy and daylighting implications are discussed.
0360-1323/$ e see front matter Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2012.04.003
322
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
2. Selection procedures for TMY and TRY
2.2. TMY selection procedure
There are various types of typical year databases for simulating building energy usage. The common typical years are the ASHRAE TRY [17] and the TMY [18]. The TRY data are taken from an actual past year of weather data measurements. It consists of hourly readings for a selected year that is sufficient for comparative building energy studies [19]. However, the TRY reaches a ‘mild’ condition which may not be considered good enough to represent the prevailing ‘average’ weather conditions over a long-term period. The TMY method developed by Sandia National Laboratories in the United States is one of the commonly accepted methods for determining typical years. A TMY contains twelve typical meteorological months (TMMs) selected from different calendar months in a multi-year weather database. The January of a particular year may be selected as the first TMM, the February of another year as the second TMM and so on. Accordingly, all the 12 TMMs will then be selected and combined to form the TMY. The TMY method tends to be more widely used in building energy simulations compared with the TRY method.
The generation of TMY is based on the statistical analysis which evaluates the nine climatic indices, namely daily maximum, minimum and mean dry bulb temperatures (DBT) and dew point temperatures (DPT), daily maximum and mean wind speed (WSP) and daily total global solar radiation (GSR). A summary of the nine daily indices with their weighting factors is shown in Table 3. Selection of each TMM involves minimizing the difference between the month being considered and the long-term distributions. For each TMM, a screening process is first performed to select five candidate years. A nonparametric method, known as FinkelsteinSchafer (FS) statistics [20] is used to determine the candidates by comparing the yearly cumulative distribution function (CDF) with the long-term CDF in the month concerned. An empirical CDF, which is a monotonic increasing function, is defined as follows [21]:
2.1. TRY selection and dataset for Hong Kong The principle of the selection procedure is to eliminate those years in the period of record containing months with extremely high or low air temperatures until only one year remains. Extreme months are arranged in order of importance for energy comparisons. Hot Julys and cold Januarys are assumed to be the most important. All months were ranked by alternating between warm half (May to October) and the cold half (November to April) of the year, as shown in the Table 1. The selection begins by marking the 24 extreme months according to the rankings. If more than one year remain without any marked months, elimination will continue with the next-to-hottest July, the next-tocoldest January, and so on, until one year is left without being marked (i.e. TRY). For generation of TRY weather dataset, measured monthly average dry bulb air temperatures for the 30 years were obtained and are summarized in Table 2. For a particular month, the 30 monthly air temperatures were ranked in descending order. Number one is for the highest temperature and number thirty is for the lowest temperature. If two or more months have the same mean temperature, they are given the same number and are marked together in the elimination process. After marking of the first 24 extreme months, it was found 15 years remaining without any marked months. The marking process was then repeated with the 2nd, 3rd, etc extremes until one year without any marked month, which was the Year 2000 in this study.
Table 1 Rankings in the ASHRAE TRY procedure. 1. Hottest July 2. Coldest January 3. Hottest August 4. Coldest February 5. Hottest June 6. Coldest December
7. Hottest 13. Coldest July September 8. Coldest March 14. Hottest January 9. Hottest May 15. Coldest August 10. Coldest 16. Hottest November February 11. Hottest 17. Coldest June October 12. Coldest April 18. Hottest December
19. Coldest September 20. Hottest March 21. Coldest May 22. Hottest November 23. Coldest October 24. Hottest April
8 9 for x < xð1Þ <0 = ðk 0:5Þ=n for xðkÞ x xðkþ1Þ Sn ðxÞ ¼ : ; 1 for x xðnÞ
(1)
where Sn(x) ¼ value of the cumulative distribution function at x; n ¼ total number of elements; k ¼ rank order number ¼ 1, 2, 3, .., n1. Values of the FS statistics are calculated for each of the daily indices using the following equation:
FS ¼
N 1 X di N
(2)
i¼1
where FS ¼ value of FS test statistics; di ¼ absolute difference between long-term CDF and the yearly CDF at x(i) value; N ¼ number of daily readings for that month. A weighting sum average (WS) or composite index is then computed for each year, and the five years with the smallest WS values are selected as the candidate years for the final selection. The WS value is given by
WS ¼
NI X
WFi FSi
(3)
i¼1
where NI ¼ number of indices; WFi ¼ weighting factor for the ith parameter; FSi ¼ FS test statistics calculated for the ith parameter. The final selection involves two steps. The first step checks the statistics associated with the mean daily DBT and daily total GSR, including the FS statistics and the deviations of the monthly mean and median from the long-term mean and median. The second step looks at the persistence in the mean daily DBT and daily total GSR by examining their run structure. Persistence is considered important for the design and analysis of solar systems, since in some cases, the distribution of a given year can be quite close to that of the longeterm, yet there can still be atypically long runs of cloudy or warm or cool days. The general principle is to select years with small FS values, small deviation and typical run structures, but there is no universal procedure or criteria. Various methods for the final selection have been proposed and used by different researchers. Some of them considered at the root mean square difference of GSR [22]; some assessed the persistence and the monthly mean and median; and some used the year with the lowest WS value as the TMM [23]. The hourly weather records of 30 years (1979e2008) were employed for TMY selection. The required nine weather indices were derived from the hourly measured data. By using Eq. (3) and the weighting factors listed in Table 3, the WS values for all months
Table 2 Measured monthly average dry bulb temperature ( C). Jan
Ranking Feb
Ranking Mar
Ranking Apr
Ranking May
Ranking Jun
Ranking Jul
Ranking Aug
Ranking Sep
Ranking Oct
Ranking Nov
Ranking Dec
Ranking
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
17.63 16.61 16.33 16.86 14.96 14.08 15.44 15.90 17.30 17.89 15.68 15.92 16.87 15.75 14.61 17.00 16.12 17.77 16.68 16.85 17.35 17.00 17.29 17.30 16.09 15.86 15.90 16.44 16.35 15.95
3 14 17 11 28 30 27 23 5 1 26 21 10 25 29 8 18 2 13 12 4 9 7 6 19 24 22 15 16 20
2 29 9 20 26 28 21 27 5 17 15 18 14 22 7 11 24 25 19 13 3 23 10 6 4 12 16 8 1 30
13 10 3 14 28 22 30 26 2 29 19 20 6 23 12 25 24 11 8 17 5 15 4 1 18 16 27 21 7 9
24 18 5 27 8 26 29 15 21 28 19 25 13 20 22 2 16 30 14 1 4 10 17 3 7 12 9 6 23 11
30 29 21 20 16 27 5 17 24 8 22 28 9 26 14 1 15 23 13 6 25 12 7 2 4 11 3 18 10 19
29 9 28 26 6 18 25 14 23 5 22 17 10 27 12 15 4 3 24 11 1 13 21 2 20 7 19 16 8 30
6 19 24 20 4 10 23 21 16 12 18 11 15 26 3 30 29 5 28 9 7 17 27 14 2 22 8 13 1 25
27 10 5 22 6 23 26 7 11 29 8 2 13 3 12 28 30 15 19 1 20 14 4 16 9 17 24 21 25 18
27 23 25 20 3 21 28 11 22 18 7 10 5 2 19 26 16 14 30 9 8 13 6 24 17 15 4 29 12 1
27 15 26 11 2 16 12 22 13 30 21 20 25 29 28 24 17 8 10 6 5 9 7 19 18 23 4 3 14 1
28 10 29 9 20 11 8 26 16 30 18 14 24 27 17 6 22 4 3 2 12 23 21 19 13 7 5 1 25 15
9 16 30 29 28 25 22 19 27 18 17 8 11 3 23 1 21 12 6 4 26 7j 13 15 20 5 24 14 2 10
18.85 13.97 17.61 15.95 14.76 14.52 15.77 14.70 18.28 16.29 16.61 16.18 17.10 15.40 18.02 17.28 15.11 14.90 16.15 17.20 18.66 15.36 17.59 18.19 18.47 17.20 16.47 17.67 19.51 13.32
19.32 19.78 20.63 19.17 17.05 18.04 16.73 17.62 21.34 16.83 18.55 18.55 20.29 17.96 19.38 17.81 17.93 19.71 20.06 19.06 20.45 19.15 20.55 21.49 19.04 19.09 17.37 18.48 20.24 19.98
21.70 22.14 24.21 20.94 23.17 21.38 20.51 22.59 21.93 20.83 22.02 21.45 22.77 21.94 21.80 24.70 22.51 20.43 22.75 24.82 24.27 23.08 22.36 24.60 23.93 22.90 23.09 23.93 21.80 23.05
24.40 24.56 25.16 25.30 25.96 24.61 26.85 25.95 24.99 26.64 25.14 24.58 26.54 24.79 26.05 27.40 25.97 25.04 26.07 26.76 24.88 26.10 26.69 27.02 26.86 26.28 26.97 25.82 26.40 25.34
26.87 28.41 26.88 27.16 28.57 27.90 27.22 27.99 27.46 28.63 27.53 27.92 28.37 27.15 28.30 27.97 28.68 28.76 27.33 28.31 28.89 28.29 27.60 28.82 27.65 28.56 27.76 27.96 28.44 26.67
29.20 28.71 28.40 28.57 29.44 29.01 28.41 28.48 28.85 28.98 28.84 29.00 28.85 28.22 29.45 27.91 27.98 29.25 28.10 29.03 29.15 28.85 28.13 28.87 29.56 28.42 29.08 28.96 29.64 28.36
27.86 28.79 29.11 28.29 28.99 28.24 28.01 28.98 28.64 27.83 28.87 29.53 28.56 29.35 28.61 27.85 27.37 28.50 28.37 29.55 28.33 28.51 29.18 28.44 28.84 28.43 28.04 28.31 28.02 28.39
27.06 27.27 27.16 27.53 28.35 27.28 26.82 27.79 27.27 27.55 28.11 27.80 28.13 28.85 27.55 27.07 27.62 27.68 26.41 27.82 27.83 27.68 28.12 27.17 27.56 27.63 28.21 26.59 27.73 28.97
24.73 25.46 24.76 25.78 26.39 25.32 25.77 24.95 25.69 24.38 25.07 25.15 24.82 24.56 24.65 24.92 25.29 25.97 25.90 26.15 26.20 25.96 26.08 25.15 25.26 24.92 26.20 26.36 25.59 26.49
20.38 22.27 20.31 22.27 21.48 22.26 22.44 20.46 21.79 19.88 21.53 22.14 20.99 20.41 21.69 22.91 21.21 23.01 23.15 23.17 22.20 21.01 21.47 21.52 22.20 22.62 22.97 23.25 20.89 21.94
18.55 18.15 16.45 16.72 16.76 17.02 17.18 17.70 16.82 17.71 17.82 18.79 18.42 19.19 17.11 19.78 17.36 18.34 18.90 19.11 16.84 18.85 18.25 18.17 17.61 19.07 17.04 18.20 19.27 18.43
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
Year
Notes: 1. Ranking (from the highest to the lowest) is shown in shaded column. 2. TRY is shown in highlighted row.
323
324
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
Table 3 Weather indices and weighting factors for TMY method. Index
Sandia’s TMY method
Dry-bulb temperature
Daily Daily Daily Daily Daily Daily Daily Daily Daily
Dew point temperature
Wind speed Global solar radiation
maximum minimum mean maximum minimum mean maximum mean total
1/24 1/24 2/24 1/24 1/24 2/24 2/24 2/24 12/24
of the 30-year period were determined and the results are listed in Table 4. As shown in the table, the five candidate years for each month with the lowest values of WS are underlined, whereas the lowest one is displayed in bold. Accordingly, the 12 calendar months were selected based on the lowest WS values exhibited in the table. The 12 selected TMMs in TMY were used to form weather input files for computer simulations. Apart from the nine weather indices, other weather parameters such as atmospheric pressure, wind direction, hours of sunshine and cloud cover which can be obtained from the HKO, were also adopted to create the weather files. The direct and diffuse components of solar radiation were determined from their corresponding global solar radiation using the hybrid model established in our previous study [24]. Smoothing of the weather data for discontinuities was carried out using the cubic spline function to avoid abrupt changes at the boundary between two adjacent months from different years. The last 6 h of the preceding day and the first 6 h of the following day were adjusted accordingly [25]. 3. Building energy prediction test To get some idea about the effects of weather data from different years and to assess how close the monthly and annual energy
consumption predicted from the developed TRY and TMY weather files to that predicted from the long-term weather data would be, a series of computer simulations were performed. The building energy simulation program Energy Plus was employed. A generic commercial building was developed to serve as a baseline reference for comparison and evaluation. The base-case office building used was a square (35 35 m2) 40-storey office block with curtain wall designs and a centralized HVAC system. The floor-to-floor and window heights were 3.5 m and 1.5 m respectively. This represented a window-to-wall ratio (WWR) of 43%. Glazing was single reflective glass with a shading coefficient (SC) and a light transmittance (LT) of 0.4 and 0.3, respectively. The U-values for the roof, external walls and windows were 0.46, 1.95 and 5.6W/m2 C, respectively. The air-conditioning plant was a variable-air-volume system with a temperature set point of 24 C in summer and 21 C in winter [26]. The chillers were of a packaged air-cooled hermetic reciprocating type with a coefficient of performance of 2.8. The key variables are summarized in Table 5. Fig. 1 shows the 30 monthly electricity consumption profiles for the individual years from 1979 to 2008. It can be seen that all the years show similar seasonal variations in the electricity use. Electricity consumption peaks during the six hot summer months from May to October, when the average outdoor air temperature exceeds 25 C. The minimum and maximum monthly differences between the thirty years were simulated to be 32 and 102 MWh, respectively. These represented 3.8 and 12.2% of the long-term mean monthly electricity use of 833 MWh. On annual basis, the largest difference between the 30 years was computed to be 289 MWh accounting for 2.9% of the long-term mean annual electricity consumption. However, the larger discrepancies were found on monthly basis between individual years and the long-term monthly electricity consumption, ranging from 2.8% to 6.3%. It reveals that a random year weather year data cannot predict monthly energy use accurately. Predictions from the TMY and TRY (i.e. the year of 2000) weather files were analysed and compared
Table 4 Weighted sum averages of the FS statistics for each month using TMY method.
2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0.0841 0.0845 0.0752 0.0596 0.0443 0.1022 0.0787 0.0814 0.0642 0.0527 0.1198 0.0688 0.0785 0.0538 0.0761 0.0932 0.1324 0.0890 0.1121 0.0945 0.1294 0.1160 0.1761 0.0855 0.1214 0.1468 0.1114 0.1143 0.0653 0.1301
0.1647 0.1573 0.0938 0.1249 0.1173 0.1322 0.1373 0.1062 0.0596 0.1605 0.0900 0.0645 0.0917 0.0935 0.1266 0.1321 0.1091 0.0918 0.0799 0.1233 0.0753 0.1274 0.1026 0.1653 0.1354 0.2309 0.1165 0.1010 0.1143 0.1291
0.1578 0.0930 0.0618 0.0839 0.0643 0.0492 0.1289 0.1364 0.1208 0.0703 0.0503 0.0886 0.0494 0.0835 0.1044 0.0632 0.1045 0.0887 0.0940 0.1239 0.1478 0.1271 0.0963 0.1114 0.0967 0.2052 0.0819 0.0873 0.0623 0.1212
0.0641 0.1096 0.1113 0.0822 0.0701 0.0745 0.1565 0.0607 0.0616 0.1179 0.1272 0.0760 0.1166 0.0796 0.1591 0.0753 0.0755 0.0759 0.1108 0.0961 0.1122 0.0732 0.0585 0.1052 0.1600 0.1085 0.1143 0.1057 0.0529 0.0592
0.0693 0.1070 0.0747 0.1587 0.0869 0.0756 0.0926 0.0830 0.0689 0.0742 0.0780 0.0563 0.0785 0.0614 0.1258 0.0629 0.1097 0.0971 0.0992 0.0764 0.0963 0.1023 0.0585 0.1261 0.1027 0.0970 0.0893 0.1013 0.1002 0.1057
0.1818 0.1117 0.1075 0.1286 0.1070 0.1132 0.0851 0.1004 0.1288 0.1036 0.1414 0.0990 0.0995 0.0970 0.1086 0.0990 0.0584 0.0581 0.0580 0.0959 0.0986 0.0691 0.0611 0.1275 0.0942 0.1007 0.1129 0.1074 0.1094 0.1001
0.0832 0.1755 0.1107 0.0768 0.0993 0.1528 0.1411 0.1245 0.0573 0.1165 0.0865 0.1381 0.0552 0.0721 0.1673 0.1034 0.0709 0.0919 0.0570 0.0670 0.0883 0.0766 0.0502 0.1113 0.1747 0.0850 0.0906 0.1250 0.0732 0.1778
0.1277 0.0860 0.0547 0.0608 0.0830 0.0827 0.0471 0.0874 0.0830 0.0972 0.1189 0.1000 0.0612 0.1463 0.1224 0.0553 0.0872 0.0641 0.1090 0.0637 0.0969 0.1257 0.0641 0.0612 0.0742 0.0767 0.0650 0.0951 0.0904 0.0996
0.1398 0.0740 0.0919 0.0787 0.0830 0.0572 0.0764 0.0877 0.1304 0.0635 0.0602 0.1156 0.0568 0.0645 0.1357 0.0642 0.1156 0.0729 0.0612 0.0535 0.0904 0.0609 0.0975 0.0902 0.0765 0.0905 0.0483 0.1120 0.0943 0.1039
0.0992 0.0569 0.1647 0.0811 0.1707 0.0895 0.1292 0.0898 0.0922 0.0692 0.0567 0.1356 0.0551 0.1062 0.1182 0.0892 0.1556 0.0800 0.1198 0.0696 0.1018 0.0700 0.0726 0.0630 0.0516 0.1615 0.0811 0.0737 0.0621 0.3408
0.1423 0.1694 0.1646 0.1051 0.0709 0.0792 0.0687 0.0998 0.1046 0.0776 0.0980 0.0787 0.0684 0.0769 0.1498 0.1117 0.1435 0.0786 0.0875 0.0432 0.1270 0.1449 0.0680 0.0644 0.0560 0.1647 0.1356 0.1254 0.1362 0.1237
0.1095 0.1093 0.0881 0.0819 0.1049 0.1403 0.1231 0.0818 0.0725 0.0910 0.0758 0.1449 0.0724 0.1058 0.1998 0.0649 0.1172 0.1309 0.1011 0.0802 0.1052 0.0974 0.0924 0.0683 0.1055 0.0786 0.0834 0.1507 0.1102 0.1040
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
325
Table 5 Brief description of base case office building. Location: Hong Kong (latitude 22.3 N, longitude 114.2 E) Building type and Office building, 40 storeys above ground storeys: Floor areas: Total gross floor area ¼ 49,000 m2 Air-conditioned area ¼ 41,160 m2 Dimensions and 35 m 35 m (square); floor-to-floor ¼ 3.5 m; window heights: height ¼ 1.5 m; window-to-wall ratio ¼ 0.43 Constructions of building envelope: (a) External walls (spandrel portion of curtain wall) - 6 mm glass þ 25 mm airspace þ 19 mm plywood þ wall paper (U-value ¼ 1.95 W/m2 C) (b) Roof - 13 mm slag þ 10 mm roof build-up þ 50 mm roof insulation þ 200 mm n.w. concrete þ ceiling void þ 19 mm ceiling panel (U-value ¼ 0.46 W/m2 C) (c) Windows - 6 mm reflective single glazing (SC ¼ 0.4, LT ¼ 0.3, Uvalue ¼ 5.6 W/m2 C) Operating hours: Mon. to Fri.: 08:00e18:00; Sat.: 08:00e13:00; Sun. closed HVAC design Occupancy density ¼ 8 m2/person parameters: Lighting load ¼ 20 W/m2; equipment load ¼ 18 W/m2 Infiltration ¼ 0.6 air change per hour during fans OFF Space design temperature ¼ 24 C
with those from the 30 years. Fig. 2 shows that predictions from the two weather files appear within the maximum and minimum range of predictions from the thirty individual years. In general, they follow quite closely the 30-year long-term mean. The performance of the typical weather datasets were further examined by mean-bias deviation (MBD) and root-mean-square deviation (RMSD) as follows:
P MBD ¼
ðENmonth ENmean Þ 12
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðENmonth ENmean Þ2 RMSD ¼ 12
(4)
(5)
where ENmonth is the building energy use for each month (MWh); ENmean is the long-term monthly mean building energy use (MWh). Accordingly, the MBD and RMSD were calculated and Fig. 3 shows the results for the two weather datasets compared with the long-term monthly mean consumption. The MBD for the TMY and TRY are 4.9 and 2.4 MWh, respectively. These represent around 0.6% and 0.3% of the long-term average monthly electricity use of 833 MWh for TMY and TRY, respectively. The two small MBDs are a result of a fortuitous cancellation between over- and under-estimation. It can be seen that the TMY has an obvious smaller value of RMSD of 9.6 MWh accounting for only 1.2% of the long-term monthly mean consumption. The TRY has the RMSD of 20 MWh, denotes 2.4% of the long-term mean. The results show that TMY is an appropriate weather dataset to be selected for longterm energy analysis. A sensitivity analysis was carried out to examine the parametric weightings affecting annual electricity consumption by introducing an alternative weighting scheme, which was used by the International Weather for Energy Calculations (IWEC) [27]. Larger total weighting factor of 0.4 are assigned to the dry bulb temperatures because the criterion affects the energy use of air-conditioning systems significantly. The weighting factor of global solar radiation is reduced to 0.4. Twelve calendar months were selected based on the lowest WS values and are shown in Table 6. The results showed that the years with the lowest WS values are the same between August and December and total eight identical TMMs were selected. In total, forty-one out of sixty underlined WS values are the same. The statistical results suggest that the month selection using the two approaches often gives similar findings. The simulated results showed that the MBD and the RMSD of the IWEC
Fig. 1. Predicted monthly electricity use for the base-case building in (a) 1979e1988, (b) 1989e1998, and (c) 1999 to 2008.
Fig. 2. Predicted monthly electricity use for the base-case building based on long-term mean, TMY and TRY.
326
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
Fig. 3. Mean-bias and root-mean-square deviations for the TMY and TRY with respect to long-term mean.
Fig. 4. Cumulative frequency curves of global, sky-diffuse and direct-beam illuminance.
is 4.8 MWh and 8.3 MWh, respectively, compared with the longterm monthly mean consumption, which are comparable to the TMY scheme. It implies that using TMY approach with different weighting schemes can also find an appropriate weather dataset for long-term energy analysis.
applications, sky-diffuse illuminance is more important and widely used and direct sunlight is often excluded. Problems of glare, excessive brightness ratios and thermal discomfort have support the exclusion [31]. Because of higher luminous efficacy, sky-diffuse illuminance is more energy-efficient in daylighting design. However, direct sunlight can give a sense of excitement, cheerfulness, warmth and psychological well being in occupants’ living and working environments [32]. People may desire to have sunshine when the overheating and glare effects are of accepting levels. Generally, high illuminance values are often dominated by BV and DV is more than BV at low illuminance levels. The cumulative frequency for BV is less than that for DV at 15 klux and vice-versa. Also, no BV and DV of up to 25 klux are observed at around 40% of daytime period which corresponds to overcast skies and conditions near sunrise and sunset. The maximum values for BV, DV and GV are 74, 46 and 105 klux, respectively. Recently, the CIE adopted a list of 15 standard skies. Each sky standard has its own well-defined sky luminance pattern which is the straight-forward approach for sky classification and can help the determination of daylight illuminance on inclined surfaces facing various orientations. In practice, the important issue would be the frequency with which these individual standard skies appear at a given location. However, luminance distributions for the whole sky vault are less assessable and sky conditions categorized using climatic parameters which are widely obtainable would be more appropriate. As reported by Kittler et al. [15], the ratio of zenith luminance to horizontal sky-diffuse illuminance (Lz/Dv) can characterize the momentary sky brightness and theoretically can represent the pattern of the 15 CIE Standard General Skies. The Lz/Dv theoretical curves for the 15 standard skies are not parallel but they intersect with each other at as of 35 or more. This could introduce errors in sky categorization for a place where solar altitudes often exceed 35 [33,34]. Previously, we used the 15 Lz/Dv-as theoretical curves, Gv/Ev, Dv/Gv and luminous turbidity (Tv) for classifying the 15 CIE skies [13]. The general equation for Tv is given as:
4. Daylight data and standard skies for TMY and TRY Daylighting is an important strategy in displacing the need for high grade energy (electricity) used for artificial lighting. The first step towards designing a building to utilize daylight for illuminating its interior is to acquire information on the amount of daylight available. An accurate estimation of the available daylight is to acquire, not just the total amount of light coming from the sky, but also the distributions of luminance across the sky vault [28]. For successful daylighting analysis, there is a potential need to have a typical weather dataset containing daylight data and classified CIE General Standard Skies. The horizontal global solar radiation data have been measuring by the HKO as part of their routine work but no outdoor illuminance measurement. For estimating daylight illuminance, the common approach has been the derivation of illuminance from measured solar irradiance using the luminous efficacy approach. The following mathematical expressions were used to compute the direct-beam (KB) and sky-diffuse (KD) luminous efficacies on a horizontal surface [29,30].
KB ¼ 59:15 þ 1:12aS 0:0061a2S ðlm=WÞ
(6)
KD ¼ 130:6 14:4Cðlm=WÞ
(7)
where aS is the solar altitude (degrees); C is the cloud fraction Using Eqs. (6) and (7), the direct-beam (BV) and sky-diffuse (Dv) daylight illuminance can be calculated from the corresponding solar irradiance data and the horizontal global daylight illuminance (GV) is the sum of the computed BV and DV. The BV, DV and GV for the TMY were determined and their cumulative frequency curves are displayed in Fig. 4. The cumulative frequency distribution of outdoor illuminance can indicate the percentage of the working year in which a given illuminance is exceeded. For daylighting
Tv ¼
ln ðEv =Bv Þ av mv
(8)
Table 6 Twelve selected calendar months for TMY and IWEC schemes. Month
1
2
3
4
5
6
7
8
9
10
11
12
TMY IWEC
2004 1995
2000 1988
2003 2003
1980 1986
1997 1997
1990 1990
1986 2000
2002 2002
1982 1982
1984 1984
1989 1989
1993 1993
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
327
the TMY and TRY were in good agreements with the 30-year (1979e2008) long-term prediction. The root-mean-square deviation (RMSD) for TMY and TRY were 1.2 and 2.4%, respectively. It is envisaged that both TMY and TRY can give a good indication of the prevailing energy performance in building energy simulation exercise in Hong Kong. Further studies were carried out to generate daylight variables and to identify various daytime periods into the 15 CIE Standard General Skies based on these generated daylight parameters. For sky categorization, the frequency of occurrence pattern however was not quite similar to that using measured daylight data. Subsequent analysis may be required to modify the models for predicting daylight variables.
Acknowledgments Fig. 5. Frequency of occurrence of the classified 15 CIE general standard skies for Hong Kong TMY and TRY.
where Ev is the extraterrestrial illuminance (lux); av is the 1 luminous ideal extinction ¼ [35]; mv is the optical 9:9 þ 0:043mv 1 [36]. air mass ¼ sin aS þ 0:50572ðaS þ 6:07995Þ1:6364 The LZ data were estimated from the Perez et al. model [37]. The predicted hourly daylight illumiance and LZ data for the both TMY and TRY were used for the categorization of the CIE Standard General Skies based on our proposed approach [13]. Fig. 5 presents the frequency distributions for the 15 CIE Standard Skies. For the TMY, even though large variations can be observed for individual standard skies, a thorough examination of the figure revealed that the three main sky types (i.e. overcast e sky nos. 1 to 5; partly cloudy e sky nos. 6 to 10; and clear e sky nos. 11e15) are quite evenly distributed in Hong Kong under this classification method. Skies 1, 7, 9, 13 and 15 occur most frequently. Sky type 1 dominates the subset of overcast skies representing 13% of the Hong Kong sky conditions. Sky types 7 and 9 are the main sky patterns for the partly cloudy skies with the frequency of occurrence of around 10%. Referring to clear sky conditions, sky types 13 and 15 have the highest frequency of occurrence accounting for 11 and 16%, respectively. The overall result of the 15 sky classifications between TRY and TMY is quite consistent. However, the clear sky dominated for the TRY accounting for 45% of the frequency of occurrence in Hong Kong. The sky 13 showed the highest frequency of occurrence of 17.6% in the TRY sky classification. The frequency of occurrence pattern however was not generally similar to our previous study [12]. The discrepancy may be due to the fact that modeled rather than measured daylight variables were used for the sky classification. 5. Conclusions Typical weather database representative of the prevailing weather conditions is a key element in the building energy simulation process. Weather data of not less than a 30-year period are conservatively stable for building designs and the typical weather dataset should be periodically reviewed to reflect the climatic trend and variation. Based on hourly weather data collected between 1979 and 2008 (30 years) and the selection procedures, a typical meteorological year (TMY) and a typical reference year (TRY) for Hong Kong were established. The performance of the two weather files were conducted via building energy simulations. It was found that the monthly electricity consumption profiles simulated from
The work described in this paper was fully supported by a grant from the Central Policy Unit of the Government of Hong Kong Special Administrative Region and the Research Grants Council of the Special Administrative Region, China (Project No. CityU1011PPR-10). The authors thank Mr. Thomas W. K. Wong for his help with data analysis.
References [1] Lee K, Yoo H, Levermore GJ. Generation of typical weather data using the ISO Test Reference Year (TRY) method for major cities of South Korea. Building Environ 2010;45(4):956e63. [2] Yang L, Wan KKW, Li DHW, Lam JC. A new method to develop typical weather years in different climates for building energy use studies. Energy 2011; 36(10):6121e9. [3] Chan ALS. Developing a modified typical meteorological year weather file for Hong Kong taking into account the urban heat island effect. Building Environ 2011;46(12):2434e41. [4] Bhandari M, Shrestha S, New J. Evaluation of weather datasets for building energy simulation. Energ Build, in press, Corrected proof, Available online 10 February 2012. [5] Crow LW. Development of hourly data for weather year for energy calculations (WYEC), including solar data, at 21 stations throughout the U.S. ASHRAE Trans 1981;87(Part 1):896e906. [6] Chow TT, Chan ALS, Fong KF, Lin Z. Some perceptions on typical weather year e from the observations of Hong Kong and Macau. Solar Energy 2006;80(4): 459e67. [7] Li DHW, Cheung GHW, Lam JC. Analysis of the operational performance and efficiency characteristic for photovoltaic system in Hong Kong. Energy Conversion Manag 2005;46(7,8):1107e18. [8] Li DHW, Lam TNT. An analysis of building energy performances and benefits using solar facades. Proc Inst Mech Engineers Part A J Power Energy 2008; 222(A3):299e308. [9] Li DHW, Lam JC. An analysis of climatic parameters and sky condition classification. Building Environ 2001;36(4):435e45. [10] CIE, S 011/E. Spatial distribution of daylight e CIE standard general sky. Vienna: Standard, CIE Central Bureau; 2003. [11] Tregenza PR. Standard skies for maritime climates. Lighting Res Technol 1999; 31(3):97e106. [12] Li DHW, Lau CCS, Lam JC. A study of 15 sky luminance patterns against Hong Kong data. Architectural Sci Rev 2003;46(1):61e8. [13] Li DHW, Tang HL. Standard skies classification in Hong Kong. J Atmos SolarTerrestrial Phys 2008;70(8e9):1222e30. [14] Li DHW, Cheung KL, Tang HL, Cheng CCK. Identifying CIE Standard Skies using vertical sky component. J Atmos Solar-Terrestrial Phys 2011;73(13): 1861e7. [15] Kittler R, Darula S, Perez R. A set of standard skies. Bratislava: Polygrafia; 1998. [16] Hong Kong Observatory. Surface observations in Hong Kong, annual publications, Hong Kong Observatory, Hong Kong SAR, China (annual publication). [17] ASHRAE Handbook e Fundamentals. American Society of Heating. Atlanta: Refrigerating and Air Conditioning Engineers, Inc.; 2005. [18] Hall IJ, Prairie RR, Anderson HE, Boes EC. Generation of a typical meteorological year. Proceedings of the 1978 Annual Meeting of the American Section of the International Solar Energy Society, Denver; 1978. pp. 669e671. [19] Hui SCM, Lam JC. Test reference year (TRY) for comparative energy study. J Hong Kong Inst Engineers; 1992, February:13e6. [20] Finkelstein JM, Schafer RE. Improved goodness-of-fit tests. Biometrika 1971; 58(3):641e5. [21] Conover WJ. Practical nonparametric statistics. 2nd ed. New York: John Wiley & Sons; 1980.
328
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
[22] Skeiker K. Generation of a typical meteorological year for Damascus zone using the Finkelstein-Schafer statistical method. Energy Conversion Manag 2004;45(1):99e112. [23] Yang L, Lam JC, Liu JP. Analysis of typical meteorological years in different climates of China. Energy Conversion Manag 2007;48(2):654e68. [24] Lam JC, Li DHW. Correlation between global solar radiation and its direct and diffuse components. Building Environ 1996;31(6):527e55. [25] Marion W, Urban K. User’s manual for TMY2s. Golden, Colo: National Renewable Energy Laboratory; 1995. [26] Electrical and Mechanical Services Department. Guidelines on energy efficiency of lighting installations. Hong Kong: Electrical and Mechanical Services Department; 1998. [27] ASHRAE. International weather for energy calculations (IWEC weather files) User’s Manual and CD-ROM. Atlanta, GA: American Society of Heating, Refrigerating and Air- Conditioning Engineers, Inc.; 2001. [28] Li DHW, Cheung GHW, Cheung KL. Evaluation of simplified procedure for indoor daylight illuminance determination against data in scale model measurements. Indoor Built Environ 2006;15(3):213e23. [29] Lam JC, Li DHW. Luminous efficiency of daylight under different sky conditions. Energy Conversion Manag 1996;37(12):1703e11.
[30] Li DHW, Lam TNT, Cheung KL, Tang HL. An analysis of luminous efficacies under the CIE standard skies. Renewable Energy 2008;33(11): 2357e65. [31] Lam JC, Li DHW. Day lighting and energy analysis for air-conditioning office building. Energy 1998;23(2):79e89. [32] Cheung HD, Chung TM. Analyzing sunlight duration and optimum shading using a sky map. Building Environ 2007;42(9):3138e48. [33] Bartzokas A, Darula S, Kambezidis HD, Kittler R. Sky luminance distribution in Central Europe and the Mediterranean area during the winter period. J Atmos Solar-Terrestrial Phys 2003;65(1):113e9. [34] Bartzokas A, Kambezidis HD, Darula S, Kittler R. Comparison between winter and summer sky-luminance distribution in Central Europe and in the Eastern Mediterranean. J Atmos Solar-Terrestrial Phys 2005;67(7):709e18. [35] Navvab M, Karayel M, Ne’eman E, Selkovitz S. Analysis of atmospheric turbidity for daylight calculations. Energ Build 1984;6(2e4):293e303. [36] Kasten F, Young AT. Revised optical air mass tables and approximation formula. Appl Opt 1989;28(22):4735e8. [37] Perez R, Ineichen P, Seals R. Modeling daylight availability and irradiance components from direct and global irradiance. Solar Energy 1990;44(5): 271e89.
Contents lists available at SciVerse ScienceDirect
Building and Environment journal homepage: www.elsevier.com/locate/buildenv
Generation of typical weather years with identified standard skies for Hong Kong S.L. Wong*, Kevin K.W. Wan, Danny H.W. Li, Joseph C. Lam Building Energy Research Group, Department of Civil and Architectural Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 November 2011 Received in revised form 2 April 2012 Accepted 3 April 2012
In subtropical Hong Kong, buildings consume the most of the electricity use. Computer simulation technique is a useful tool for predicting building thermal and energy performance. Simulation computer programs require weather data input to drive the mathematical models within the simulation tools for comparative studies and annual energy estimation. Typical weather year data representing the long-term climatic conditions are often used. Recently, there has been an increasing interest in incorporating daylight in the architectural and building designs to reduce building energy consumption. However, daylight parameters are not always available. In 2003, the International Commission on Illumination (CIE) adopted a range of 15 standard skies covering probably the whole spectrum of the usual skies in the world. Once the skies have been identified, the basic solar irradiance and daylight illuminance at various inclined planes can be obtained. This paper presents the work on the development and evaluation of two typical weather datasets, namely Typical Meteorological Year (TMY) and Test Reference Year (TRY). Measured hourly weather data for the 30-year period from 1979 to 2008 were analysed. Building energy simulations were conducted to evaluate the two weather datasets. Daylight parameters were derived using luminous efficacy approach and the sky conditions were categorized based on common climatic variables. Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved.
Keywords: Typical weather year Solar radiation Standard sky Daylight illuminance Dry-bulb temperature
1. Introduction Typical weather year data representing the long-term climatic conditions are essential for computer simulation studies and numerical analysis of building energy performances and daylighting schemes. Weather conditions vary from one year to another. There is a need to establish more comprehensive datasets on the basis of more recent and long period weather information for building system applications at acceptable risk levels. For comparative studies and annual building energy estimations, a yearly representative of the average climatic condition is often used [1e4]. The longer the period of records and the more recent the weather data are, the better and more representative the results will be. Weather data based on not less than a 30-year period are conservatively stable for building designs [5] and the typical weather dataset should be periodically reviewed to reflect the climatic trend and variation [6]. Solar radiation is an important climatic element for active solar conversion system analyses [7], passive energy-efficient building designs [8] and built environment studies [9]. Daylighting is an essential issue in modern architecture. * Corresponding author. Tel.: þ86 852 34424312; fax: þ86 852 34420425. E-mail address: [email protected] (S.L. Wong).
Reliable daylight data particularly on vertical surfaces facing various orientations are required for analyses and evaluations. The International Commission on Illumination (CIE) [10] has adopted a set of 15 standard skies covering the whole probable spectrum of skies in the world. A number of researchers have reported that the 15 CIE Standard General Skies provide a good overall framework for representing the actual sky conditions [11,12]. It indicates that all daytime sky conditions can be classified into various CIE Standard Skies [13,14]. Skies of a given category have the same well-defined sky luminance pattern [15] and complicated expressions that contain numerous parameters and coefficients to model the sky distributions are not required. In Hong Kong, hourly global radiation measurement was initiated in 1979 by the Hong Kong Observatory (HKO) [16] and hourly weather databases of more than 30 years are now available. There is very little information on developing a typical year for building energy and daylight analysis in terms of monthly and annual energy consumption predictions. This paper presents the work on the development and analysis of two typical weather datasets, namely Test Reference Year (TRY) [17] and Typical Meteorological Year (TMY) [18] containing identified standard skies. Measured weather parameters for the 30-year period from 1979 to 2008 were analysed. The building energy and daylighting implications are discussed.
0360-1323/$ e see front matter Crown Copyright Ó 2012 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.buildenv.2012.04.003
322
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
2. Selection procedures for TMY and TRY
2.2. TMY selection procedure
There are various types of typical year databases for simulating building energy usage. The common typical years are the ASHRAE TRY [17] and the TMY [18]. The TRY data are taken from an actual past year of weather data measurements. It consists of hourly readings for a selected year that is sufficient for comparative building energy studies [19]. However, the TRY reaches a ‘mild’ condition which may not be considered good enough to represent the prevailing ‘average’ weather conditions over a long-term period. The TMY method developed by Sandia National Laboratories in the United States is one of the commonly accepted methods for determining typical years. A TMY contains twelve typical meteorological months (TMMs) selected from different calendar months in a multi-year weather database. The January of a particular year may be selected as the first TMM, the February of another year as the second TMM and so on. Accordingly, all the 12 TMMs will then be selected and combined to form the TMY. The TMY method tends to be more widely used in building energy simulations compared with the TRY method.
The generation of TMY is based on the statistical analysis which evaluates the nine climatic indices, namely daily maximum, minimum and mean dry bulb temperatures (DBT) and dew point temperatures (DPT), daily maximum and mean wind speed (WSP) and daily total global solar radiation (GSR). A summary of the nine daily indices with their weighting factors is shown in Table 3. Selection of each TMM involves minimizing the difference between the month being considered and the long-term distributions. For each TMM, a screening process is first performed to select five candidate years. A nonparametric method, known as FinkelsteinSchafer (FS) statistics [20] is used to determine the candidates by comparing the yearly cumulative distribution function (CDF) with the long-term CDF in the month concerned. An empirical CDF, which is a monotonic increasing function, is defined as follows [21]:
2.1. TRY selection and dataset for Hong Kong The principle of the selection procedure is to eliminate those years in the period of record containing months with extremely high or low air temperatures until only one year remains. Extreme months are arranged in order of importance for energy comparisons. Hot Julys and cold Januarys are assumed to be the most important. All months were ranked by alternating between warm half (May to October) and the cold half (November to April) of the year, as shown in the Table 1. The selection begins by marking the 24 extreme months according to the rankings. If more than one year remain without any marked months, elimination will continue with the next-to-hottest July, the next-tocoldest January, and so on, until one year is left without being marked (i.e. TRY). For generation of TRY weather dataset, measured monthly average dry bulb air temperatures for the 30 years were obtained and are summarized in Table 2. For a particular month, the 30 monthly air temperatures were ranked in descending order. Number one is for the highest temperature and number thirty is for the lowest temperature. If two or more months have the same mean temperature, they are given the same number and are marked together in the elimination process. After marking of the first 24 extreme months, it was found 15 years remaining without any marked months. The marking process was then repeated with the 2nd, 3rd, etc extremes until one year without any marked month, which was the Year 2000 in this study.
Table 1 Rankings in the ASHRAE TRY procedure. 1. Hottest July 2. Coldest January 3. Hottest August 4. Coldest February 5. Hottest June 6. Coldest December
7. Hottest 13. Coldest July September 8. Coldest March 14. Hottest January 9. Hottest May 15. Coldest August 10. Coldest 16. Hottest November February 11. Hottest 17. Coldest June October 12. Coldest April 18. Hottest December
19. Coldest September 20. Hottest March 21. Coldest May 22. Hottest November 23. Coldest October 24. Hottest April
8 9 for x < xð1Þ <0 = ðk 0:5Þ=n for xðkÞ x xðkþ1Þ Sn ðxÞ ¼ : ; 1 for x xðnÞ
(1)
where Sn(x) ¼ value of the cumulative distribution function at x; n ¼ total number of elements; k ¼ rank order number ¼ 1, 2, 3, .., n1. Values of the FS statistics are calculated for each of the daily indices using the following equation:
FS ¼
N 1 X di N
(2)
i¼1
where FS ¼ value of FS test statistics; di ¼ absolute difference between long-term CDF and the yearly CDF at x(i) value; N ¼ number of daily readings for that month. A weighting sum average (WS) or composite index is then computed for each year, and the five years with the smallest WS values are selected as the candidate years for the final selection. The WS value is given by
WS ¼
NI X
WFi FSi
(3)
i¼1
where NI ¼ number of indices; WFi ¼ weighting factor for the ith parameter; FSi ¼ FS test statistics calculated for the ith parameter. The final selection involves two steps. The first step checks the statistics associated with the mean daily DBT and daily total GSR, including the FS statistics and the deviations of the monthly mean and median from the long-term mean and median. The second step looks at the persistence in the mean daily DBT and daily total GSR by examining their run structure. Persistence is considered important for the design and analysis of solar systems, since in some cases, the distribution of a given year can be quite close to that of the longeterm, yet there can still be atypically long runs of cloudy or warm or cool days. The general principle is to select years with small FS values, small deviation and typical run structures, but there is no universal procedure or criteria. Various methods for the final selection have been proposed and used by different researchers. Some of them considered at the root mean square difference of GSR [22]; some assessed the persistence and the monthly mean and median; and some used the year with the lowest WS value as the TMM [23]. The hourly weather records of 30 years (1979e2008) were employed for TMY selection. The required nine weather indices were derived from the hourly measured data. By using Eq. (3) and the weighting factors listed in Table 3, the WS values for all months
Table 2 Measured monthly average dry bulb temperature ( C). Jan
Ranking Feb
Ranking Mar
Ranking Apr
Ranking May
Ranking Jun
Ranking Jul
Ranking Aug
Ranking Sep
Ranking Oct
Ranking Nov
Ranking Dec
Ranking
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
17.63 16.61 16.33 16.86 14.96 14.08 15.44 15.90 17.30 17.89 15.68 15.92 16.87 15.75 14.61 17.00 16.12 17.77 16.68 16.85 17.35 17.00 17.29 17.30 16.09 15.86 15.90 16.44 16.35 15.95
3 14 17 11 28 30 27 23 5 1 26 21 10 25 29 8 18 2 13 12 4 9 7 6 19 24 22 15 16 20
2 29 9 20 26 28 21 27 5 17 15 18 14 22 7 11 24 25 19 13 3 23 10 6 4 12 16 8 1 30
13 10 3 14 28 22 30 26 2 29 19 20 6 23 12 25 24 11 8 17 5 15 4 1 18 16 27 21 7 9
24 18 5 27 8 26 29 15 21 28 19 25 13 20 22 2 16 30 14 1 4 10 17 3 7 12 9 6 23 11
30 29 21 20 16 27 5 17 24 8 22 28 9 26 14 1 15 23 13 6 25 12 7 2 4 11 3 18 10 19
29 9 28 26 6 18 25 14 23 5 22 17 10 27 12 15 4 3 24 11 1 13 21 2 20 7 19 16 8 30
6 19 24 20 4 10 23 21 16 12 18 11 15 26 3 30 29 5 28 9 7 17 27 14 2 22 8 13 1 25
27 10 5 22 6 23 26 7 11 29 8 2 13 3 12 28 30 15 19 1 20 14 4 16 9 17 24 21 25 18
27 23 25 20 3 21 28 11 22 18 7 10 5 2 19 26 16 14 30 9 8 13 6 24 17 15 4 29 12 1
27 15 26 11 2 16 12 22 13 30 21 20 25 29 28 24 17 8 10 6 5 9 7 19 18 23 4 3 14 1
28 10 29 9 20 11 8 26 16 30 18 14 24 27 17 6 22 4 3 2 12 23 21 19 13 7 5 1 25 15
9 16 30 29 28 25 22 19 27 18 17 8 11 3 23 1 21 12 6 4 26 7j 13 15 20 5 24 14 2 10
18.85 13.97 17.61 15.95 14.76 14.52 15.77 14.70 18.28 16.29 16.61 16.18 17.10 15.40 18.02 17.28 15.11 14.90 16.15 17.20 18.66 15.36 17.59 18.19 18.47 17.20 16.47 17.67 19.51 13.32
19.32 19.78 20.63 19.17 17.05 18.04 16.73 17.62 21.34 16.83 18.55 18.55 20.29 17.96 19.38 17.81 17.93 19.71 20.06 19.06 20.45 19.15 20.55 21.49 19.04 19.09 17.37 18.48 20.24 19.98
21.70 22.14 24.21 20.94 23.17 21.38 20.51 22.59 21.93 20.83 22.02 21.45 22.77 21.94 21.80 24.70 22.51 20.43 22.75 24.82 24.27 23.08 22.36 24.60 23.93 22.90 23.09 23.93 21.80 23.05
24.40 24.56 25.16 25.30 25.96 24.61 26.85 25.95 24.99 26.64 25.14 24.58 26.54 24.79 26.05 27.40 25.97 25.04 26.07 26.76 24.88 26.10 26.69 27.02 26.86 26.28 26.97 25.82 26.40 25.34
26.87 28.41 26.88 27.16 28.57 27.90 27.22 27.99 27.46 28.63 27.53 27.92 28.37 27.15 28.30 27.97 28.68 28.76 27.33 28.31 28.89 28.29 27.60 28.82 27.65 28.56 27.76 27.96 28.44 26.67
29.20 28.71 28.40 28.57 29.44 29.01 28.41 28.48 28.85 28.98 28.84 29.00 28.85 28.22 29.45 27.91 27.98 29.25 28.10 29.03 29.15 28.85 28.13 28.87 29.56 28.42 29.08 28.96 29.64 28.36
27.86 28.79 29.11 28.29 28.99 28.24 28.01 28.98 28.64 27.83 28.87 29.53 28.56 29.35 28.61 27.85 27.37 28.50 28.37 29.55 28.33 28.51 29.18 28.44 28.84 28.43 28.04 28.31 28.02 28.39
27.06 27.27 27.16 27.53 28.35 27.28 26.82 27.79 27.27 27.55 28.11 27.80 28.13 28.85 27.55 27.07 27.62 27.68 26.41 27.82 27.83 27.68 28.12 27.17 27.56 27.63 28.21 26.59 27.73 28.97
24.73 25.46 24.76 25.78 26.39 25.32 25.77 24.95 25.69 24.38 25.07 25.15 24.82 24.56 24.65 24.92 25.29 25.97 25.90 26.15 26.20 25.96 26.08 25.15 25.26 24.92 26.20 26.36 25.59 26.49
20.38 22.27 20.31 22.27 21.48 22.26 22.44 20.46 21.79 19.88 21.53 22.14 20.99 20.41 21.69 22.91 21.21 23.01 23.15 23.17 22.20 21.01 21.47 21.52 22.20 22.62 22.97 23.25 20.89 21.94
18.55 18.15 16.45 16.72 16.76 17.02 17.18 17.70 16.82 17.71 17.82 18.79 18.42 19.19 17.11 19.78 17.36 18.34 18.90 19.11 16.84 18.85 18.25 18.17 17.61 19.07 17.04 18.20 19.27 18.43
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
Year
Notes: 1. Ranking (from the highest to the lowest) is shown in shaded column. 2. TRY is shown in highlighted row.
323
324
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
Table 3 Weather indices and weighting factors for TMY method. Index
Sandia’s TMY method
Dry-bulb temperature
Daily Daily Daily Daily Daily Daily Daily Daily Daily
Dew point temperature
Wind speed Global solar radiation
maximum minimum mean maximum minimum mean maximum mean total
1/24 1/24 2/24 1/24 1/24 2/24 2/24 2/24 12/24
of the 30-year period were determined and the results are listed in Table 4. As shown in the table, the five candidate years for each month with the lowest values of WS are underlined, whereas the lowest one is displayed in bold. Accordingly, the 12 calendar months were selected based on the lowest WS values exhibited in the table. The 12 selected TMMs in TMY were used to form weather input files for computer simulations. Apart from the nine weather indices, other weather parameters such as atmospheric pressure, wind direction, hours of sunshine and cloud cover which can be obtained from the HKO, were also adopted to create the weather files. The direct and diffuse components of solar radiation were determined from their corresponding global solar radiation using the hybrid model established in our previous study [24]. Smoothing of the weather data for discontinuities was carried out using the cubic spline function to avoid abrupt changes at the boundary between two adjacent months from different years. The last 6 h of the preceding day and the first 6 h of the following day were adjusted accordingly [25]. 3. Building energy prediction test To get some idea about the effects of weather data from different years and to assess how close the monthly and annual energy
consumption predicted from the developed TRY and TMY weather files to that predicted from the long-term weather data would be, a series of computer simulations were performed. The building energy simulation program Energy Plus was employed. A generic commercial building was developed to serve as a baseline reference for comparison and evaluation. The base-case office building used was a square (35 35 m2) 40-storey office block with curtain wall designs and a centralized HVAC system. The floor-to-floor and window heights were 3.5 m and 1.5 m respectively. This represented a window-to-wall ratio (WWR) of 43%. Glazing was single reflective glass with a shading coefficient (SC) and a light transmittance (LT) of 0.4 and 0.3, respectively. The U-values for the roof, external walls and windows were 0.46, 1.95 and 5.6W/m2 C, respectively. The air-conditioning plant was a variable-air-volume system with a temperature set point of 24 C in summer and 21 C in winter [26]. The chillers were of a packaged air-cooled hermetic reciprocating type with a coefficient of performance of 2.8. The key variables are summarized in Table 5. Fig. 1 shows the 30 monthly electricity consumption profiles for the individual years from 1979 to 2008. It can be seen that all the years show similar seasonal variations in the electricity use. Electricity consumption peaks during the six hot summer months from May to October, when the average outdoor air temperature exceeds 25 C. The minimum and maximum monthly differences between the thirty years were simulated to be 32 and 102 MWh, respectively. These represented 3.8 and 12.2% of the long-term mean monthly electricity use of 833 MWh. On annual basis, the largest difference between the 30 years was computed to be 289 MWh accounting for 2.9% of the long-term mean annual electricity consumption. However, the larger discrepancies were found on monthly basis between individual years and the long-term monthly electricity consumption, ranging from 2.8% to 6.3%. It reveals that a random year weather year data cannot predict monthly energy use accurately. Predictions from the TMY and TRY (i.e. the year of 2000) weather files were analysed and compared
Table 4 Weighted sum averages of the FS statistics for each month using TMY method.
2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
0.0841 0.0845 0.0752 0.0596 0.0443 0.1022 0.0787 0.0814 0.0642 0.0527 0.1198 0.0688 0.0785 0.0538 0.0761 0.0932 0.1324 0.0890 0.1121 0.0945 0.1294 0.1160 0.1761 0.0855 0.1214 0.1468 0.1114 0.1143 0.0653 0.1301
0.1647 0.1573 0.0938 0.1249 0.1173 0.1322 0.1373 0.1062 0.0596 0.1605 0.0900 0.0645 0.0917 0.0935 0.1266 0.1321 0.1091 0.0918 0.0799 0.1233 0.0753 0.1274 0.1026 0.1653 0.1354 0.2309 0.1165 0.1010 0.1143 0.1291
0.1578 0.0930 0.0618 0.0839 0.0643 0.0492 0.1289 0.1364 0.1208 0.0703 0.0503 0.0886 0.0494 0.0835 0.1044 0.0632 0.1045 0.0887 0.0940 0.1239 0.1478 0.1271 0.0963 0.1114 0.0967 0.2052 0.0819 0.0873 0.0623 0.1212
0.0641 0.1096 0.1113 0.0822 0.0701 0.0745 0.1565 0.0607 0.0616 0.1179 0.1272 0.0760 0.1166 0.0796 0.1591 0.0753 0.0755 0.0759 0.1108 0.0961 0.1122 0.0732 0.0585 0.1052 0.1600 0.1085 0.1143 0.1057 0.0529 0.0592
0.0693 0.1070 0.0747 0.1587 0.0869 0.0756 0.0926 0.0830 0.0689 0.0742 0.0780 0.0563 0.0785 0.0614 0.1258 0.0629 0.1097 0.0971 0.0992 0.0764 0.0963 0.1023 0.0585 0.1261 0.1027 0.0970 0.0893 0.1013 0.1002 0.1057
0.1818 0.1117 0.1075 0.1286 0.1070 0.1132 0.0851 0.1004 0.1288 0.1036 0.1414 0.0990 0.0995 0.0970 0.1086 0.0990 0.0584 0.0581 0.0580 0.0959 0.0986 0.0691 0.0611 0.1275 0.0942 0.1007 0.1129 0.1074 0.1094 0.1001
0.0832 0.1755 0.1107 0.0768 0.0993 0.1528 0.1411 0.1245 0.0573 0.1165 0.0865 0.1381 0.0552 0.0721 0.1673 0.1034 0.0709 0.0919 0.0570 0.0670 0.0883 0.0766 0.0502 0.1113 0.1747 0.0850 0.0906 0.1250 0.0732 0.1778
0.1277 0.0860 0.0547 0.0608 0.0830 0.0827 0.0471 0.0874 0.0830 0.0972 0.1189 0.1000 0.0612 0.1463 0.1224 0.0553 0.0872 0.0641 0.1090 0.0637 0.0969 0.1257 0.0641 0.0612 0.0742 0.0767 0.0650 0.0951 0.0904 0.0996
0.1398 0.0740 0.0919 0.0787 0.0830 0.0572 0.0764 0.0877 0.1304 0.0635 0.0602 0.1156 0.0568 0.0645 0.1357 0.0642 0.1156 0.0729 0.0612 0.0535 0.0904 0.0609 0.0975 0.0902 0.0765 0.0905 0.0483 0.1120 0.0943 0.1039
0.0992 0.0569 0.1647 0.0811 0.1707 0.0895 0.1292 0.0898 0.0922 0.0692 0.0567 0.1356 0.0551 0.1062 0.1182 0.0892 0.1556 0.0800 0.1198 0.0696 0.1018 0.0700 0.0726 0.0630 0.0516 0.1615 0.0811 0.0737 0.0621 0.3408
0.1423 0.1694 0.1646 0.1051 0.0709 0.0792 0.0687 0.0998 0.1046 0.0776 0.0980 0.0787 0.0684 0.0769 0.1498 0.1117 0.1435 0.0786 0.0875 0.0432 0.1270 0.1449 0.0680 0.0644 0.0560 0.1647 0.1356 0.1254 0.1362 0.1237
0.1095 0.1093 0.0881 0.0819 0.1049 0.1403 0.1231 0.0818 0.0725 0.0910 0.0758 0.1449 0.0724 0.1058 0.1998 0.0649 0.1172 0.1309 0.1011 0.0802 0.1052 0.0974 0.0924 0.0683 0.1055 0.0786 0.0834 0.1507 0.1102 0.1040
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
325
Table 5 Brief description of base case office building. Location: Hong Kong (latitude 22.3 N, longitude 114.2 E) Building type and Office building, 40 storeys above ground storeys: Floor areas: Total gross floor area ¼ 49,000 m2 Air-conditioned area ¼ 41,160 m2 Dimensions and 35 m 35 m (square); floor-to-floor ¼ 3.5 m; window heights: height ¼ 1.5 m; window-to-wall ratio ¼ 0.43 Constructions of building envelope: (a) External walls (spandrel portion of curtain wall) - 6 mm glass þ 25 mm airspace þ 19 mm plywood þ wall paper (U-value ¼ 1.95 W/m2 C) (b) Roof - 13 mm slag þ 10 mm roof build-up þ 50 mm roof insulation þ 200 mm n.w. concrete þ ceiling void þ 19 mm ceiling panel (U-value ¼ 0.46 W/m2 C) (c) Windows - 6 mm reflective single glazing (SC ¼ 0.4, LT ¼ 0.3, Uvalue ¼ 5.6 W/m2 C) Operating hours: Mon. to Fri.: 08:00e18:00; Sat.: 08:00e13:00; Sun. closed HVAC design Occupancy density ¼ 8 m2/person parameters: Lighting load ¼ 20 W/m2; equipment load ¼ 18 W/m2 Infiltration ¼ 0.6 air change per hour during fans OFF Space design temperature ¼ 24 C
with those from the 30 years. Fig. 2 shows that predictions from the two weather files appear within the maximum and minimum range of predictions from the thirty individual years. In general, they follow quite closely the 30-year long-term mean. The performance of the typical weather datasets were further examined by mean-bias deviation (MBD) and root-mean-square deviation (RMSD) as follows:
P MBD ¼
ðENmonth ENmean Þ 12
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P ðENmonth ENmean Þ2 RMSD ¼ 12
(4)
(5)
where ENmonth is the building energy use for each month (MWh); ENmean is the long-term monthly mean building energy use (MWh). Accordingly, the MBD and RMSD were calculated and Fig. 3 shows the results for the two weather datasets compared with the long-term monthly mean consumption. The MBD for the TMY and TRY are 4.9 and 2.4 MWh, respectively. These represent around 0.6% and 0.3% of the long-term average monthly electricity use of 833 MWh for TMY and TRY, respectively. The two small MBDs are a result of a fortuitous cancellation between over- and under-estimation. It can be seen that the TMY has an obvious smaller value of RMSD of 9.6 MWh accounting for only 1.2% of the long-term monthly mean consumption. The TRY has the RMSD of 20 MWh, denotes 2.4% of the long-term mean. The results show that TMY is an appropriate weather dataset to be selected for longterm energy analysis. A sensitivity analysis was carried out to examine the parametric weightings affecting annual electricity consumption by introducing an alternative weighting scheme, which was used by the International Weather for Energy Calculations (IWEC) [27]. Larger total weighting factor of 0.4 are assigned to the dry bulb temperatures because the criterion affects the energy use of air-conditioning systems significantly. The weighting factor of global solar radiation is reduced to 0.4. Twelve calendar months were selected based on the lowest WS values and are shown in Table 6. The results showed that the years with the lowest WS values are the same between August and December and total eight identical TMMs were selected. In total, forty-one out of sixty underlined WS values are the same. The statistical results suggest that the month selection using the two approaches often gives similar findings. The simulated results showed that the MBD and the RMSD of the IWEC
Fig. 1. Predicted monthly electricity use for the base-case building in (a) 1979e1988, (b) 1989e1998, and (c) 1999 to 2008.
Fig. 2. Predicted monthly electricity use for the base-case building based on long-term mean, TMY and TRY.
326
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
Fig. 3. Mean-bias and root-mean-square deviations for the TMY and TRY with respect to long-term mean.
Fig. 4. Cumulative frequency curves of global, sky-diffuse and direct-beam illuminance.
is 4.8 MWh and 8.3 MWh, respectively, compared with the longterm monthly mean consumption, which are comparable to the TMY scheme. It implies that using TMY approach with different weighting schemes can also find an appropriate weather dataset for long-term energy analysis.
applications, sky-diffuse illuminance is more important and widely used and direct sunlight is often excluded. Problems of glare, excessive brightness ratios and thermal discomfort have support the exclusion [31]. Because of higher luminous efficacy, sky-diffuse illuminance is more energy-efficient in daylighting design. However, direct sunlight can give a sense of excitement, cheerfulness, warmth and psychological well being in occupants’ living and working environments [32]. People may desire to have sunshine when the overheating and glare effects are of accepting levels. Generally, high illuminance values are often dominated by BV and DV is more than BV at low illuminance levels. The cumulative frequency for BV is less than that for DV at 15 klux and vice-versa. Also, no BV and DV of up to 25 klux are observed at around 40% of daytime period which corresponds to overcast skies and conditions near sunrise and sunset. The maximum values for BV, DV and GV are 74, 46 and 105 klux, respectively. Recently, the CIE adopted a list of 15 standard skies. Each sky standard has its own well-defined sky luminance pattern which is the straight-forward approach for sky classification and can help the determination of daylight illuminance on inclined surfaces facing various orientations. In practice, the important issue would be the frequency with which these individual standard skies appear at a given location. However, luminance distributions for the whole sky vault are less assessable and sky conditions categorized using climatic parameters which are widely obtainable would be more appropriate. As reported by Kittler et al. [15], the ratio of zenith luminance to horizontal sky-diffuse illuminance (Lz/Dv) can characterize the momentary sky brightness and theoretically can represent the pattern of the 15 CIE Standard General Skies. The Lz/Dv theoretical curves for the 15 standard skies are not parallel but they intersect with each other at as of 35 or more. This could introduce errors in sky categorization for a place where solar altitudes often exceed 35 [33,34]. Previously, we used the 15 Lz/Dv-as theoretical curves, Gv/Ev, Dv/Gv and luminous turbidity (Tv) for classifying the 15 CIE skies [13]. The general equation for Tv is given as:
4. Daylight data and standard skies for TMY and TRY Daylighting is an important strategy in displacing the need for high grade energy (electricity) used for artificial lighting. The first step towards designing a building to utilize daylight for illuminating its interior is to acquire information on the amount of daylight available. An accurate estimation of the available daylight is to acquire, not just the total amount of light coming from the sky, but also the distributions of luminance across the sky vault [28]. For successful daylighting analysis, there is a potential need to have a typical weather dataset containing daylight data and classified CIE General Standard Skies. The horizontal global solar radiation data have been measuring by the HKO as part of their routine work but no outdoor illuminance measurement. For estimating daylight illuminance, the common approach has been the derivation of illuminance from measured solar irradiance using the luminous efficacy approach. The following mathematical expressions were used to compute the direct-beam (KB) and sky-diffuse (KD) luminous efficacies on a horizontal surface [29,30].
KB ¼ 59:15 þ 1:12aS 0:0061a2S ðlm=WÞ
(6)
KD ¼ 130:6 14:4Cðlm=WÞ
(7)
where aS is the solar altitude (degrees); C is the cloud fraction Using Eqs. (6) and (7), the direct-beam (BV) and sky-diffuse (Dv) daylight illuminance can be calculated from the corresponding solar irradiance data and the horizontal global daylight illuminance (GV) is the sum of the computed BV and DV. The BV, DV and GV for the TMY were determined and their cumulative frequency curves are displayed in Fig. 4. The cumulative frequency distribution of outdoor illuminance can indicate the percentage of the working year in which a given illuminance is exceeded. For daylighting
Tv ¼
ln ðEv =Bv Þ av mv
(8)
Table 6 Twelve selected calendar months for TMY and IWEC schemes. Month
1
2
3
4
5
6
7
8
9
10
11
12
TMY IWEC
2004 1995
2000 1988
2003 2003
1980 1986
1997 1997
1990 1990
1986 2000
2002 2002
1982 1982
1984 1984
1989 1989
1993 1993
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
327
the TMY and TRY were in good agreements with the 30-year (1979e2008) long-term prediction. The root-mean-square deviation (RMSD) for TMY and TRY were 1.2 and 2.4%, respectively. It is envisaged that both TMY and TRY can give a good indication of the prevailing energy performance in building energy simulation exercise in Hong Kong. Further studies were carried out to generate daylight variables and to identify various daytime periods into the 15 CIE Standard General Skies based on these generated daylight parameters. For sky categorization, the frequency of occurrence pattern however was not quite similar to that using measured daylight data. Subsequent analysis may be required to modify the models for predicting daylight variables.
Acknowledgments Fig. 5. Frequency of occurrence of the classified 15 CIE general standard skies for Hong Kong TMY and TRY.
where Ev is the extraterrestrial illuminance (lux); av is the 1 luminous ideal extinction ¼ [35]; mv is the optical 9:9 þ 0:043mv 1 [36]. air mass ¼ sin aS þ 0:50572ðaS þ 6:07995Þ1:6364 The LZ data were estimated from the Perez et al. model [37]. The predicted hourly daylight illumiance and LZ data for the both TMY and TRY were used for the categorization of the CIE Standard General Skies based on our proposed approach [13]. Fig. 5 presents the frequency distributions for the 15 CIE Standard Skies. For the TMY, even though large variations can be observed for individual standard skies, a thorough examination of the figure revealed that the three main sky types (i.e. overcast e sky nos. 1 to 5; partly cloudy e sky nos. 6 to 10; and clear e sky nos. 11e15) are quite evenly distributed in Hong Kong under this classification method. Skies 1, 7, 9, 13 and 15 occur most frequently. Sky type 1 dominates the subset of overcast skies representing 13% of the Hong Kong sky conditions. Sky types 7 and 9 are the main sky patterns for the partly cloudy skies with the frequency of occurrence of around 10%. Referring to clear sky conditions, sky types 13 and 15 have the highest frequency of occurrence accounting for 11 and 16%, respectively. The overall result of the 15 sky classifications between TRY and TMY is quite consistent. However, the clear sky dominated for the TRY accounting for 45% of the frequency of occurrence in Hong Kong. The sky 13 showed the highest frequency of occurrence of 17.6% in the TRY sky classification. The frequency of occurrence pattern however was not generally similar to our previous study [12]. The discrepancy may be due to the fact that modeled rather than measured daylight variables were used for the sky classification. 5. Conclusions Typical weather database representative of the prevailing weather conditions is a key element in the building energy simulation process. Weather data of not less than a 30-year period are conservatively stable for building designs and the typical weather dataset should be periodically reviewed to reflect the climatic trend and variation. Based on hourly weather data collected between 1979 and 2008 (30 years) and the selection procedures, a typical meteorological year (TMY) and a typical reference year (TRY) for Hong Kong were established. The performance of the two weather files were conducted via building energy simulations. It was found that the monthly electricity consumption profiles simulated from
The work described in this paper was fully supported by a grant from the Central Policy Unit of the Government of Hong Kong Special Administrative Region and the Research Grants Council of the Special Administrative Region, China (Project No. CityU1011PPR-10). The authors thank Mr. Thomas W. K. Wong for his help with data analysis.
References [1] Lee K, Yoo H, Levermore GJ. Generation of typical weather data using the ISO Test Reference Year (TRY) method for major cities of South Korea. Building Environ 2010;45(4):956e63. [2] Yang L, Wan KKW, Li DHW, Lam JC. A new method to develop typical weather years in different climates for building energy use studies. Energy 2011; 36(10):6121e9. [3] Chan ALS. Developing a modified typical meteorological year weather file for Hong Kong taking into account the urban heat island effect. Building Environ 2011;46(12):2434e41. [4] Bhandari M, Shrestha S, New J. Evaluation of weather datasets for building energy simulation. Energ Build, in press, Corrected proof, Available online 10 February 2012. [5] Crow LW. Development of hourly data for weather year for energy calculations (WYEC), including solar data, at 21 stations throughout the U.S. ASHRAE Trans 1981;87(Part 1):896e906. [6] Chow TT, Chan ALS, Fong KF, Lin Z. Some perceptions on typical weather year e from the observations of Hong Kong and Macau. Solar Energy 2006;80(4): 459e67. [7] Li DHW, Cheung GHW, Lam JC. Analysis of the operational performance and efficiency characteristic for photovoltaic system in Hong Kong. Energy Conversion Manag 2005;46(7,8):1107e18. [8] Li DHW, Lam TNT. An analysis of building energy performances and benefits using solar facades. Proc Inst Mech Engineers Part A J Power Energy 2008; 222(A3):299e308. [9] Li DHW, Lam JC. An analysis of climatic parameters and sky condition classification. Building Environ 2001;36(4):435e45. [10] CIE, S 011/E. Spatial distribution of daylight e CIE standard general sky. Vienna: Standard, CIE Central Bureau; 2003. [11] Tregenza PR. Standard skies for maritime climates. Lighting Res Technol 1999; 31(3):97e106. [12] Li DHW, Lau CCS, Lam JC. A study of 15 sky luminance patterns against Hong Kong data. Architectural Sci Rev 2003;46(1):61e8. [13] Li DHW, Tang HL. Standard skies classification in Hong Kong. J Atmos SolarTerrestrial Phys 2008;70(8e9):1222e30. [14] Li DHW, Cheung KL, Tang HL, Cheng CCK. Identifying CIE Standard Skies using vertical sky component. J Atmos Solar-Terrestrial Phys 2011;73(13): 1861e7. [15] Kittler R, Darula S, Perez R. A set of standard skies. Bratislava: Polygrafia; 1998. [16] Hong Kong Observatory. Surface observations in Hong Kong, annual publications, Hong Kong Observatory, Hong Kong SAR, China (annual publication). [17] ASHRAE Handbook e Fundamentals. American Society of Heating. Atlanta: Refrigerating and Air Conditioning Engineers, Inc.; 2005. [18] Hall IJ, Prairie RR, Anderson HE, Boes EC. Generation of a typical meteorological year. Proceedings of the 1978 Annual Meeting of the American Section of the International Solar Energy Society, Denver; 1978. pp. 669e671. [19] Hui SCM, Lam JC. Test reference year (TRY) for comparative energy study. J Hong Kong Inst Engineers; 1992, February:13e6. [20] Finkelstein JM, Schafer RE. Improved goodness-of-fit tests. Biometrika 1971; 58(3):641e5. [21] Conover WJ. Practical nonparametric statistics. 2nd ed. New York: John Wiley & Sons; 1980.
328
S.L. Wong et al. / Building and Environment 56 (2012) 321e328
[22] Skeiker K. Generation of a typical meteorological year for Damascus zone using the Finkelstein-Schafer statistical method. Energy Conversion Manag 2004;45(1):99e112. [23] Yang L, Lam JC, Liu JP. Analysis of typical meteorological years in different climates of China. Energy Conversion Manag 2007;48(2):654e68. [24] Lam JC, Li DHW. Correlation between global solar radiation and its direct and diffuse components. Building Environ 1996;31(6):527e55. [25] Marion W, Urban K. User’s manual for TMY2s. Golden, Colo: National Renewable Energy Laboratory; 1995. [26] Electrical and Mechanical Services Department. Guidelines on energy efficiency of lighting installations. Hong Kong: Electrical and Mechanical Services Department; 1998. [27] ASHRAE. International weather for energy calculations (IWEC weather files) User’s Manual and CD-ROM. Atlanta, GA: American Society of Heating, Refrigerating and Air- Conditioning Engineers, Inc.; 2001. [28] Li DHW, Cheung GHW, Cheung KL. Evaluation of simplified procedure for indoor daylight illuminance determination against data in scale model measurements. Indoor Built Environ 2006;15(3):213e23. [29] Lam JC, Li DHW. Luminous efficiency of daylight under different sky conditions. Energy Conversion Manag 1996;37(12):1703e11.
[30] Li DHW, Lam TNT, Cheung KL, Tang HL. An analysis of luminous efficacies under the CIE standard skies. Renewable Energy 2008;33(11): 2357e65. [31] Lam JC, Li DHW. Day lighting and energy analysis for air-conditioning office building. Energy 1998;23(2):79e89. [32] Cheung HD, Chung TM. Analyzing sunlight duration and optimum shading using a sky map. Building Environ 2007;42(9):3138e48. [33] Bartzokas A, Darula S, Kambezidis HD, Kittler R. Sky luminance distribution in Central Europe and the Mediterranean area during the winter period. J Atmos Solar-Terrestrial Phys 2003;65(1):113e9. [34] Bartzokas A, Kambezidis HD, Darula S, Kittler R. Comparison between winter and summer sky-luminance distribution in Central Europe and in the Eastern Mediterranean. J Atmos Solar-Terrestrial Phys 2005;67(7):709e18. [35] Navvab M, Karayel M, Ne’eman E, Selkovitz S. Analysis of atmospheric turbidity for daylight calculations. Energ Build 1984;6(2e4):293e303. [36] Kasten F, Young AT. Revised optical air mass tables and approximation formula. Appl Opt 1989;28(22):4735e8. [37] Perez R, Ineichen P, Seals R. Modeling daylight availability and irradiance components from direct and global irradiance. Solar Energy 1990;44(5): 271e89.