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Development of TMY database in Northeast China for solar energy applications/TMM duomenu bazes siaures Kinijoje sukurimas saules energijos programoms.

Introduction

To relieve the dual pressure from rising energy demand and growing environmental problems, renewable energy sources like solar energy are more favored. In this respect, solar radiation data, particularly typical solar radiation data, are the most basic and important parameters in many solar energy applications.

In the past, several approaches for generating TMYs have been proposed. These methods are similar-the main differences lie in the number of daily indices (weather parameters) to be included and their assigned weightings [1]. In the paper authored by Hall et al. [2], 13 meteorological indices were examined and 4 of the 13 indices were of very little importance so zero weightings were given to them. Said and Kadry [3] analyzed and researched seven weather indices and gave different weightings. Kalogirou [4] applied and selected 15 weather parameters. Moreover, Marion and Urban [5], Wilcox and Marion [6], Petrakis et al. [7] also made attempts to generate TMYs for different locations with respective weather parameters and assigned weighting factors.

In recent years, a few individual studies were performed to select the TMYs for different zones of China. Chow et al. [1] developed the typical weather year files for two neighboring cities, namely, Hong Kong and Macau. In the paper of Zhou et al. [8], typical solar radiation years and typical solar radiation data for 30 meteorological stations of China were produced only using the long-term daily global solar radiation records. Jiang [9] generated TMYs only for eight typical cities representing different climates of China, using nine weather parameters. Although a few attempts have been carried out on this subject, the work is going on or immature for China.

In this paper, in view of the actual situation in China, eight meteorological indices and novel assigned weighting factors are chosen and proposed in the procedure of forming TMY data. Based on the latest and accurate long term weather data and novel weighting factors, this paper generates the TMYs of eight cities for three provinces of Northeast China.

Region applied and data used

In China, the related weather data are recorded and managed by China meteorological stations. Attributing to new observation instrument, the relative errors of global solar radiation measured data in China meteorological stations are changed from [+ or -] 10% to [+ or -] 0.5% since 1993. The measured weather data at the eight stations are obtained over the periods between 1994 and 2009 in this study. The relevant information for the eight stations in the northeast three provinces of China is shown in Table 1.

Method used

The Typical meteorological year (TMY) method, which was developed by Sandia National Laboratories, is an empirical methodology for combining 12 typical meteorological months (TMMs) from different years to form a complete year. The process adopted to select the 12 typical weather months is illustrated as follow:

According to the Finkelstein-Schafer (FS) statistic [10], the cumulative distribution function (CDF) for each weather index x, which is a monotonic increasing function, is formulated by a function CDF(x):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1)

where n is the total number of elements; i is the rank order number (i = 1, 2, 3, ..., n-1). From its definition, CDF(x) is a monotonically increasing step function with steps of sizes 1/n occurring at [x.sub.i] and is bounded by 0 and 1.

The FS statistic is calculated for each of the weather index by the following equation

[FS.sub.x] (y, m) = 1/N [N.summation over (i)][[delta].sub.i], (2)

where [[delta].sub.i] is the absolute difference between the long-term CDF of the month and one year CDF for the same month at [x.sub.i] (i = 1, 2, 3, ..., n-1); N is the number of daily readings of the month (e.g. for January, N=31).

Considering the characteristics of solar energy systems, eight weather indices are considered in this paper. These indices are maximum, minimum and mean dry-bulb temperature ([T.sub.max], [T.sub.min], [T.sub.ma]); minimum and mean relative humidity ([RH.sub.min], [RH.sub.ma]); maximum and mean wind velocity ([W.sub.max], [W.sub.ma]); and daily global solar radiation (DGSR). Only eight indices are used because some data (for instance, maximum relative humidity and minimum wind velocity) are not available in Northeast China.

The weighted sum (WS) of the FS statistic for the above eight weather indices is then calculated for each year. Moreover, the five years with the smallest WS values are chosen as the candidate years. The WS is defined and calculated as follows

WS(y, m) = 1/M [M.summation over (x=1)] [WF.sub.x] x [FS.sub.x](y, m), (3)

where WS(y, m) is the average weighted sum for the month m in the year y; [WF.sub.x] is the weighting factor for the xth weather index; M is the number of meteorological indices.

Various sets of the weighting factors were suggested in different references. The weighting factors in this paper, which are significant for forming TMY data, are shown in Table 2. A large weighting factor of 0.5 is assigned to the solar radiation because the criteria is mainly used for solar energy systems and the other weather variables (e.g. dry bulb temperature and relative humidity) are affected by solar radiation. For instance, in general, the higher for the solar radiation, the higher for the dry-bulb temperature.

The last step is to select the typical meteorological month (TMM) from the five candidate years. This paper applies a simper selection process introduced by Pissimanis [11]. The month with the minimum root mean square difference (RMSD) of global solar radiation is selected as the TMM. The RMSD is defined as follows

RMSD = [[n.summation over (i=1)][([H.sub.y, m, i]--[H.sub.ma]).sup.2].sup.1/2], (4)

where [H.sub.y, m, i] is the daily global solar radiation values of the year y, month m and day i; [H.sub.ma] is mean values of the long-term global solar radiation for the month m; N is the number of daily readings of the month.

Results and discussion

Based on the above TMY method and the data of the eight stations listed in Table 1, the TMYs of the eight stations in three provinces of Northeast China are formed and analyzed in the following.

To illustrate the selection procedure, the Shenyang station in Liaoning province of Northeast China is chosen as an example. In addition, to reflect the seasonal changes, January and July are selected as the typical months for winter and summer, respectively.

For each calendar month, CDFs of each index between short term and the long term are compared and calculated by (1) and (2). With mean dry-bulb temperature and daily global solar radiation as example, the comparison between the short term CDFs and the long term CDFs for Shenyang station is given in Fig. 1 and 2. It is obvious that, in general, the short term CDFs appearing the typical "S" type distribution follow quite closely their long term counterparts. In Fig. 1, using the January of Shenyang as example, the CDF of mean dry-bulb temperature (Tma) for 2003 is most similar to the long term CDF (smallest value of FS statistic), while the CDF of Tma for 2000 is least similar (largest value of FS statistic). Also, the CDF of Tma for TMM of 2009 is between the two. Likewise, From Fig. 2, the CDFs of daily global solar radiation (DGSR) for January 1997 and July 2008 are closest to the long term CDF for January (in Fig. 2(a)) and for July (in Fig. 2(b)), while the DGSR CDFs for January 2008 (in Fig. 2(a)) and July 2000 (in Fig. 2(b)) are most dissimilar. It is also found that the years considered representative for a particular index might not be necessarily representative for another index at the same month. And similarly, the years considered typical for a certain month might not be inevitably typical for another month at the same weather index. For example, in Fig. 1(b), the CDF of Tma for July of 2005 follows the long term CDF remarkably well, whereas in Fig. 2(b), the CDF of DGSR for July of 2005 is not the best agreement with the long term CDF. Also, for instance, in Fig. 1(a), the CDF of Tma for January 2009 is compared the good with the long- term, whereas in Fig. 1(b), the CDF of Tma for July 2009 is the worst with respect to the long term CDF.

The FS statistic is estimated and examined for each weather index and for each month of every year in the database. Due to space limitation, only the FS values of daily global solar radiation for Shenyang station are shown in Table 3. It is found that the FS statistic (e.g. the FS statistic of DGSR in Table 3) often varies month to month and differs from one index to another.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

From (3), the weighted sum (WS) of the FS statistics is computed and determined. The values of WS for Shenyang station and the five candidate years of each calendar month (bold numbers) are tabulated and presented in Table 4.

The RMSD values of daily global solar radiation are solved by the above (4). The RMSD results of Shenyang station and the minimum value of RMSD for each month (bold characters) are shown in Table 5. The smallest RMSD values for each month vary between 1.3528 and 6.5429 MJ/m2.

Then, the month with smallest RMSD is selected as the TMM. Finally, the 12 TMMs is used to form a TMY. The TMY for Shenyang station can be found in Table 6. These database would be useful for the utilization of solar energy system in Northeast China.

Table 6 shows a summary of the TMYs selected for eight stations in three provinces of Northeast China. In order to know which years tend to follow the 16 year (1994-2009) long term weather patterns more closely than the others, the TMYs acquired for the eight stations in Northeast China are analyzed and investigated. Fig. 3 shows the year selection frequency for the TMYs derived from the 1994-2009 database. It can be found that 2004 and 2007 are the most and least frequent years respectively. In Fig. 3, the frequency occurrence of the 2004 year is up to12.5%. This means the typical data derived from 2004 is the prime eement with the long term (1994-2009) data.

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

Additionally, the accuracy of TMY data is excellent on monthly bases. The monthly average values of the long term measured data and typical solar radiation derived from the TMY data for the eight cities in three provinces Heilongjiang, Jilin, Liaoning) of Northeast China are compared and shown in Fig. 4(a).

As can be seen from Fig. 4(a), the degree of the deviation from the diagonal between the TMY data and the recorded data is small. To be obvious, the corresponding mean absolute percentage error (MAPE) between monthly mean values of the long term measured data and typical solar radiation data from TMY data for each month and for eight stations are shown in Fig. 4(b). From Fig. 4(a) and (b), the TMY data generally represent good agreement with the long-term data. In particular, the TMY data for Fuyu station is the best. The R value in the Fuyu station is up to 0.9983, and the MAPE lies between 0.03% and 5.04%.

Conclusions

The generation of the TMY data are essential and important for solar energy utilization. In this paper, the TMY method using the Finkelstein-Schafer statistical and novel assigned weighting factors is applied and utilized. Typical meteorological years for eight stations located in three provinces of Northeast China are formed based on the recent and accurate 16 years (1994-2009) recorded weather data. It is found that the cumulative distribution functions of each weather index for the TMMs selected tend to follow their long term counterparts well. It is also seen that the typical data from the 2004 is the prime agreement with the long-term data. In addition, comparison analysis between the monthly data from TMYs and the long term recorded data for this region show that TMYs perform well on monthly bases.

From the analysis and results, it is concluded that the solar energy resource in the three provinces of Northeast China is abundant and potential. It is believed that the TMY data developed by this paper will exert positive effects on some energy-related scientific researches and engineering applications in Northeast China. Future researches will focus on the TMY data on a larger regional scale.

Acknowledgements

The research is financially supported by National High Technology Research and Development Program of China (863 Program) (No. 2012AA050214), NSFC (No. 50907010), Natural Science Foundation of Jiangsu Province (No. BK2012753) and Major Scientific Research Guidance Foundation of Southeast University (3216002103). The authors would like to thank China Meteorological administration.

References

[1.] Chow T. T., Chan A. L. S., Fong K F., Lin Z. Some perceptions on typical weather year-from the observations of Hong Kong and Macau // Solar Energy, 2006.--No. 80(4). --P. 459-467.

[2.] Hall I. J., Prairie R. R., Anderson H. E., Boes E. C. Generation of a typical meteorological year // Proceeding of the 1978 annual meeting of the American Society of the international solar energy society, 1978.--P. 641-645.

[3.] Said S. A. M., Kadry H. M.. Generation of representative weather-year data for Saudi Arabia // Applied Energy, 1994.--No. 48(2).--P. 131-136.

[4.] Kalogirou S. A.. Generation of typical meteorological year (TMY-2) for Nicosia, Cyprus // Renewable Energy, 2003.--No. 28(15).--P. 2317-2334.

[5.] Marion W., Urban K. User manual for TMY2s-Typical Meteorological Years Derived from the 1961-1990 National Solar Radiation Data Base.--National Renewable Energy Laboratory, 1995.

[6.] Wilcox S., Marion W. Users Manual for TMY3 Data Sets. National Renewable Energy Laboratory, 2008. DOI: 10.2172/928611.

[7.] Petrakis M., Kambezidis H. D., Lykoudis S., Adamopoulos A. D., Kassomenos P., Michaelides I. M., Kalogirou S. A., Roditis G., Chrysis I., Hadjigianni A. Generation of a "typical meteorological year" for Nicosia, Cyprus // Renewable Energy, 1998.--No. 13(3).--P. 381388.

[8.] Zhou J., Wu Y. Z., Yan G. Generation of typical solar radiation year for China // Renewable Energy, 2006.--No. 31(12).--P. 1972-1985.

[9.] Jiang Y. N.. Generation of typical meteorological year for different climates of China // Energy, 2010.--No. 35(5).--P. 1946-1953.

[10.] Finkelstein J. M., Schafer R. E.. Improved goodness-of-fit tests // Biometrika, 1971.--No. 58(3).--P. 641-645.

[11.] Pissimanis D., Karras G., Notaridou V., Gavra K.. The generation of a "typical meteorological year" for the city of Athens // Solar Energy, 1988.--No. 40(5).--P. 405-411.

Received 2011 11 08

Accepted after revision 2012 01 22

Qingshan Xu, Haixiang Zang

School of Electrical Engineering, Southeast University, Sipailou 2#, Nanjing, China, 210096, phone: +86-25-83793692, e-mail: zanghaixiang@seu.edu.cn

cross ref http://dx.doi.Org/10.5755/j01.eee.123.7.2386
Table 1. Geographical locations and data period

Province       Location    Latitude(N)      Longitude(E)

Heilongjiang   Fuyu        47[degrees]48'   124[degrees]29'
               Harbin      45[degrees]45'   126[degrees]46'
               Kiamusze    46[degrees]49'   130[degrees]17'
               Changchun   43[degrees]54'   125[degrees]13'
Jilin          Yanji       42[degrees]53'   129[degrees]28'
               Chaoyang    41[degrees]33'   120[degrees]27'
Liaoning       Dalian      38[degrees]54'   121[degrees]38'
               Shenyang    41[degrees]44'   123[degrees]27'

Province       Location    Elevation        Period            Total
                           (m)                                years

Heilongjiang   Fuyu        162.7            1994-2009         16
               Harbin      142.3            1994-2009         16
               Kiamusze    81.2             1994-2009         16
               Changchun   236.8            1994-2009         16
Jilin          Yanji       176.8            1994-2009         16
               Chaoyang    169.9            1994-2009         16
Liaoning       Dalian      91.5             1994-2009         16
               Shenyang    44.7             1994-2009         16

Table 2. Weighting factors for FS statistics

[T.sub.max]   [T.sub.min]   [T.sub.ma]   [RH.sub.min]   [RH.sub.ma]

1/24             1/24          3/24          1/24          2/24

[T.sub.max]   [W.sub.max]   [W.sub.ma]   DGSR

1/24             2/24          2/24      12/24

Table 3. Summary of FS statistics of DGSR for Shenyang station

M    1994    1995    1996    1997    1998    1999    2000    2001

1    0.063   0.102   0.083   0.018   0.098   0.134   0.052   0.087
2    0.031   0.075   0.031   0.042   0.059   0.108   0.135   0.037
3    0.088   0.053   0.048   0.057   0.099   0.091   0.069   0.063
4    0.061   0.030   0.166   0.087   0.066   0.053   0.026   0.033
5    0.051   0.049   0.146   0.042   0.039   0.093   0.026   0.025
6    0.036   0.037   0.068   0.094   0.049   0.086   0.121   0.147
7    0.060   0.100   0.084   0.124   0.098   0.073   0.138   0.033
8    0.079   0.224   0.029   0.041   0.052   0.043   0.050   0.079
9    0.039   0.050   0.044   0.051   0.058   0.063   0.023   0.107
10   0.076   0.087   0.033   0.070   0.040   0.055   0.035   0.032
11   0.065   0.075   0.029   0.041   0.039   0.048   0.025   0.087
12   0.128   0.077   0.056   0.023   0.107   0.071   0.086   0.060

M    2002    2003    2004    2005    2006    2007    2008    2009

1    0.032   0.065   0.057   0.089   0.049   0.060   0.154   0.062
2    0.074   0.111   0.090   0.046   0.041   0.036   0.101   0.055
3    0.057   0.115   0.022   0.077   0.038   0.067   0.099   0.057
4    0.087   0.135   0.180   0.038   0.098   0.035   0.058   0.074
5    0.150   0.222   0.140   0.042   0.038   0.046   0.098   0.087
6    0.044   0.090   0.155   0.109   0.055   0.115   0.039   0.055
7    0.074   0.085   0.078   0.052   0.051   0.043   0.030   0.068
8    0.055   0.116   0.043   0.059   0.030   0.046   0.067   0.148
9    0.063   0.080   0.046   0.037   0.056   0.047   0.078   0.032
10   0.123   0.034   0.073   0.055   0.029   0.025   0.076   0.062
11   0.055   0.108   0.153   0.073   0.062   0.035   0.139   0.030
12   0.092   0.042   0.163   0.128   0.042   0.046   0.047   0.025

Table 4. Summary of WS of FS statistic for Shenyang station
(bold numbers correspond to the five candidate years of
each month)

M           1994      1995      1996      1997      1998      1999

1           0.0107#   0.0174    0.0114    0.0074#   0.0125    0.0197
2           0.0080#   0.0110    0.0091#   0.0080#   0.0126    0.0131
3           0.0106    0.0077#   0.0103    0.0083#   0.0148    0.0130
4           0.0101    0.0081#   0.0156    0.0090    0.0127    0.0072#
5           0.0103    0.0115    0.0149    0.0086#   0.0066#   0.0107
6           0.0078#   0.0084#   0.0088#   0.0131    0.0099    0.0102
7           0.0144    0.0155    0.0142    0.0187    0.0103#   0.0133
8           0.0123    0.0200    0.0089    0.0104    0.0086    0.0080#
9           0.0086#   0.0078#   0.0080#   0.0104    0.0101    0.0101
10          0.0088    0.0103    0.0082    0.0119    0.0084    0.0098
11          0.0094    0.0116    0.0058#   0.0072#   0.0078    0.0084
December    0.0114    0.0107    0.0132    0.0102    0.0121    0.0086#

M           2002      2003      2004      2005      2006      2007

1           0.0109    0.0071#   0.0115    0.0105#   0.0110    0.0144
2           0.0172    0.0126    0.0143    0.0144    0.0103#   0.0112
3           0.0138    0.0125    0.0093    0.0100    0.0060#   0.0103
4           0.0092    0.0129    0.0188    0.0057#   0.0138    0.0090
5           0.0194    0.0193    0.0139    0.0092    0.0058#   0.0093
6           0.0092#   0.0105    0.0171    0.0128    0.0097#   0.0166
7           0.0106#   0.0115    0.0106    0.0070#   0.0077#   0.0111
8           0.0083    0.0138    0.0085    0.0070#   0.0067#   0.0079#
9           0.0124    0.0086#   0.0086    0.0088    0.0102    0.0108
10          0.0129    0.0048#   0.0118    0.0075#   0.0053#   0.0096
11          0.0120    0.0098    0.0161    0.0127    0.0066#   0.0075#
12          0.0107    0.0066#   0.0157    0.0201    0.0065#   0.0119

M           2000      2001

1           0.0162    0.0163
2           0.0199    0.0115
3           0.0075#   0.0084#
4           0.0052#   0.0075#
5           0.0053#   0.0067#
6           0.0175    0.0173
7           0.0207    0.0117
8           0.0106    0.0090
9           0.0064#   0.0112
10          0.0079#   0.0078#
11          0.0062#   0.0097
December    0.0094    0.0110

M           2008      2009

1           0.0172    0.0093#
2           0.0155    0.0096#
3           0.0153    0.0096
4           0.0095    0.0106
5           0.0161    0.0129
6           0.0145    0.0128
7           0.0104#   0.0151
8           0.0111    0.0154
9           0.0109    0.0089
10          0.0109    0.0104
11          0.0128    0.0099
12          0.0083#   0.0083#

Note: correspond to the five candidate years
of each month are indicated with #.

Table 5. RMSD (MJ/[m.sup.2]) of DGSR for the five candidate
years at Shenyang station (bold numbers correspond to the
min value in the month)

year    January    1994     1997     2003     2005     2009

RMSD               1.9061   1.9737   1.8982   1.8298   1.4779
year    February   1994     1996     1997     2006     2009
RMSD               2.9039   2.8918   2.7319   3.7731   3.2397
year    March      1995     1997     2000     2001     2006
RMSD               4.6122   3.6407   4.9080   4.7633   4.7120
year    April      1995     1999     2000     2001     2005
RMSD               5.7412   6.7960   5.9783   5.7499   6.0400
year    May        1997     1998     2000     2001     2006
RMSD               7.2136   7.6182   6.4018   6.5230   6.0313
year    June       1994     1995     1996     2002     2006
RMSD               7.0481   7.6254   6.5429   7.9038   7.0655

year    July       1998     2002     2005     2006     2008

RMSD               7.0274   5.7967   5.2308   5.6133   6.5459
year    August     1999     2002     2005     2006     2007
RMSD               6.3501   5.3472   6.3209   6.5659   6.6722
year    September  1994     1995     1996     2000     2003
RMSD               4.1717   4.6976   5.0640   5.1818   6.1704
year    October    2000     2001     2003     2005     2006
RMSD               4.3431   3.6942   4.7471   4.2195   3.9475
year    November   1996     1997     2000     2006     2007
RMSD               2.8450   2.7861   2.9053   2.7438   3.1269
year    December   1999     2003     2006     2008     2009
RMSD               1.3528   1.5334   1.8407   1.6079   2.0264

Note: Correspond to the min value in the month are indicated
with #.

Table 6. Summary of the TMYs selected for eight stations
in three provinces of Northeast China

                           Month

Province       Location    Jan    Feb    Mar    Apr    May    Jun

Heilongjiang   Fuyu        2003   1994   2004   1994   1994   2003
               Harbin      2003   1998   2004   1996   2001   1995
               Kiamusze    2003   1997   1997   2008   2002   1995

Jilin          Changchun   2004   1997   2006   2001   1996   2001
               Yanji       2005   1999   2004   2001   2006   2003

Liaoning       Chaoyang    1998   1999   1996   2007   1999   1995
               Dalian      2004   1995   2000   2000   1996   2009
               Shenyang    2009   1997   1997   1995   2006   1996
               Month

Province       Jul    Aug    Sep    Oct    Nov    Dec

Heilongjiang   1996   2001   2008   2001   2008   2002
               1998   2009   2004   2004   2009   1996
               2000   1995   2002   2008   2000   2003

Jilin          2002   2008   2009   2006   2006   1996
               2005   2001   2006   1998   2006   2006

Liaoning       2002   2006   2004   2003   1994   1998
               2004   2000   1998   1998   2004   1998
               2005   2002   1994   2001   2006   1999
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Title Annotation:SYSTEM ENGINEERING, COMPUTER TECHNOLOGY/SISTEMU INZINERIJA, KOMPIUTERINES TECHNOLOGIJOS
Author:Xu, Qingshan; Zang, Haixiang
Publication:Elektronika ir Elektrotechnika
Article Type:Report
Geographic Code:9CHIN
Date:Jul 1, 2012
Words:4016
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