- Email: [email protected]
Development of the typical meteorological database for Chinese locations
Energy and Buildings 38 (2006) 1320–1326 www.elsevier.com/locate/enbuild
Development of the typical meteorological database for Chinese locations Qingyuan Zhang * Tsukuba University of Technology, 4-3-15 Amakubo, Tsukuba 305-0005, Japan
Abstract The typical meteorological database for 57 Chinese locations was developed for building simulations and air-conditioning design. The database consists of three parts: the typical meteorological years (TMY), the typical meteorological days (TMD) and the design temperature and humidity (DTH). The typical meteorological year (TMY) is the main part of the database. Because there are not data on solar radiation in the observations, a method to estimate solar radiation with dry bulb temperature difference, relative humidity, total cloud cover and wind speed was developed. Methodologies of interpolations were developed to produce 1 h data with the 3 h data. The global solar radiation on the horizontal surface was separated into direct and diffuse components with the Gompertz function. The typical meteorological day (TMD) consists of the monthly average values of dry bulb temperature, solar radiation, relative humidity, etc. for each hour of the day. The design temperature and humidity (DTH) was developed for the purpose of air-conditioning design. The frequencies of 2.5% and 5.0% were selected to decide the design temperature and humidity for the 57 Chinese locations. # 2006 Elsevier B.V. All rights reserved. Keywords: Typical meteorological year; Typical meteorological day; Design temperature and humidity; Observation; Interpolation
1. Introduction Weather is one of the primary determinants of indoor thermal conditions and space conditioning energy use. There are two kinds of weather data in building energy calculations: one is the data for hourly simulations and another is for equipment design. Usually the former consists of 8760 h data forming an average year; the latter consists of extreme hours in summer or winter. The weather data for hourly simulations in China have not been developed systematically until recently. The main reason for the delays in the development of weather data for building simulations is that most of the observation data were not in an electronic format, especially before the 1990s, making data processing difficult and expensive. The typical meteorological database for 57 Chinese locations was developed for building simulations and airconditioning design. The database consists of three parts: (1) the typical meteorological years (TMY); (2) the typical meteorological days (TMD) which can be considered a kind
* Tel.: +81 29 858 9376; fax: +81 29 858 9376. E-mail address: [email protected]. 0378-7788/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2006.04.003
of simplified TMY; and (3) the design temperature and humidity (DTH). The typical meteorological year (TMY) is the main part of the database [1,2]. The TMY is based on observations with 3 h intervals. Because there are not data on solar radiation in the observations, a method to estimate solar radiation with dry bulb temperature difference, relative humidity and total cloud cover was developed. The typical meteorological months were selected by how close the variables of a month are to the average. In order to produce 1 h data with the 3 h data, methodologies of interpolations were developed. The global solar radiation on the horizontal surface was separated into direct and diffuse components with the Gompertz function. The typical meteorological day (TMD) consists of the monthly average values of dry bulb temperature, relative humidity, etc. for each hour of the day [3]. It was verified that the air-conditioning load of an apartment house can be approximated by using the average meteorological elements like dry bulb temperature, humidity, solar radiation, wind speed and cloud amount. Therefore the monthly average meteorological data for each hour were defined as the typical weather days for the month. The typical weather days for the 57 locations in China were given.
Q. Zhang / Energy and Buildings 38 (2006) 1320–1326
The design temperature and humidity (DTH) was developed for the purpose of air-conditioning design. It was based on the frequency level of a specific temperature over the past 16 years. The frequencies of 2.5% and 5.0% were selected to decide the design temperature and humidity for the 57 Chinese locations. 2. Typical meteorological year The typical meteorological year is the main part of the Chinese typical meteorological database developed in this study because it is indispensable in hourly building simulations. The development of Chinese TMYs began in 1999, and the TMYs for 57 Chinese locations have been completed. The TMYs are based on the observations from International Surface Weather Observations (ISWO database) [4]. Because there are not any data about solar radiation, a model to predict radiation is necessary. In order to estimate the hourly solar radiation for the 57 Chinese locations, it is necessary to find relations between solar radiation and other variables. Cui et al. [5] showed the correlation between solar radiation and temperature change from previous hours, the amount of cloud cover, relative humidity and wind speed using observations with 6 h intervals, but the accuracy was not sufficient due to the large time intervals. It is possible to improve the accuracy using the data from ISWO database because the observation intervals are smaller (3 h) than that used by Cui et al. Also it is possible for the author to develop models that are applicable to more locations because there are solar data included in the OSR database (observed solar data in 1993 for 24 major cities of China). In order to select parameters in the models to estimate solar radiation, the relations between solar radiation and other
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variables like the amount of cloud cover, temperature changes, and relative humidity were examined using the observations of 24 locations from the ISWO and OSR databases. Multiple regressions were carried out to establish models to estimate hourly solar radiation. Dry-bulb temperature changes, total amount of cloud cover and relative humidity were adopted as variables as shown in Eq. (1): 2 CC CC Ih ¼ I0 sin h C0 þ C1 þ C2 þ C3 10 10 ðun un3 Þ þ C4 f C5 =k (1) where Ih is the estimated global solar radiation on the horizontal surface in W/m2; I0 is the solar constant; CC is the amount of cloud cover in tenths; un and un3 are dry-bulb temperature at hours n and n 3, respectively; h is the sun’s altitude and the term (I0sin h) means solar radiation on the horizontal surface in the outer space; f is the relative humidity in %; C1 . . . C5 and k are regression coefficients, the values of which differ from place to place as shown in Table 1. The coefficients of correlation R for all the 24 locations are between 0.91 and 0.97 with maximum error of 137 W/m2 except for Lhasa. The error for Lhasa is larger than that for other locations but the total amount of solar radiation in Lhasa is also much stronger than other locations. The relationship between the observational solar radiation and estimations from Eq. (1) for Beijing is shown in Fig. 1 as an example. The correlation coefficient R is 0.97, and the root mean square error (RMSE) of the estimation is 80 W/m2, which implies that Eq. (1) can be used to estimate the hourly horizontal solar radiation with good accuracy in Beijing. Based
Table 1 Coefficients in Eq. (1) for different cities Cities
C0
C1
C2
C3
C4
C5
k
R
RMSE
Beijing Changchun Changsha Chengdu Fuzhou Guangzhou Guiyang Hangzhou Harbin Hefei Jinan Kunming Lhasa Lanzhou Nanchang Nanning Nanjing Shenyang Tianjin Wuhan Xian Xining Yinchuan Zhengzhou
0.6584 0.8412 0.7085 0.3645 0.7960 0.6050 0.4688 0.4378 1.0235 0.8084 0.6497 0.4817 0.6996 0.3545 0.7638 0.4989 0.7586 0.8199 0.7297 0.7395 0.5283 0.3856 0.5831 0.7085
0.4864 0.4406 0.7065 0.4800 0.7279 0.5755 0.4750 0.8395 0.5162 0.6724 0.4679 0.2936 0.0929 0.6723 0.8086 0.7322 0.5914 0.6304 0.5113 0.7426 0.6062 0.6237 0.4261 0.5092
0.6647 0.6853 0.9413 0.6335 0.9365 0.7893 0.7223 1.1174 0.6877 0.8846 0.6317 0.5768 0.2399 0.8564 1.0198 0.9156 0.7919 0.8533 0.7432 0.9817 0.7861 0.8658 0.7089 0.7069
0.0203 0.0021 0.0230 0.0495 0.0200 0.0278 0.0321 0.0408 0.0056 0.0189 0.0242 0.0403 0.0162 0.0430 0.0348 0.0402 0.0181 0.0035 0.0118 0.0276 0.0353 0.0376 0.0282 0.0165
0.0039 0.0047 0.0038 0.0011 0.0052 0.0030 0.0008 0.0001 0.0063 0.0051 0.0038 0.0003 0.0026 0.0007 0.0048 0.0020 0.0050 0.0051 0.0036 0.0043 0.0024 0.0015 0.0006 0.0037
36.6114 40.2260 42.2020 40.5660 35.7491 36.8362 38.3084 41.3412 35.0285 35.6732 27.2746 43.9962 56.6359 40.8254 35.5071 42.7619 31.8024 39.2128 38.6689 37.0186 36.6207 41.7887 37.4911 37.0826
0.9300 0.8868 0.8747 0.8250 0.9112 0.8998 0.8743 0.8702 0.8924 0.9197 0.9178 0.8745 0.8811 0.8954 0.9192 0.8687 0.9350 0.9047 0.9110 0.9123 0.9070 0.8910 0.9237 0.9250
0.97 0.94 0.94 0.92 0.94 0.95 0.93 0.91 0.93 0.96 0.96 0.93 0.94 0.95 0.96 0.93 0.97 0.96 0.97 0.96 0.96 0.94 0.96 0.97
80 116 84 103 96 87 96 115 136 88 88 137 165 110 90 114 76 98 106 90 91 122 105 86
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Q. Zhang / Energy and Buildings 38 (2006) 1320–1326
Fig. 1. Correlation between observed and estimated solar radiation by Eq. (1) (Beijing 1993). Fig. 2. Comparison between the values of Kn from observation and Eq. (2).
on these figures and Table 1, it is reasonable to conclude that Eq. (1) can be used to estimate hourly global solar radiation on the horizontal surface for the 24 Chinese locations. The coefficients for other locations were decided referring to that for the locations with similar longitude, latitude and elevation. In simulating the thermal performance of buildings, solar radiation in the normal direction and on declined surfaces is necessary. Therefore a procedure to separate the horizontal radiation to direct beam and diffuse component was developed. Using observations from six Chinese locations (Beijing, Chengdu, Lanzhou, Lhasa, Shanghai and Urumqi) and one Japanese location (the Aerological Observatory, Tsukuba), a method of separating horizontal radiation into direct and diffuse components was suggested by Zhang et al. [6]. The direct beam transmittance Kn can be calculated with the following equation in the form of Gompertz function [7]: Kn ¼
A4 Kt
A3 A A1 A2 2
(2)
The comparison between the values of Kn from observation and Eq. (2) is shown in Fig. 2, which shows that Eq. (2) fits the observations with minor errors. Because the interval of the ISWO data is 3 h and 1 h data are necessary in most computer programs like DOE-2 [8], procedures of interpolating 3 h data to 1 h data were developed using the Fourier series. The following equations were used in the interpolation: f ðtÞ ¼
A1 ¼ 0:1556 sin h þ 0:1028 sin h þ 1:3748
(3)
A2 ¼ 0:7973 sin2 h þ 0:1509 sin h þ 3:035
(4)
A3 ¼ 5:4307 sin h þ 7:2182
(5)
A4 ¼ 0:2990
(6)
The direct normal radiation can be calculated using the following equation: In ¼ Kn I0
(7)
The diffuse component at the horizontal surface can be obtained by the following equation: Id ¼ Ih In sin h
(8)
where Id is the diffuse horizontal radiation at the earth’s surface in W/m2; In is the direct normal at the earth’s surface in W/m2; the direct beam transmittance Kn is expressed as: Kn ¼
In sin h In ¼ I0 sin h I0
(9)
an sinðnvtÞ þ b0 þ
n¼1
M X
bn cosðnvtÞ
(10)
n¼1
an ¼
7 1X npk f ðkÞ sin 4 k¼0 4
(11)
bn ¼
7 1X npk f ðkÞ cos 4 k¼0 4
(12)
b0 ¼
7 1X f ðkÞ 8 k¼0
(13)
where 2
M X
Because of the assumption in the Fourier series that the temperature in the beginning of a day is equal or close to that in the end of the day, a single interpolation over a day is not enough. Another procedure of interpolation using the data of 14 through 11 of the next day was carried out to improve the connection between 2 days. A comparison of the results from the observations, a single Fourier interpolation and the double Fourier interpolation is shown in Fig. 3. There is a sudden change between July 23 and July 24 in the single Fourier interpolation. The connection between the days is smooth with the double Fourier interpolation. The interpolation of dew point temperature, as well as wind speed, cloud cover, was also carried out with the double Fourier interpolation. A computer subroutine for the double Fourier interpolation can be found in a reference by Zhang and Huang [9]. The TMY consists of 12 typical months (TMM) that are selected from the observations. The TMM is defined as the month that is neither cooler nor warmer than the average. There
Q. Zhang / Energy and Buildings 38 (2006) 1320–1326
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between the neighboring months. The following equation was used to smooth the joining values of temperature or humidity: ui ¼
12 i 0 i u þ u00 12 i 12 i
(15)
where ui shows the temperature after smoothing; u0i and u00 are the values of temperature of the former and the latter months, respectively. 3. Typical meteorological days
Fig. 3. Comparison of results from different interpolations (Tokyo, 23–24 July 2003).
have been different methods in selecting TMMs [10–12]. In this study, the procedure to select TMMs can be described as follows: (1) Select the months whose average dry-bulb temperature, dew-point temperature, solar radiation and wind speed are within the range of 0.6 times the standard deviation. If only one candidate is left after Step (1), the only candidate is selected as the TMM. If there is no candidate left, go to Step (2). If there is more than one candidate after Step (1), go to Step (4). (2) Select the months whose average dry-bulb temperature, dew-point temperature, solar radiation and wind speed are within the range of 0.8 times the standard deviation. If only one candidate is left after Step (2), the only candidate is selected as the TMM. If there is no candidate left, go to Step (3). If there is more than one candidate after Step (2), go to Step (4). (3) Select the months whose average dry-bulb temperature, dew-point temperature, solar radiation and wind speed are within the range of the standard deviation. If only one candidate is left after Step (3), the only candidate is selected as the TMM. If there is more than one candidate left after Step (3), go to Step (4). (4) Compare the WS values of the remaining months, and select the month whose WS is the smallest as the TMM. The value of WS is calculated as follows: X WS ¼ ðwi FSi Þ
The typical meteorological years can serve as an important tool for building simulations, but the TMY has the following problems: (1) the average of each meteorological element does not always equal to the average of past years; (2) the characteristics of a location cannot be imagined only by the TMY. To solve these problems, it is desirable to find a kind of simple data that can characterize the climate of a location. One of the methods is to make a set of 24 h data to representative a month by averaging each meteorological element. In order to verify that this kind of weather data may characterize the climate of a location, two kinds of simulations were carried out using an apartment house model (Fig. 4): one is simulations with historical hourly data; another is simulations with monthly average meteorological elements including temperature, humidity, solar radiation, and wind speed for each hour of the day. The correlations between the results from these two kinds of simulations are shown in Figs. 5 and 6, respectively. The average heating load in January (Fig. 5) and cooling load in July (Fig. 6) from hourly historical weather data and from monthly average meteorological elements agree with each other well. These facts imply that the 24 h data consisting of average meteorological elements can be representative of
(14)
where FSi means the Finkelstein–Shafer (FS) statistic [13]. The smaller the FSi, the closer the structure of a variable will be to the average year. The weights wi applied to the different climate elements are the same as those used by NCDC in the development of the TMYs [10]. Usually the neighboring months are from different years, therefore sudden changes in temperature or humidity may occur
Fig. 4. Apartment model for simulations.
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Q. Zhang / Energy and Buildings 38 (2006) 1320–1326
Fig. 7. The temperature in the TMD for January, April, July and October for Beijing. Fig. 5. Relation between heating load by using historical weather data and by using average elements (January).
historical hourly weather data, at least in the field of calculating air-conditioning load. Therefore the 24 h data containing monthly average elements are defined as the typical meteorological day (or TMD) of the month. Comparing with the historical hourly data or the TMY data, the TMD contains far less data and they can characterize the climate of a location. Another advantage of the TMD is that time used for simulations with the TMD can be shortened to about one thirtieth. Figs. 7–9 show the 24 h temperature, humidity and horizontal solar radiation in January, April, July and October for Beijing. These data can be used to calculate air conditioning load of an average month, as well as to characterize the climate of a location. In Beijing, the hourly temperature in April is almost the same as that in October (Fig. 7), while the relative humidity in October is obviously higher than that in April (Fig. 8). The horizontal solar radiation in April is the strongest of the four months (Fig. 9). These advantages do not mean, however, that the TMY can be replaces by the TMD, because when daily or hourly load is concerned, TMY should be used instead of the TMD. Therefore which kind of data should be used depends upon the purposes.
Fig. 6. Relation between cooling load by using historical weather data and by using average elements (July).
Fig. 8. Relative humidity in the TMD for January, April, July and October for Beijing.
4. Design temperature and humidity The main purpose of TMYand TMD is to simulate the thermal behavior of buildings for an average year or a month, and it is not for the design of air-conditioning equipment. The design weather data should consist of extreme hours in summer or winter. One of the main methods to decide the design temperature is based on the frequency level of a specific temperature over a time period. The frequencies of 2.5% and 5.0% were selected to decide the design temperatures for the 57 Chinese locations. The ASHRAE [14] has recommended the weather data for
Fig. 9. Global horizontal solar radiation in the TMD for January, April, July and October for Beijing.
Q. Zhang / Energy and Buildings 38 (2006) 1320–1326
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5. Conclusions The Chinese typical meteorological database was developed and it consists of three parts: the typical meteorological year (TMY); the typical meteorological day (TMD); and the design temperature and humidity (DTH). The main conclusions from this study are:
Fig. 10. Temperature for heating design with the frequency level of 2.5% and 5.0% (Beijing).
some Chinese locations, but there are two problems: (1) only one dry-bulb temperature and one wet-bulb temperature were given for each location, while 24 h weather data are necessary for some calculation methods; and (2) the number of Chinese cities whose weather data are given for air-conditioning design is too small therefore more studies are needed about these and other locations. In this study, the frequencies of dry-bulb temperature and relative humidity were computed for the 57 Chinese locations over the period of 1982–1997 and cumulative frequency curves were drawn for each month and each location. For cooling design temperature, frequencies were calculated over the period of June through August; while for the heating design, frequency calculation was carried out over the period of December through February. Twenty-four hours data were given for the heating design for the 57 locations. Usually there is a gap of about 18 between 2.5% and 5% frequencies. As shown in Fig. 10, the lowest temperature for heating design in Beijing is 11.5 8C for the frequency level of 2.5%, which is lower than that of 5.0% by about 18. The temperature for cooling design for Beijing is shown in Fig. 11. The highest temperature is 35.5 8C, which is higher than that of 5.0% by 0.78. The temperatures for both heating and cooling are higher than that by the on-going standard [15], where the design temperatures for heating and cooling are 12 and 33.2 8C for Beijing, respectively.
Fig. 11. Temperature for cooling design with the frequency level of 2.5% and 5.0% (Beijing).
(1) A set of typical meteorological year (TMY) data has been developed for 57 Chinese locations. The methodology for the TMY includes the estimation of solar radiation, separation of global radiation into direct and diffuse components, selection of typical meteorological months, interpolation of temperature and humidity from 3 h to 1 h. (2) It is verified that the air-conditioning load by simulations using the historical weather data can be approximated by using the 24 h data consisting of the average of each meteorological element. This kind of 24 h data is called the typical meteorological days or TMD. The TMDs of the 57 locations have been developed and included in the database. (3) A set of design temperature and humidity for the 57 locations has been developed based on the frequency levels of temperature and humidity over the period of 1982–1997. The database has been put into a CD and published [9]. Acknowledgment The author would like to thank Mr. Joe Huang, Lawrence Berkeley National Laboratory, the USA for his valuable advice and support to this study. References [1] Q. Zhang, K. Asano, Development of the typical weather data for the main Chinese cities, Journal of Architectural Planning and Engineering 543 (2001) 65–69, in Japanese. [2] Q. Zhang, J. Huang, S. Lang, Development of typical year weather data for Chinese locations, ASHRAE Transactions 108 (Part 2) (2002) 1063–1075. [3] Q. Zhang, K. Asano, T. Hayashi, Study on the typical weather day and the weather data for building thermal design in China, Journal of Architectural Planning and Environmental Design of AIJ 555 (2002) 51–68, in Japanese. [4] National Climatic Data Center (NCDC) 1998. International Surface Weather Observations 1982–1997, Volumes 1 through 5, jointly produced by NCDC, National Oceanic and Atmospheric Administration, US Dept. of Commerce, Asheville NC, and the Air Force Combat Climatology Center (AFCCC), US Dept. of Air Force, Asheville NC. [5] L. Cui, Y. Matsuo, Y. Sakamoto, H. Nimiya, The Prediction of Solar Radiation and its Application, Summaries of Technical Papers of Annual Meeting, Architectural Institute of Japan, Osaka, Japan, 1996, in Japanese. [6] Q. Zhang, C. Lou, H. Yang, A new method to separate horizontal solar radiation into direct and diffuse components, in: Proceedings of the ISESPacific, 2004, pp. 682–688. [7] W.J. Parton, G.S. Innis, Some Graphs and Their Functional Forms. Technical Report No.153, National Resources Ecology Laboratory, 1972, p. 21. [8] F.C. Winkelmann, B.E. Birdsall, W.F. Buhl, K.L. Ellington, A.E. Erdem, J.J. Hirsch, S. Gates, DOE-2 Supplement, Version 2.1E, LBL-234949, Lawrence Berkeley Laboratory, Berkeley CA, USA, 1993.
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[9] Q. Zhang, J. Huang, Chinese Typical Meteorological Database for Buildings, China Machine Press, 2004. [10] National Renewable Energy Laboratory: Typical Meteorological Year. [11] Y. Matsuo, et al., A Study on the typical weather data, Journal of the Society of Heating, Air-conditioning and Sanitary Engineering of Japan 48 (7) (1974).
[12] Architectural Institute of Japan, Expanded AMeDAS Weather Data, Maruzen Co. Ltd., 2000. [13] J.M. Finkelstein, R.E. Schafer, Improved goodness of fit test, Biometrika 58 (3) (1971) 641–645. [14] ASHRAE, 1989 ASHRAE Handbooks Fundamentals, 1989. [15] China State Standard: Design Standard for Heating, Ventilating and Airconditioning, GBJ19-87, 2001.
Development of the typical meteorological database for Chinese locations Qingyuan Zhang * Tsukuba University of Technology, 4-3-15 Amakubo, Tsukuba 305-0005, Japan
Abstract The typical meteorological database for 57 Chinese locations was developed for building simulations and air-conditioning design. The database consists of three parts: the typical meteorological years (TMY), the typical meteorological days (TMD) and the design temperature and humidity (DTH). The typical meteorological year (TMY) is the main part of the database. Because there are not data on solar radiation in the observations, a method to estimate solar radiation with dry bulb temperature difference, relative humidity, total cloud cover and wind speed was developed. Methodologies of interpolations were developed to produce 1 h data with the 3 h data. The global solar radiation on the horizontal surface was separated into direct and diffuse components with the Gompertz function. The typical meteorological day (TMD) consists of the monthly average values of dry bulb temperature, solar radiation, relative humidity, etc. for each hour of the day. The design temperature and humidity (DTH) was developed for the purpose of air-conditioning design. The frequencies of 2.5% and 5.0% were selected to decide the design temperature and humidity for the 57 Chinese locations. # 2006 Elsevier B.V. All rights reserved. Keywords: Typical meteorological year; Typical meteorological day; Design temperature and humidity; Observation; Interpolation
1. Introduction Weather is one of the primary determinants of indoor thermal conditions and space conditioning energy use. There are two kinds of weather data in building energy calculations: one is the data for hourly simulations and another is for equipment design. Usually the former consists of 8760 h data forming an average year; the latter consists of extreme hours in summer or winter. The weather data for hourly simulations in China have not been developed systematically until recently. The main reason for the delays in the development of weather data for building simulations is that most of the observation data were not in an electronic format, especially before the 1990s, making data processing difficult and expensive. The typical meteorological database for 57 Chinese locations was developed for building simulations and airconditioning design. The database consists of three parts: (1) the typical meteorological years (TMY); (2) the typical meteorological days (TMD) which can be considered a kind
* Tel.: +81 29 858 9376; fax: +81 29 858 9376. E-mail address: [email protected]. 0378-7788/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2006.04.003
of simplified TMY; and (3) the design temperature and humidity (DTH). The typical meteorological year (TMY) is the main part of the database [1,2]. The TMY is based on observations with 3 h intervals. Because there are not data on solar radiation in the observations, a method to estimate solar radiation with dry bulb temperature difference, relative humidity and total cloud cover was developed. The typical meteorological months were selected by how close the variables of a month are to the average. In order to produce 1 h data with the 3 h data, methodologies of interpolations were developed. The global solar radiation on the horizontal surface was separated into direct and diffuse components with the Gompertz function. The typical meteorological day (TMD) consists of the monthly average values of dry bulb temperature, relative humidity, etc. for each hour of the day [3]. It was verified that the air-conditioning load of an apartment house can be approximated by using the average meteorological elements like dry bulb temperature, humidity, solar radiation, wind speed and cloud amount. Therefore the monthly average meteorological data for each hour were defined as the typical weather days for the month. The typical weather days for the 57 locations in China were given.
Q. Zhang / Energy and Buildings 38 (2006) 1320–1326
The design temperature and humidity (DTH) was developed for the purpose of air-conditioning design. It was based on the frequency level of a specific temperature over the past 16 years. The frequencies of 2.5% and 5.0% were selected to decide the design temperature and humidity for the 57 Chinese locations. 2. Typical meteorological year The typical meteorological year is the main part of the Chinese typical meteorological database developed in this study because it is indispensable in hourly building simulations. The development of Chinese TMYs began in 1999, and the TMYs for 57 Chinese locations have been completed. The TMYs are based on the observations from International Surface Weather Observations (ISWO database) [4]. Because there are not any data about solar radiation, a model to predict radiation is necessary. In order to estimate the hourly solar radiation for the 57 Chinese locations, it is necessary to find relations between solar radiation and other variables. Cui et al. [5] showed the correlation between solar radiation and temperature change from previous hours, the amount of cloud cover, relative humidity and wind speed using observations with 6 h intervals, but the accuracy was not sufficient due to the large time intervals. It is possible to improve the accuracy using the data from ISWO database because the observation intervals are smaller (3 h) than that used by Cui et al. Also it is possible for the author to develop models that are applicable to more locations because there are solar data included in the OSR database (observed solar data in 1993 for 24 major cities of China). In order to select parameters in the models to estimate solar radiation, the relations between solar radiation and other
1321
variables like the amount of cloud cover, temperature changes, and relative humidity were examined using the observations of 24 locations from the ISWO and OSR databases. Multiple regressions were carried out to establish models to estimate hourly solar radiation. Dry-bulb temperature changes, total amount of cloud cover and relative humidity were adopted as variables as shown in Eq. (1): 2 CC CC Ih ¼ I0 sin h C0 þ C1 þ C2 þ C3 10 10 ðun un3 Þ þ C4 f C5 =k (1) where Ih is the estimated global solar radiation on the horizontal surface in W/m2; I0 is the solar constant; CC is the amount of cloud cover in tenths; un and un3 are dry-bulb temperature at hours n and n 3, respectively; h is the sun’s altitude and the term (I0sin h) means solar radiation on the horizontal surface in the outer space; f is the relative humidity in %; C1 . . . C5 and k are regression coefficients, the values of which differ from place to place as shown in Table 1. The coefficients of correlation R for all the 24 locations are between 0.91 and 0.97 with maximum error of 137 W/m2 except for Lhasa. The error for Lhasa is larger than that for other locations but the total amount of solar radiation in Lhasa is also much stronger than other locations. The relationship between the observational solar radiation and estimations from Eq. (1) for Beijing is shown in Fig. 1 as an example. The correlation coefficient R is 0.97, and the root mean square error (RMSE) of the estimation is 80 W/m2, which implies that Eq. (1) can be used to estimate the hourly horizontal solar radiation with good accuracy in Beijing. Based
Table 1 Coefficients in Eq. (1) for different cities Cities
C0
C1
C2
C3
C4
C5
k
R
RMSE
Beijing Changchun Changsha Chengdu Fuzhou Guangzhou Guiyang Hangzhou Harbin Hefei Jinan Kunming Lhasa Lanzhou Nanchang Nanning Nanjing Shenyang Tianjin Wuhan Xian Xining Yinchuan Zhengzhou
0.6584 0.8412 0.7085 0.3645 0.7960 0.6050 0.4688 0.4378 1.0235 0.8084 0.6497 0.4817 0.6996 0.3545 0.7638 0.4989 0.7586 0.8199 0.7297 0.7395 0.5283 0.3856 0.5831 0.7085
0.4864 0.4406 0.7065 0.4800 0.7279 0.5755 0.4750 0.8395 0.5162 0.6724 0.4679 0.2936 0.0929 0.6723 0.8086 0.7322 0.5914 0.6304 0.5113 0.7426 0.6062 0.6237 0.4261 0.5092
0.6647 0.6853 0.9413 0.6335 0.9365 0.7893 0.7223 1.1174 0.6877 0.8846 0.6317 0.5768 0.2399 0.8564 1.0198 0.9156 0.7919 0.8533 0.7432 0.9817 0.7861 0.8658 0.7089 0.7069
0.0203 0.0021 0.0230 0.0495 0.0200 0.0278 0.0321 0.0408 0.0056 0.0189 0.0242 0.0403 0.0162 0.0430 0.0348 0.0402 0.0181 0.0035 0.0118 0.0276 0.0353 0.0376 0.0282 0.0165
0.0039 0.0047 0.0038 0.0011 0.0052 0.0030 0.0008 0.0001 0.0063 0.0051 0.0038 0.0003 0.0026 0.0007 0.0048 0.0020 0.0050 0.0051 0.0036 0.0043 0.0024 0.0015 0.0006 0.0037
36.6114 40.2260 42.2020 40.5660 35.7491 36.8362 38.3084 41.3412 35.0285 35.6732 27.2746 43.9962 56.6359 40.8254 35.5071 42.7619 31.8024 39.2128 38.6689 37.0186 36.6207 41.7887 37.4911 37.0826
0.9300 0.8868 0.8747 0.8250 0.9112 0.8998 0.8743 0.8702 0.8924 0.9197 0.9178 0.8745 0.8811 0.8954 0.9192 0.8687 0.9350 0.9047 0.9110 0.9123 0.9070 0.8910 0.9237 0.9250
0.97 0.94 0.94 0.92 0.94 0.95 0.93 0.91 0.93 0.96 0.96 0.93 0.94 0.95 0.96 0.93 0.97 0.96 0.97 0.96 0.96 0.94 0.96 0.97
80 116 84 103 96 87 96 115 136 88 88 137 165 110 90 114 76 98 106 90 91 122 105 86
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Fig. 1. Correlation between observed and estimated solar radiation by Eq. (1) (Beijing 1993). Fig. 2. Comparison between the values of Kn from observation and Eq. (2).
on these figures and Table 1, it is reasonable to conclude that Eq. (1) can be used to estimate hourly global solar radiation on the horizontal surface for the 24 Chinese locations. The coefficients for other locations were decided referring to that for the locations with similar longitude, latitude and elevation. In simulating the thermal performance of buildings, solar radiation in the normal direction and on declined surfaces is necessary. Therefore a procedure to separate the horizontal radiation to direct beam and diffuse component was developed. Using observations from six Chinese locations (Beijing, Chengdu, Lanzhou, Lhasa, Shanghai and Urumqi) and one Japanese location (the Aerological Observatory, Tsukuba), a method of separating horizontal radiation into direct and diffuse components was suggested by Zhang et al. [6]. The direct beam transmittance Kn can be calculated with the following equation in the form of Gompertz function [7]: Kn ¼
A4 Kt
A3 A A1 A2 2
(2)
The comparison between the values of Kn from observation and Eq. (2) is shown in Fig. 2, which shows that Eq. (2) fits the observations with minor errors. Because the interval of the ISWO data is 3 h and 1 h data are necessary in most computer programs like DOE-2 [8], procedures of interpolating 3 h data to 1 h data were developed using the Fourier series. The following equations were used in the interpolation: f ðtÞ ¼
A1 ¼ 0:1556 sin h þ 0:1028 sin h þ 1:3748
(3)
A2 ¼ 0:7973 sin2 h þ 0:1509 sin h þ 3:035
(4)
A3 ¼ 5:4307 sin h þ 7:2182
(5)
A4 ¼ 0:2990
(6)
The direct normal radiation can be calculated using the following equation: In ¼ Kn I0
(7)
The diffuse component at the horizontal surface can be obtained by the following equation: Id ¼ Ih In sin h
(8)
where Id is the diffuse horizontal radiation at the earth’s surface in W/m2; In is the direct normal at the earth’s surface in W/m2; the direct beam transmittance Kn is expressed as: Kn ¼
In sin h In ¼ I0 sin h I0
(9)
an sinðnvtÞ þ b0 þ
n¼1
M X
bn cosðnvtÞ
(10)
n¼1
an ¼
7 1X npk f ðkÞ sin 4 k¼0 4
(11)
bn ¼
7 1X npk f ðkÞ cos 4 k¼0 4
(12)
b0 ¼
7 1X f ðkÞ 8 k¼0
(13)
where 2
M X
Because of the assumption in the Fourier series that the temperature in the beginning of a day is equal or close to that in the end of the day, a single interpolation over a day is not enough. Another procedure of interpolation using the data of 14 through 11 of the next day was carried out to improve the connection between 2 days. A comparison of the results from the observations, a single Fourier interpolation and the double Fourier interpolation is shown in Fig. 3. There is a sudden change between July 23 and July 24 in the single Fourier interpolation. The connection between the days is smooth with the double Fourier interpolation. The interpolation of dew point temperature, as well as wind speed, cloud cover, was also carried out with the double Fourier interpolation. A computer subroutine for the double Fourier interpolation can be found in a reference by Zhang and Huang [9]. The TMY consists of 12 typical months (TMM) that are selected from the observations. The TMM is defined as the month that is neither cooler nor warmer than the average. There
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between the neighboring months. The following equation was used to smooth the joining values of temperature or humidity: ui ¼
12 i 0 i u þ u00 12 i 12 i
(15)
where ui shows the temperature after smoothing; u0i and u00 are the values of temperature of the former and the latter months, respectively. 3. Typical meteorological days
Fig. 3. Comparison of results from different interpolations (Tokyo, 23–24 July 2003).
have been different methods in selecting TMMs [10–12]. In this study, the procedure to select TMMs can be described as follows: (1) Select the months whose average dry-bulb temperature, dew-point temperature, solar radiation and wind speed are within the range of 0.6 times the standard deviation. If only one candidate is left after Step (1), the only candidate is selected as the TMM. If there is no candidate left, go to Step (2). If there is more than one candidate after Step (1), go to Step (4). (2) Select the months whose average dry-bulb temperature, dew-point temperature, solar radiation and wind speed are within the range of 0.8 times the standard deviation. If only one candidate is left after Step (2), the only candidate is selected as the TMM. If there is no candidate left, go to Step (3). If there is more than one candidate after Step (2), go to Step (4). (3) Select the months whose average dry-bulb temperature, dew-point temperature, solar radiation and wind speed are within the range of the standard deviation. If only one candidate is left after Step (3), the only candidate is selected as the TMM. If there is more than one candidate left after Step (3), go to Step (4). (4) Compare the WS values of the remaining months, and select the month whose WS is the smallest as the TMM. The value of WS is calculated as follows: X WS ¼ ðwi FSi Þ
The typical meteorological years can serve as an important tool for building simulations, but the TMY has the following problems: (1) the average of each meteorological element does not always equal to the average of past years; (2) the characteristics of a location cannot be imagined only by the TMY. To solve these problems, it is desirable to find a kind of simple data that can characterize the climate of a location. One of the methods is to make a set of 24 h data to representative a month by averaging each meteorological element. In order to verify that this kind of weather data may characterize the climate of a location, two kinds of simulations were carried out using an apartment house model (Fig. 4): one is simulations with historical hourly data; another is simulations with monthly average meteorological elements including temperature, humidity, solar radiation, and wind speed for each hour of the day. The correlations between the results from these two kinds of simulations are shown in Figs. 5 and 6, respectively. The average heating load in January (Fig. 5) and cooling load in July (Fig. 6) from hourly historical weather data and from monthly average meteorological elements agree with each other well. These facts imply that the 24 h data consisting of average meteorological elements can be representative of
(14)
where FSi means the Finkelstein–Shafer (FS) statistic [13]. The smaller the FSi, the closer the structure of a variable will be to the average year. The weights wi applied to the different climate elements are the same as those used by NCDC in the development of the TMYs [10]. Usually the neighboring months are from different years, therefore sudden changes in temperature or humidity may occur
Fig. 4. Apartment model for simulations.
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Fig. 7. The temperature in the TMD for January, April, July and October for Beijing. Fig. 5. Relation between heating load by using historical weather data and by using average elements (January).
historical hourly weather data, at least in the field of calculating air-conditioning load. Therefore the 24 h data containing monthly average elements are defined as the typical meteorological day (or TMD) of the month. Comparing with the historical hourly data or the TMY data, the TMD contains far less data and they can characterize the climate of a location. Another advantage of the TMD is that time used for simulations with the TMD can be shortened to about one thirtieth. Figs. 7–9 show the 24 h temperature, humidity and horizontal solar radiation in January, April, July and October for Beijing. These data can be used to calculate air conditioning load of an average month, as well as to characterize the climate of a location. In Beijing, the hourly temperature in April is almost the same as that in October (Fig. 7), while the relative humidity in October is obviously higher than that in April (Fig. 8). The horizontal solar radiation in April is the strongest of the four months (Fig. 9). These advantages do not mean, however, that the TMY can be replaces by the TMD, because when daily or hourly load is concerned, TMY should be used instead of the TMD. Therefore which kind of data should be used depends upon the purposes.
Fig. 6. Relation between cooling load by using historical weather data and by using average elements (July).
Fig. 8. Relative humidity in the TMD for January, April, July and October for Beijing.
4. Design temperature and humidity The main purpose of TMYand TMD is to simulate the thermal behavior of buildings for an average year or a month, and it is not for the design of air-conditioning equipment. The design weather data should consist of extreme hours in summer or winter. One of the main methods to decide the design temperature is based on the frequency level of a specific temperature over a time period. The frequencies of 2.5% and 5.0% were selected to decide the design temperatures for the 57 Chinese locations. The ASHRAE [14] has recommended the weather data for
Fig. 9. Global horizontal solar radiation in the TMD for January, April, July and October for Beijing.
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5. Conclusions The Chinese typical meteorological database was developed and it consists of three parts: the typical meteorological year (TMY); the typical meteorological day (TMD); and the design temperature and humidity (DTH). The main conclusions from this study are:
Fig. 10. Temperature for heating design with the frequency level of 2.5% and 5.0% (Beijing).
some Chinese locations, but there are two problems: (1) only one dry-bulb temperature and one wet-bulb temperature were given for each location, while 24 h weather data are necessary for some calculation methods; and (2) the number of Chinese cities whose weather data are given for air-conditioning design is too small therefore more studies are needed about these and other locations. In this study, the frequencies of dry-bulb temperature and relative humidity were computed for the 57 Chinese locations over the period of 1982–1997 and cumulative frequency curves were drawn for each month and each location. For cooling design temperature, frequencies were calculated over the period of June through August; while for the heating design, frequency calculation was carried out over the period of December through February. Twenty-four hours data were given for the heating design for the 57 locations. Usually there is a gap of about 18 between 2.5% and 5% frequencies. As shown in Fig. 10, the lowest temperature for heating design in Beijing is 11.5 8C for the frequency level of 2.5%, which is lower than that of 5.0% by about 18. The temperature for cooling design for Beijing is shown in Fig. 11. The highest temperature is 35.5 8C, which is higher than that of 5.0% by 0.78. The temperatures for both heating and cooling are higher than that by the on-going standard [15], where the design temperatures for heating and cooling are 12 and 33.2 8C for Beijing, respectively.
Fig. 11. Temperature for cooling design with the frequency level of 2.5% and 5.0% (Beijing).
(1) A set of typical meteorological year (TMY) data has been developed for 57 Chinese locations. The methodology for the TMY includes the estimation of solar radiation, separation of global radiation into direct and diffuse components, selection of typical meteorological months, interpolation of temperature and humidity from 3 h to 1 h. (2) It is verified that the air-conditioning load by simulations using the historical weather data can be approximated by using the 24 h data consisting of the average of each meteorological element. This kind of 24 h data is called the typical meteorological days or TMD. The TMDs of the 57 locations have been developed and included in the database. (3) A set of design temperature and humidity for the 57 locations has been developed based on the frequency levels of temperature and humidity over the period of 1982–1997. The database has been put into a CD and published [9]. Acknowledgment The author would like to thank Mr. Joe Huang, Lawrence Berkeley National Laboratory, the USA for his valuable advice and support to this study. References [1] Q. Zhang, K. Asano, Development of the typical weather data for the main Chinese cities, Journal of Architectural Planning and Engineering 543 (2001) 65–69, in Japanese. [2] Q. Zhang, J. Huang, S. Lang, Development of typical year weather data for Chinese locations, ASHRAE Transactions 108 (Part 2) (2002) 1063–1075. [3] Q. Zhang, K. Asano, T. Hayashi, Study on the typical weather day and the weather data for building thermal design in China, Journal of Architectural Planning and Environmental Design of AIJ 555 (2002) 51–68, in Japanese. [4] National Climatic Data Center (NCDC) 1998. International Surface Weather Observations 1982–1997, Volumes 1 through 5, jointly produced by NCDC, National Oceanic and Atmospheric Administration, US Dept. of Commerce, Asheville NC, and the Air Force Combat Climatology Center (AFCCC), US Dept. of Air Force, Asheville NC. [5] L. Cui, Y. Matsuo, Y. Sakamoto, H. Nimiya, The Prediction of Solar Radiation and its Application, Summaries of Technical Papers of Annual Meeting, Architectural Institute of Japan, Osaka, Japan, 1996, in Japanese. [6] Q. Zhang, C. Lou, H. Yang, A new method to separate horizontal solar radiation into direct and diffuse components, in: Proceedings of the ISESPacific, 2004, pp. 682–688. [7] W.J. Parton, G.S. Innis, Some Graphs and Their Functional Forms. Technical Report No.153, National Resources Ecology Laboratory, 1972, p. 21. [8] F.C. Winkelmann, B.E. Birdsall, W.F. Buhl, K.L. Ellington, A.E. Erdem, J.J. Hirsch, S. Gates, DOE-2 Supplement, Version 2.1E, LBL-234949, Lawrence Berkeley Laboratory, Berkeley CA, USA, 1993.
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[9] Q. Zhang, J. Huang, Chinese Typical Meteorological Database for Buildings, China Machine Press, 2004. [10] National Renewable Energy Laboratory: Typical Meteorological Year. [11] Y. Matsuo, et al., A Study on the typical weather data, Journal of the Society of Heating, Air-conditioning and Sanitary Engineering of Japan 48 (7) (1974).
[12] Architectural Institute of Japan, Expanded AMeDAS Weather Data, Maruzen Co. Ltd., 2000. [13] J.M. Finkelstein, R.E. Schafer, Improved goodness of fit test, Biometrika 58 (3) (1971) 641–645. [14] ASHRAE, 1989 ASHRAE Handbooks Fundamentals, 1989. [15] China State Standard: Design Standard for Heating, Ventilating and Airconditioning, GBJ19-87, 2001.