Comments on “Generation of typical meteorological year for different climates of China” [Energy, 35 (2010) 1946–1953]

Energy 36 (2011) 6285e6288

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Comments on “Generation of typical meteorological year for different climates of China” [Energy, 35 (2010) 1946e1953] Qingshan Xu, Haixiang Zang* School of Electrical Engineering, Southeast University, Nanjing 210096, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 May 2011 Received in revised form 30 June 2011 Accepted 6 August 2011 Available online 6 September 2011

This is the comment to the article “Generation of typical meteorological year for different climates of China” [Energy, 35 (2010) 1946e1953]. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Typical meteorological year Cumulative distribution function Finkelstein-Schafer statistical method

1. The detailed description of the issue in the article by Jiang This paper comments on an article written by Jiang [1]. The article compared the individual monthly cumulative frequency distributions (CDFs) with the long term CDFs and the similar comparisons were analyzed and investigated by Marion and Urban [2], Wilcox and Marion [3], and Lee et al. [4]. However, the results of [2e4] are different from those of [1]. In Ref. [1], taken Fig. 1 as an example, it can be seen that the maximum values of the mean drybulb temperature CDF in January (e.g. Worst (2000, Jan)) are lower than 1 rather than equal to 1. Also, Figs.2e4 in Ref. [1] have the same conditions. However, the maximum values of the CDF for Figure.A-1 [2], Fig. 2-1 in Ref. [3] and Fig. 2 in Ref. [4] are equal to 1. The comparisons of the mean dry-bulb temperature CDFs and daily global solar radiation CDFs between the short term and the long term are calculated by the Eq. (1) and (2) in Ref. [1]. In addition, the Eq. (1) in Ref. [1] are the same as in the references [5e8]. The erroneous origin is from the Eq. (1) and the Eq. (1) in the article [1] is given in the following.

8 < 0 Sn ðxÞ ¼ ðk  0:5Þ=n : 1

for for for

x < x1 xk  x < xkþ1 x  xn

DOI of original article: 10.1016/j.energy.2011.07.022. * Corresponding author. Tel.: þ86 13770719919; fax: þ86 25 83793371. E-mail address: [email protected] (H. Zang). 0360-5442/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.08.006

(1)

From the Eq. (1), it is obviously implied that the value of the function Sn is 1,when x  xn. However, the maximum values of the CDF for Figs.1e4 in Ref. [1] are lower than 1. Clearly, there are not typographical errors. The errors of Figs.1e4 show that there are some issues in the applications of the Eq. (1) in the paper [1]. Moreover, the main process of the Sandia method used in Ref. [1] is shown in Fig. 1. From Fig. 1, It can be concluded that the values of the Finkelstein-Schafer (FS) statistics, the weighted sum (WS) of the FS statistics, and the typical meteorological months should be changed and corrected partly if the numerical results of the CDF in the first part are altered. In other words, the results in the Tables 3e8 in the paper [1] are partly incorrect due to the numerical errors in the Bold part. 2. The generation of the typical meteorological year (TMY) The most serious error in the paper [1] is derived from the wrong applications of the Eq. (1). By applying the identical procedure of the Sandia method and the same data from the China Meteorological administration in Ref. [1], the correct results of Figs.1e4 and Tables 3e8 in Ref. [1] are given in Figs.2e5 and Tables 1e6 as follow. Using the Eqs. (1) and (2) in Ref. [1], the correct figures for comparing the short term CDF with long term CDF (daily mean drybulb temperature and daily global solar radiation) are given in Figs.2e5, respectively. Figs.2e5 show that the maximum values of the CDF are equal to 1. It also can be seen that, in general, the short

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Q. Xu, H. Zang / Energy 36 (2011) 6285e6288

Cumulative distribution function

1 long term (Jul) Best (2002,Jul) Worst (2000,Jul) TMM (1997,Jul)

0.8

0.6

0.4

0.2

0 16

18

20

22

24

26

28

30

32

34

36

Daily mean dry-bulb temperature (°C) Fig. 1. The flow chart of the method used in the paper [1] (the process in the red part has some numerical errors). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Cumulative distribution function

1

0.8

long term (Jan) Best (2003,Jan) Worst (2000,Jan) TMM (1999,Jan)

0.6

0.4

0.2

0 -14

-12

-10

-8 -6 -4 -2 0 2 Daily mean dry-bulb temperature (°C)

4

6

Fig. 2. Comparison of short term CDF with long term CDF (daily mean dry-bulb temperature) in January for Beijing.

term CDFs appearing the typical “S” type distribution follow quite closely their long term counterparts. From Fig. 2, the CDF of mean dry-bulb temperature (MDBT) for January 2003 is most similar to the long term CDF for January, while the CDF of MDBT for January

Fig. 4. Comparison of short term CDF with long term CDF (daily mean dry-bulb temperature) in July for Beijing.

2000 is least similar. Also, the CDF of MDBT for TMM of 1999 is between the two. Likewise, from Fig. 3, the CDF of daily global solar radiation (DGSR) for January 2004 is closest to the long term CDF for January, while the DGSR CDF for January 2001 is most dissimilar. In Fig. 4, the MDBT CDF for July 2002 is most similar to the long term MDBT CDF, while the MDBT CDF for July 2000 is least similar and the MDBT CDF for TMM of July 1997 is found between the two. Similarly, from Fig. 5, the DGSR CDF for TMM of July 1997 is found between the DGSR CDF for July 2004 and the DGSR CDF for July 1996. Moreover, there are some errors in the results of Tables 3e8 in Ref. [1]. Based on the Eqs. (1)e(4) and weighting factors in Ref. [1], the corresponding and true results are listed in the following Tables 1e6. Table 1 and Table 2 show the Finkelstein-Schafer (FS) statistics of the MDBT and DGSR for Beijing station, respectively. From Tables 1 and 2, it is obvious that the values of FS statistics constantly alter from one month to another and change from one index to another. This agrees with other researchers [1,9]. The weighted sums (WS) of the FS statistics for Beijing station and the five candidate years of each month (bold characters) are listed in Table 3. Table 4 presents the root mean square difference (RMSD) results of DGSR for Beijing stations and the smallest values of RMSD for each calendar month (bold numbers). In the meantime, the minimum RMSD values for each month vary between 1.7268 MJ/m2

1

0.8

long term (Jan) Best (2004,Jan) Worst (2001,Jan) TMM (1999,Jan)

Cumulative distribution function

Cumulative distribution function

1

0.6

0.4

0

0.8

0.6

0.4

0.2

0.2

0

long term (Jul) Best (2004,Jul) Worst (1996,Jul) TMM (1997,Jul)

2

4

6

8

10

12

14

Daily global solar radiation (MJ/m 2 ) Fig. 3. Comparison of short term CDF with long term CDF (daily global solar radiation) in January for Beijing.

0

0

2

4

6

8

10 12 14 16 18 20 22 24 Daily global solar radiation (MJ/m 2 )

26

28

30

32

Fig. 5. Comparison of short term CDF with long term CDF (daily global solar radiation) in July for Beijing.

Q. Xu, H. Zang / Energy 36 (2011) 6285e6288

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Table 1 FS statistics of mean air temperature for Beijing station.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

0.191 0.123 0.068 0.024 0.064 0.114 0.120 0.046 0.160 0.124 0.190 0.065

0.083 0.165 0.120 0.096 0.074 0.076 0.163 0.173 0.068 0.075 0.065 0.161

0.079 0.036 0.092 0.033 0.087 0.073 0.128 0.164 0.203 0.058 0.048 0.115

0.093 0.088 0.062 0.058 0.112 0.125 0.095 0.049 0.148 0.100 0.103 0.070

0.099 0.088 0.206 0.049 0.119 0.038 0.126 0.047 0.132 0.048 0.073 0.053

0.300 0.236 0.045 0.053 0.049 0.159 0.280 0.038 0.111 0.091 0.166 0.053

0.198 0.177 0.050 0.037 0.181 0.087 0.038 0.052 0.063 0.079 0.085 0.190

0.278 0.172 0.175 0.073 0.090 0.116 0.035 0.056 0.061 0.152 0.121 0.210

0.035 0.050 0.103 0.043 0.059 0.026 0.146 0.103 0.044 0.060 0.120 0.112

0.044 0.149 0.058 0.117 0.028 0.029 0.117 0.095 0.067 0.064 0.123 0.117

Table 2 FS statistics of global solar radiation for Beijing station.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

0.086 0.065 0.030 0.058 0.053 0.073 0.056 0.101 0.056 0.023 0.120 0.100

0.067 0.107 0.038 0.046 0.076 0.031 0.102 0.171 0.052 0.083 0.047 0.059

0.063 0.048 0.079 0.080 0.082 0.061 0.062 0.056 0.113 0.252 0.089 0.069

0.072 0.044 0.027 0.110 0.042 0.041 0.035 0.124 0.045 0.044 0.037 0.064

0.065 0.095 0.136 0.061 0.036 0.138 0.055 0.041 0.094 0.029 0.045 0.086

0.078 0.070 0.150 0.079 0.064 0.103 0.083 0.053 0.051 0.083 0.082 0.043

0.105 0.128 0.090 0.023 0.064 0.083 0.041 0.078 0.065 0.113 0.062 0.045

0.043 0.048 0.067 0.052 0.042 0.106 0.045 0.066 0.024 0.046 0.117 0.163

0.028 0.077 0.116 0.054 0.141 0.037 0.061 0.052 0.108 0.034 0.155 0.063

0.024 0.054 0.046 0.055 0.055 0.030 0.025 0.052 0.052 0.032 0.049 0.066

Table 3 Weight sums “WS” of FS statistics (the bold numbers show the lowest five values in the month).

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

0.0150 0.0094 0.0066 0.0088 0.0082 0.0095 0.0073 0.0102 0.0086 0.0054 0.0167 0.0119

0.0102 0.0178 0.0085 0.0079 0.0090 0.0064 0.0130 0.0181 0.0072 0.0083 0.0068 0.0103

0.0077 0.0049 0.0091 0.0080 0.0069 0.0088 0.0076 0.0090 0.0130 0.0209 0.0101 0.0092

0.0071 0.0059 0.0071 0.0150 0.0087 0.0090 0.0088 0.0096 0.0078 0.0075 0.0073 0.0079

0.0084 0.0107 0.0175 0.0080 0.0085 0.0099 0.0082 0.0051 0.0104 0.0044 0.0064 0.0081

0.0154 0.0119 0.0118 0.0080 0.0066 0.0130 0.0141 0.0068 0.0085 0.0091 0.0112 0.0060

0.0165 0.0184 0.0091 0.0050 0.0110 0.0100 0.0057 0.0078 0.0085 0.0135 0.0087 0.0098

0.0115 0.0103 0.0108 0.0064 0.0077 0.0118 0.0058 0.0067 0.0064 0.0084 0.0125 0.0200

0.0049 0.0093 0.0149 0.0068 0.0143 0.0058 0.0092 0.0114 0.0108 0.0051 0.0146 0.0086

0.0055 0.0098 0.0068 0.0085 0.0071 0.0056 0.0072 0.0097 0.0080 0.0065 0.0090 0.0094

Table 4 Root mean square difference RMSD of daily global solar radiation (MJ/m2) for the five candidate years at Beijing station (the bold number shows the lowest value in the month). Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2) year RMSD(MJ/m2)

1997 2.4953 1995 2.2780 1995 4.4710 1996 4.8366 1995 7.5239 1996 6.6654 1995 9.2071 1997 6.7161 1996 5.6632 1995 4.5107 1996 2.8452 1997 2.3909

1998 3.3221 1997 3.6556 1996 4.9050 1997 5.1318 1997 7.0734 1997 5.0138 1997 6.6645 1999 6.5185 1998 5.6076 1998 4.6564 1998 3.7893 1998 1.7525

1999 2.4279 1998 3.8652 1997 4.4434 2001 6.4283 2000 6.4233 1998 6.8708 2001 6.8883 2000 5.7090 2000 5.4574 1999 4.2607 1999 2.7478 1999 2.3109

2003 2.8014 2003 4.1421 1998 5.1030 2002 7.7281 2002 5.6551 2003 6.5131 2002 7.2903 2001 5.7295 2002 6.2109 2003 4.5998 2001 2.6562 2000 1.7623

2004 2.6420 2004 3.2797 2004 3.6706 2003 7.0622 2004 8.6025 2004 7.3812 2004 7.5167 2002 4.9258 2004 6.6987 2004 4.2121 2004 2.5305 2003 1.7268

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Table 5 Results of the TMY selection of Beijing (the Bold number shows the changes from the paper [1]). Month Year

Jan 1999

Feb 1995

Mar 2004

Apr 1996

May 2002

Jun 1997

Jul 1997

Aug 2002

Sep 2000

Oct 2004

Nov 2004

Dec 2003

Table 6 Results of the TMY selection of the eight station of China (the Bold number shows the changes from the paper [1]). Station

Month Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Nov

Dec

Haerbin Lanzhou Beijing Wuhan Kunming Guangzhou Wulumuqi Lasa

2003 2000 1999 1997 2003 1996 1999 2002

1998 2003 1995 1995 1998 2003 2001 1997

2004 2000 2004 1995 1998 2003 2004 2002

2004 2000 1996 2001 2003 1999 2004 2001

2001 1995 2002 1996 1995 1995 2000 1998

1998 1998 1997 1998 2004 2002 1995 2002

2000 2002 1997 1995 1999 1998 1995 1999

1996 2000 2002 1995 1997 2002 1995 2004

1999 1998 2000 2004 2004 1998 1995 1999

1999 1998 2004 2004 2000 1999 2002 2000

2003 1999 2004 2004 1998 1999 1999 2001

1996 1999 2003 2000 2000 2004 1998 2001

(December) and 6.6645 MJ/m2 (July). The month with smallest RMSD is selected as the TMM and the selected 12 typical meteorological months (TMMs) for Beijing stations are tabulated in Table 5. In Table 5, it can be found that the 12 TMMs are mainly spread in the latter three years in the period of 1995e2004. Six TMMs are chosen from 2002 (2 months), 2003 (1 month) and 2004 (3 months), respectively. In particular, the frequency occurrence of the 2004 is up to 25%. In addition, Table 6 shows the correct results of the TMY selection for the eight stations of China in Ref. [1] and some changes from the paper [1] (Bold numbers). The correctness and accuracy of the TMY database is significant and essential for the building simulation. Also, the weather data files, particularly the TMY weather database, are important reference data for many engineering applications such as architecture, meteorology and renewable energy system [4,10e13]. 3. Conclusions As can be observed from the above arguments, there are some errors in the paper by Jiang. The correct typical meteorological years for eight stations in different climates zones of China are formed based on the Sandia method and the 10 years recorded weather data. It is also found that the DGSR and MDBT CDFs of the TMMs tend to follow their long term counterparts quite well. Acknowledgments The research is financially supported by National Natural Science Foundation of China (Program No. 50907010) and Research and Innovation Project for College Postgraduates of Jiangsu Province (Program No. CXLX11_0112). The authors would also like to

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