- Email: [email protected]
A software tool for the creation of a typical meteorological year
Environmental
PII: SO266-9838(%)0@006-8
ELSEVIER
Soj-hvare, Vol. 11. No. 4, pp. 221-227, 1996 0 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain 0266-9838/96/$15.00 + 0.00
A software tool for the creation of a typical meteorological year M. Petrakis, S. Lykoudis, P. Kassomenos Institute
of Meteorology
and Physics
of the Atmospheric Environment, National CR 118 10, Athens, Greece (Received
8 June
1995; accepted
Observatory
of Athens,
P.O. Box 20048,
20 May 1996)
Abstract The generation of a typical meteorological year is of great importance for passive solar architectural applications. In this context, within the PASCOOL project, a software tool has been developed, utilizing the Filkenstein-Schafer statistical method for the creation of a typical meteorological year. Using this software tool, a typical meteorological year was generated for Athens, Greece. The data used were from the National Observatory of Athens and cover a period of 17 years (1977-1993). 0 1997 Elsevier Science Ltd. All
rights reserved. Keywords:
Passive heating/cooling;
building thermal simulation;
typical meteorological
data; test reference
data
representative database for a year duration is known as Test Reference Year or Typical Meteorological Year (TMY). One of the most common methodologies for creating a TMY is the one proposed by Hall et al. (1978) utilizing the Filkenstein-Schafer (1971) (FS) statistical method. This method has been selected in the frame of the PASCOOL project as the most suitable one, and a software tool has been developed for the implementation of this method. This software tool was named TRY and includes a number of data filtering and processing utilities (Pet&is, 1995). The TRY program was used to generate a TMY for Athens Metropolitan Area (AMA). AMA is located in the
1. Introduction A solar climatological database is very important for the calculation of energy efficiency and must be representative of the area of interest. In the past, many attempts have been made to generate such climatological databases for different areas around the world using various methodologies (Flocas, 1963; Petrie and McClintock, 1978; Schweitzer, 1978; Notaridou and Lalas, 1979; Lalas et al., 1982; Feuermann et cd., 1985; Kouremenos et d., 1985; Bahadori and Chamberlain, 1986; Pissimanis et al., 1988; Festa and Ratto, 1993; Mosalam Shaltout and Tadros, 1994). A 221
M. Petrakis et al.lSoftware tool for creating a meteorological
222
year
are of no importance to the program since it performs comparison, so it does not make any difference whether one uses Fahrenheit or Celsius, joules or watts. The structure of the program is a series of menus through which one can make the appropriate selection of data and actions. The initial part of the program is the selection of the meteorological data files for inclusion in the TRY generation. The first menu incorporates the meteorological parameters’ selection (Fig. 1). The user can choose to process one, two, three or all four of the meteorological parameters and then proceed to the rest of the program or cancel all selections and make new selections. The second menu is the selection of the basic steps of the program (Fig. 2). With the Data Filtering option, the user examines the hourly meteorological data files for erroneous characters (e.g. alphanumeric characters, commas, periods and all the other ASCII codes which will create erroneous results). The program cannot correct these errors but it detects their location in the file and produces a report for each data file. The Missing Dates and Outlier Detection (hourly values) option allows the detection of outliers according to the criteria specified in the Criteria file. This subroutine can also detect the missing dates or misplaced dates of data files. If, for example, a day is missing or is misplaced (year, month or day), the program detects it and records the results. During this phase, a full report of the errors detected is created, but no corrections are made by the program. The Interpolation of Missing Values option examines the hourly data files for missing values-indicated by 999.9-and then performs linear interpolation for a maximum number of six consecutive missing values, producing new hourly data files. Selection of the maximum number of consecutive hourly missing values over which linear interpolation can be performed was measurements
L.
llullIuIlT
3. 4. 5. 6. 7.
WIND SPEED SoUtR RADIATION MNCIIL hLL SELECTIONS DONEAND CONTINUE END PROGRfiH
Fig. 1. Opening menu of the TRY program.
central part of the Attic peninsula and covers 450 km* of built up area. It is surrounded by mountains to the North, West and East and is open to the sea from the South. The sides of the mountains facing AMA are mostly rocky, covered by bush. About four million people live and work in the area (almost 40% of the Greek population). The meteorological and global solar radiation data that were used are from the measurements of the National Observatory of Athens (NOA) station and cover a period of 17 years (1977-1993). The NOA station (lat. 37.58 N, long. 23.43 E) is located on a small hill, with an elevation of 107 m above mean sea level, near the centre of the city, 8 km away from the seashore. A variety of routine meteorological data as well as irradiance data have been collected for a great number of years and archived in the database of NOA.
2. The TRY software tool TRY is a program developed for the generation of a typical meteorological year based on a series of meteorological measurements. The program processes data files, filters them and interpolates missing values in order to produce a report with data errors, outliers and their positions and then proceeds with the generation of the TMY, using the FS method. For the application of the TRY program, the following files are necessary: a criteria file for the detection of outliers and four data files with temperature, humidity, wind speed and solar irradiance (global) data. The units of
I
1. DBTA FILTERING(EIlROMXWS DAThA) 2. PUSSINGDATESMD OUTLIERDETECTION(HOURLYVALUES) 3. INTEM'OLftTION OF IMSING WLUES 4. DfilMPROCESSIN (GENElihTtON OF llf%NW,tbX,HIN,DIPF) I 7.
Fig. 2. Main menu of the TRY program.
Table 1 Meteorological parameters and weighting factors used for the FS statistic Parameters
Weighting factor
Solar radiation Wind velocity (maximum) Wind velocity (minimum) Wind velocity (mean) Wind velocity range Dew point (maximum) Dew point (minimum) Dew point (mean) Dew point range Dry bulb temperature (maximum) Dry bulb temperature (minimum) Dry bulb temperature (mean) Dry bulb temperature range
12124 l/24 1124 1124 l/24 1124 l/24 l/24 l/24 l/24 l/24 l/24 1124
M. Petrakis et aLlSoftware tool for creating a meteorological
year
223
Table 2 Weighted sums of FS statistic (WS) Year
1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
Month J
J
F
M
A
M
J
.0720 .0864 .0665 .0586 .0865 .0706 .0976 .0855 .0716 .0708 .0616 .0737 .0833 .0634 .0711 .0719 .0654
.1022 .0740 .0780 .0994 .0822 .0966 .1184 .0880 .0873 .0803 .0733 .0978 .0891 .0846 .07!zJo .0793 .0926
.0716 .0838 .0804 .0862 .0814 .0693 .0721 .1094 .1180 .0631 .0858 .1032 .0631 .1125 .0761 .0737 .0680
.0909 .1046 .0757 .0847 .0936 .0978 .1179 .1743 .0956 .1388 .1143 .0912 .1172 .0902 .1014 .0892 .0985
.1216 .1120 .1339 .lloo .1166 .1071 .1034 .1408 .0994 .0897 .1336 .1521 .0918 .0922 .1009 .I132 .1398
.1069 .1338 .1054 .1237 .1149 .1438 .1132 .1391 .1211 .1225 .1479 .1485 .1096 .0967 .1228 .1613 .I189
.11_56 .1134 .1238 .1283 .1068 .1264 .1741 .1146 .1147 .1206 .1666 .I577 .1098 .1162 .1311 .1581 .1128
A
S
0
N
D
.1204 .1427 .1267 .1198 .1093 .1234 .1287 .1560 .1279 .1389 .2050 .1735 .1221 .I493 .1597 .I271 .1130
.I126 .llOo .0930 .0954 .0971 .1023 .0979 .1077 .1146 .1036 .I139 .1026 .0901 .o!x4 .0986 .0882 .0957
.0991 .0929 .1414 .1250 .I 123 .1138 .0916 .1221 .0778 .0978 .0941 .1212 .1002 .0883 .1387 .1433 .I 182
.1061 .0837 .0870 .1146 .I320 .1050 .1379 .0892 .1070 .0949 .1083 .0949 .0862 .1009 .0973 .0978 .1435
.0934 .0699 .0644 .0543 .0901 .0917 .0683 .0642 .0687 .0786 .0601 .0540 .0691 .0831 .1246 .0701 .0770
Table 3 Root mean square differences (RMSD) of the mean hourly values of global solar radiation (MJ m-*) for the selected year of each month Month J
Year .I 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
.056 .054
F
M
A
,111
,063
M
J
A
S
.108
.057 ,082
.080
.069
.073 .053
N
D
,395
.062 .084
.071 ,094
0
,065
,111 .09 1
,111 ,072
.076 ,060
,068
.038
,066 .060 .070
.028 .115 ,110 .295 .077
.095 .058 .031
.05 1
,085
,119
.060 .079 ,091
.127 ,065
.062
.070
,205 .343
based on the fact that for most of the meteorological stations around the world, only 3-h or 6-h data are available. Since the program produces daily average values or daily sums, when hourly data are used, this selection leads to an error comparable to that introduced by the use of the more commonly available 3-h or 6-h data. Files produced by the interpolation procedure do not automatically replace the original ones, since the user has to decide whether interpolated values, produced by this routine, can be incorporated in the data files. The Data Processing (generation of mean, max, min, diff) routine examines each record of the hourly data files and then, if there are no data errors, creates the
.040
.035
,058 .090
,413
,378
.069 ,090
.lOl .085
.351 .065
four files containing the mean, maximum, minimum and range values for temperature, humidity and wind speed and the daily sum for solar radiation, which are necessary for the TRY Generation routine. Finally, the TRY Generation routine incorporates the application of the FS method on the meteorological data. Any number of consecutive years can be selected from the meteorological data files to form the database for the generation of the TMY. There is also an option of creating either a complete TMY or separate typical meteorological months (TMM). The cumulative distribution functions (CDFs) are calculated for each selected meteorological parameter and for each selected month, over the whole selected
M. Pet&is
224
et &/Software
tool for creating a meteorological
period of years (long-term measurements) as well as over each specific year in the selected period. In order to calculate the CDFs, for each parameter, the hourly data are grouped under a number of bins, 31 in this case, and the CDFs are calculated by counting the cases under each bin. The next step is comparison of the CDF for a meteorological parameter, say maximum temperature, for each month, say January, for each specific year, say 1975, with the respective CDF for the long-term composite of all the years used. The mean difference of the long-term CDF, CDFLT, and the specific month’s CDF, CDFsT, calculated over the bins used for the calculation of the CDFs, (Xi), is the value of the FS statistic for this month of this specific year and for the meteorological parameter under consideration, namely maximum temperature of January 1975.
FS = ,: i: ICDFLT(Xi)- CDFs,(x,)l i=l
(1)
where N equals 31 in this case. The last part of the program is the application of the weighting factors, WFj, to the FS statistic values, one for each considered meteorological parameter, FS,, corresponding to each specific month in the selected period of years. This way, a weighted average value, WS, is produced and this value is assigned to the respective month.
WS = t E WFj . FSj
(2)
j=l
:WF,=l
(3)
j=l
where M equals 13 in this case. The user can change the weighting factors, thus examining the relative importance of each meteorological parameter in the final result. The output of the TRY program includes, in the final form, presentation of the weighting factors and the WS values for all the months under consideration. The cases are grouped by month, and sorted by ascending order of WS. The smaller the WS value, the better the approximation to a TMM. For reasons of convenience, the final results are also presented on screen and the CDFs of the different parameters are recorded as output files.
3. TMY generation for Athens Using the TRY program, 13 meteorological parameters were examined for a period of 17 years. These parameters were the daily mean, maximum and mini-
year
mum values, and ranges of temperature, dew point and wind velocity, and the daily values of global solar radiation. For each month the FS statistic is estimated for every year and for all of the 13 parameters that have been considered. In order to examine the influence of the various meteorological parameters in the FS statistic and, accordingly, to the selection of the representative months, a number of estimations were made by using various weighting factors (Pissimanis et al., 1988). The weighting factors that were finally adopted were 0.5 for the solar radiation and l/24 for the other 12 parameters, since, for Athens, global solar radiation is considered to be the most important meteorological parameter involved in applications that potentially require the use of a TMY. In Table 1, the meteorological parameters and the weighting factors adopted for that study were presented. For each month, the FS statistic was computed for every year and for all 13 parameters that have been considered, and a weighted sum (WS) was produced. Applying all the above procedures for all months of the 17-year period of the Athens database, a composite year was first formed consisting of the selected months with the smallest values of WS. For each month of the year, the cases corresponding to the five lower values of the FS statistic were selected and are presented with bold characters in Table 2. According to Pissimanis et al. (1988), the final selection of the most representative year, among the five primarily selected, for each month was done in a second stage, by examining a number of statistics of the values of global solar radiation. These statistics were root mean square differences (RMSD) of the mean hourly distribution of the global solar radiation for each year of each month, with respect to the mean long-term hourly distribution and the FS statistics. For the five selected cases the RMSD was computed and, for each month, the year corresponding to the lowest value was selected. If there were missing values in the selected year, then the year corresponding to the next lowest value was selected. The RMSD results are shown in Table 3 and the final selections are presented in bold characters. In Fig. 3, the mean monthly values of global solar radiation (in MJ rnm2)are given for TMY for the period 1977-1993 and for the most unfavorable values of WS for each month of the period. Also, in Figs 4-6, the mean hourly values of global solar radiation for three representative months, January for Winter (November, December, January and February), July for Summer (June, July, August and September) and April for the transient seasons (March, April, May and October) were presented. In the same figures, the mean hourly values are given for the years with the most unfavorable (large) values of FS radiation statistics. It can be verified that the values of global solar radiation for the unfavorable years show
M. Petrakis et d/Software
tool for creating a meteorological
year
225
YEAR 1.2 .
--I i
I--
Total Mean
-F-S Best .._._. F-S Worst
-4
0 1
3
2
5
4
6
6
7
l0
Q
12
11
Monlhr
Fig. 3. Annual variation of monthly mean hourly value of global solar radiation for the whole period of 17 years (dashed line), for the selected TMY (solid line) and for the worst year, composed of the worst months of the period (dotted line).
JANUARY _..... ,*. ..’ *.... I.
1.6* 1.4.3 Ii
l-2 .-
5
0.6 .-
8
0,6 --
..+.
TOt6lMean
-F-SBest _..... F-S Worst
0.2 --
4
5
6
7
6
9
10
11
12
13
14
15
16
17
: 18
ID
1 20
f 21
Fig. 4. Monthly variation of hourly mean value of global solar radiation for the whole period of 17 years (dashed line), for the selected TMY (solid line) and for the worst year (dotted line) for January.
APRIL
+.
Total
Mean
-
F-S Best
......
F-S Worst
‘.T., .*. 4
5
6
7
8
9
:
i
:
:
:
:
i
:
i
10
11
12
13
14
15
16
17
IQ
Hotw~
Fig. 5. As in Fig. 4 but for April.
*19
20
I 21
226
M. Pet&is
et &./Software tool for creating a meteorological
year
JULY 3.5
4
5
6
7
6
0
10
11
12
13
14
15
16
17
16
19
HOW8
Fig. 6. As in Fig. 4 but for July.
considerable deviations from the long-range mean values as well as from the values of the TMY. From Figs 4-6 it can be seen that the daily values of global solar radiation for the years chosen as TMY are quite normally distributed with respect to the corresponding mean monthly values (dashed lines). It can also be noticed from the above figures that the maximum variation of global solar radiation appears in the transient seasons.
4. Conclusions The TRY program is a flexible and self-contained tool. Being menu driven, it is user friendly and allows for extensive data filtering. It also provides some data processing, while its main function, TMY generation, is applicable to any subset of data, regarding both time period and number of meteorological parameters considered. The user is able to experiment with the weighting factors and, at last, have the calculated CDFs as an output for further use (e.g. with other statistical tests). From application of the program for the generation of a TMY for the AMA, one can see that the methodology implemented leads to acceptable results.
Acknowledgements This work was partly funded by the CEC (DG XII) PASCOOL Project, contract JOU2-CT92-0013. The authors would like to thank the European Community and also NOA for providing the meteorological data analyzed in this paper.
References Bahadori, M. N. and Chamberlain, M. J. (1986). A simplification of weather data to evaluate daily and
monthly energy needs of residential buildings. Solar Energy, 36, 499-507. Festa, R. and Ratto, C. F. (1993) Proposal of a numerical procedure to select Reference Years. Solar Energy 50, 9-17. Feuermann, D., Gordon, J. M. and Zarmi, Y. (1985) A Typical Meteorological Day (TMD) approach for predicting the long-term performance of solar energy systems. Solar Energy 35, 63-69. Filkenstein, J. M. and Schafer, R. E. (1971) Improved goodness of fit tests. Biometrica 58, 641-645. Flocas, A. A. (1963) Estimation and prediction of global solar radiation over Greece. Solar Energy 24, 63-70. Hall, I. J., Prairie, R. R., Anderson, H. E. and Boes, E. C. (1978) Generation of Typical Meteorological Years for 26 SOLMET stations. Sandia Luboratories Report SAND 78-1601, Albuquerque, New Mexico. Kouremenos, D. A., Antonopoulos, K. A. and Domazakis, E. S. (1985) Solar radiation correlations for the Athens-Greece area. Solar Energy 35, 259-269. Lalas, D. P., Pissimanis, D. K. and Notaridou, V. A. (1982) Methods of estimation of the intensity of solar radiation on a tilted surface and tabulated data for 30”, 45’ and 60” in Greece. Technica Chronica, Scientific Journal of the Technical Chamber of Greece, Section B 2, 129-178. Mosalam Shaltout, M. A. and Tadros, M. T. Y. (1994) Typical solar radiation year for Egypt. Renewable Energy 4, 387-393. Notaridou, V. and Lalas, D. P. (1979) The distribution of global and net radiation over Greece. SoZar Energy 22, 505-514. Pet&is, M. (1995) A Sofnyare Tool for the Creation of a Typical Meteorological Year. Final Report for the PASCOOL Project of CEC. Petrie, W. R. and McClintock, M. (1978) Determining typical weather for use in solar energy simulations. Solar Energy 21, 55-59.
M. Petrakis et aLlSoftware tool for creating a meteorological Pissimanis, D., Karras, G., Notaridou, V. and Gavra, K. (1988) The generation of a ‘Typical Meteorological year’ for the city of Athens. Solar Energy 40, 405-411.
year
227
Schweitzer, S. (1978) A possible ‘average’ weather year on Israel’s coastal plain for solar system simulations, Technical Note. Solar Energy 21, 5 11-5 15.
PII: SO266-9838(%)0@006-8
ELSEVIER
Soj-hvare, Vol. 11. No. 4, pp. 221-227, 1996 0 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain 0266-9838/96/$15.00 + 0.00
A software tool for the creation of a typical meteorological year M. Petrakis, S. Lykoudis, P. Kassomenos Institute
of Meteorology
and Physics
of the Atmospheric Environment, National CR 118 10, Athens, Greece (Received
8 June
1995; accepted
Observatory
of Athens,
P.O. Box 20048,
20 May 1996)
Abstract The generation of a typical meteorological year is of great importance for passive solar architectural applications. In this context, within the PASCOOL project, a software tool has been developed, utilizing the Filkenstein-Schafer statistical method for the creation of a typical meteorological year. Using this software tool, a typical meteorological year was generated for Athens, Greece. The data used were from the National Observatory of Athens and cover a period of 17 years (1977-1993). 0 1997 Elsevier Science Ltd. All
rights reserved. Keywords:
Passive heating/cooling;
building thermal simulation;
typical meteorological
data; test reference
data
representative database for a year duration is known as Test Reference Year or Typical Meteorological Year (TMY). One of the most common methodologies for creating a TMY is the one proposed by Hall et al. (1978) utilizing the Filkenstein-Schafer (1971) (FS) statistical method. This method has been selected in the frame of the PASCOOL project as the most suitable one, and a software tool has been developed for the implementation of this method. This software tool was named TRY and includes a number of data filtering and processing utilities (Pet&is, 1995). The TRY program was used to generate a TMY for Athens Metropolitan Area (AMA). AMA is located in the
1. Introduction A solar climatological database is very important for the calculation of energy efficiency and must be representative of the area of interest. In the past, many attempts have been made to generate such climatological databases for different areas around the world using various methodologies (Flocas, 1963; Petrie and McClintock, 1978; Schweitzer, 1978; Notaridou and Lalas, 1979; Lalas et al., 1982; Feuermann et cd., 1985; Kouremenos et d., 1985; Bahadori and Chamberlain, 1986; Pissimanis et al., 1988; Festa and Ratto, 1993; Mosalam Shaltout and Tadros, 1994). A 221
M. Petrakis et al.lSoftware tool for creating a meteorological
222
year
are of no importance to the program since it performs comparison, so it does not make any difference whether one uses Fahrenheit or Celsius, joules or watts. The structure of the program is a series of menus through which one can make the appropriate selection of data and actions. The initial part of the program is the selection of the meteorological data files for inclusion in the TRY generation. The first menu incorporates the meteorological parameters’ selection (Fig. 1). The user can choose to process one, two, three or all four of the meteorological parameters and then proceed to the rest of the program or cancel all selections and make new selections. The second menu is the selection of the basic steps of the program (Fig. 2). With the Data Filtering option, the user examines the hourly meteorological data files for erroneous characters (e.g. alphanumeric characters, commas, periods and all the other ASCII codes which will create erroneous results). The program cannot correct these errors but it detects their location in the file and produces a report for each data file. The Missing Dates and Outlier Detection (hourly values) option allows the detection of outliers according to the criteria specified in the Criteria file. This subroutine can also detect the missing dates or misplaced dates of data files. If, for example, a day is missing or is misplaced (year, month or day), the program detects it and records the results. During this phase, a full report of the errors detected is created, but no corrections are made by the program. The Interpolation of Missing Values option examines the hourly data files for missing values-indicated by 999.9-and then performs linear interpolation for a maximum number of six consecutive missing values, producing new hourly data files. Selection of the maximum number of consecutive hourly missing values over which linear interpolation can be performed was measurements
L.
llullIuIlT
3. 4. 5. 6. 7.
WIND SPEED SoUtR RADIATION MNCIIL hLL SELECTIONS DONEAND CONTINUE END PROGRfiH
Fig. 1. Opening menu of the TRY program.
central part of the Attic peninsula and covers 450 km* of built up area. It is surrounded by mountains to the North, West and East and is open to the sea from the South. The sides of the mountains facing AMA are mostly rocky, covered by bush. About four million people live and work in the area (almost 40% of the Greek population). The meteorological and global solar radiation data that were used are from the measurements of the National Observatory of Athens (NOA) station and cover a period of 17 years (1977-1993). The NOA station (lat. 37.58 N, long. 23.43 E) is located on a small hill, with an elevation of 107 m above mean sea level, near the centre of the city, 8 km away from the seashore. A variety of routine meteorological data as well as irradiance data have been collected for a great number of years and archived in the database of NOA.
2. The TRY software tool TRY is a program developed for the generation of a typical meteorological year based on a series of meteorological measurements. The program processes data files, filters them and interpolates missing values in order to produce a report with data errors, outliers and their positions and then proceeds with the generation of the TMY, using the FS method. For the application of the TRY program, the following files are necessary: a criteria file for the detection of outliers and four data files with temperature, humidity, wind speed and solar irradiance (global) data. The units of
I
1. DBTA FILTERING(EIlROMXWS DAThA) 2. PUSSINGDATESMD OUTLIERDETECTION(HOURLYVALUES) 3. INTEM'OLftTION OF IMSING WLUES 4. DfilMPROCESSIN (GENElihTtON OF llf%NW,tbX,HIN,DIPF) I 7.
Fig. 2. Main menu of the TRY program.
Table 1 Meteorological parameters and weighting factors used for the FS statistic Parameters
Weighting factor
Solar radiation Wind velocity (maximum) Wind velocity (minimum) Wind velocity (mean) Wind velocity range Dew point (maximum) Dew point (minimum) Dew point (mean) Dew point range Dry bulb temperature (maximum) Dry bulb temperature (minimum) Dry bulb temperature (mean) Dry bulb temperature range
12124 l/24 1124 1124 l/24 1124 l/24 l/24 l/24 l/24 l/24 l/24 1124
M. Petrakis et aLlSoftware tool for creating a meteorological
year
223
Table 2 Weighted sums of FS statistic (WS) Year
1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
Month J
J
F
M
A
M
J
.0720 .0864 .0665 .0586 .0865 .0706 .0976 .0855 .0716 .0708 .0616 .0737 .0833 .0634 .0711 .0719 .0654
.1022 .0740 .0780 .0994 .0822 .0966 .1184 .0880 .0873 .0803 .0733 .0978 .0891 .0846 .07!zJo .0793 .0926
.0716 .0838 .0804 .0862 .0814 .0693 .0721 .1094 .1180 .0631 .0858 .1032 .0631 .1125 .0761 .0737 .0680
.0909 .1046 .0757 .0847 .0936 .0978 .1179 .1743 .0956 .1388 .1143 .0912 .1172 .0902 .1014 .0892 .0985
.1216 .1120 .1339 .lloo .1166 .1071 .1034 .1408 .0994 .0897 .1336 .1521 .0918 .0922 .1009 .I132 .1398
.1069 .1338 .1054 .1237 .1149 .1438 .1132 .1391 .1211 .1225 .1479 .1485 .1096 .0967 .1228 .1613 .I189
.11_56 .1134 .1238 .1283 .1068 .1264 .1741 .1146 .1147 .1206 .1666 .I577 .1098 .1162 .1311 .1581 .1128
A
S
0
N
D
.1204 .1427 .1267 .1198 .1093 .1234 .1287 .1560 .1279 .1389 .2050 .1735 .1221 .I493 .1597 .I271 .1130
.I126 .llOo .0930 .0954 .0971 .1023 .0979 .1077 .1146 .1036 .I139 .1026 .0901 .o!x4 .0986 .0882 .0957
.0991 .0929 .1414 .1250 .I 123 .1138 .0916 .1221 .0778 .0978 .0941 .1212 .1002 .0883 .1387 .1433 .I 182
.1061 .0837 .0870 .1146 .I320 .1050 .1379 .0892 .1070 .0949 .1083 .0949 .0862 .1009 .0973 .0978 .1435
.0934 .0699 .0644 .0543 .0901 .0917 .0683 .0642 .0687 .0786 .0601 .0540 .0691 .0831 .1246 .0701 .0770
Table 3 Root mean square differences (RMSD) of the mean hourly values of global solar radiation (MJ m-*) for the selected year of each month Month J
Year .I 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
.056 .054
F
M
A
,111
,063
M
J
A
S
.108
.057 ,082
.080
.069
.073 .053
N
D
,395
.062 .084
.071 ,094
0
,065
,111 .09 1
,111 ,072
.076 ,060
,068
.038
,066 .060 .070
.028 .115 ,110 .295 .077
.095 .058 .031
.05 1
,085
,119
.060 .079 ,091
.127 ,065
.062
.070
,205 .343
based on the fact that for most of the meteorological stations around the world, only 3-h or 6-h data are available. Since the program produces daily average values or daily sums, when hourly data are used, this selection leads to an error comparable to that introduced by the use of the more commonly available 3-h or 6-h data. Files produced by the interpolation procedure do not automatically replace the original ones, since the user has to decide whether interpolated values, produced by this routine, can be incorporated in the data files. The Data Processing (generation of mean, max, min, diff) routine examines each record of the hourly data files and then, if there are no data errors, creates the
.040
.035
,058 .090
,413
,378
.069 ,090
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four files containing the mean, maximum, minimum and range values for temperature, humidity and wind speed and the daily sum for solar radiation, which are necessary for the TRY Generation routine. Finally, the TRY Generation routine incorporates the application of the FS method on the meteorological data. Any number of consecutive years can be selected from the meteorological data files to form the database for the generation of the TMY. There is also an option of creating either a complete TMY or separate typical meteorological months (TMM). The cumulative distribution functions (CDFs) are calculated for each selected meteorological parameter and for each selected month, over the whole selected
M. Pet&is
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period of years (long-term measurements) as well as over each specific year in the selected period. In order to calculate the CDFs, for each parameter, the hourly data are grouped under a number of bins, 31 in this case, and the CDFs are calculated by counting the cases under each bin. The next step is comparison of the CDF for a meteorological parameter, say maximum temperature, for each month, say January, for each specific year, say 1975, with the respective CDF for the long-term composite of all the years used. The mean difference of the long-term CDF, CDFLT, and the specific month’s CDF, CDFsT, calculated over the bins used for the calculation of the CDFs, (Xi), is the value of the FS statistic for this month of this specific year and for the meteorological parameter under consideration, namely maximum temperature of January 1975.
FS = ,: i: ICDFLT(Xi)- CDFs,(x,)l i=l
(1)
where N equals 31 in this case. The last part of the program is the application of the weighting factors, WFj, to the FS statistic values, one for each considered meteorological parameter, FS,, corresponding to each specific month in the selected period of years. This way, a weighted average value, WS, is produced and this value is assigned to the respective month.
WS = t E WFj . FSj
(2)
j=l
:WF,=l
(3)
j=l
where M equals 13 in this case. The user can change the weighting factors, thus examining the relative importance of each meteorological parameter in the final result. The output of the TRY program includes, in the final form, presentation of the weighting factors and the WS values for all the months under consideration. The cases are grouped by month, and sorted by ascending order of WS. The smaller the WS value, the better the approximation to a TMM. For reasons of convenience, the final results are also presented on screen and the CDFs of the different parameters are recorded as output files.
3. TMY generation for Athens Using the TRY program, 13 meteorological parameters were examined for a period of 17 years. These parameters were the daily mean, maximum and mini-
year
mum values, and ranges of temperature, dew point and wind velocity, and the daily values of global solar radiation. For each month the FS statistic is estimated for every year and for all of the 13 parameters that have been considered. In order to examine the influence of the various meteorological parameters in the FS statistic and, accordingly, to the selection of the representative months, a number of estimations were made by using various weighting factors (Pissimanis et al., 1988). The weighting factors that were finally adopted were 0.5 for the solar radiation and l/24 for the other 12 parameters, since, for Athens, global solar radiation is considered to be the most important meteorological parameter involved in applications that potentially require the use of a TMY. In Table 1, the meteorological parameters and the weighting factors adopted for that study were presented. For each month, the FS statistic was computed for every year and for all 13 parameters that have been considered, and a weighted sum (WS) was produced. Applying all the above procedures for all months of the 17-year period of the Athens database, a composite year was first formed consisting of the selected months with the smallest values of WS. For each month of the year, the cases corresponding to the five lower values of the FS statistic were selected and are presented with bold characters in Table 2. According to Pissimanis et al. (1988), the final selection of the most representative year, among the five primarily selected, for each month was done in a second stage, by examining a number of statistics of the values of global solar radiation. These statistics were root mean square differences (RMSD) of the mean hourly distribution of the global solar radiation for each year of each month, with respect to the mean long-term hourly distribution and the FS statistics. For the five selected cases the RMSD was computed and, for each month, the year corresponding to the lowest value was selected. If there were missing values in the selected year, then the year corresponding to the next lowest value was selected. The RMSD results are shown in Table 3 and the final selections are presented in bold characters. In Fig. 3, the mean monthly values of global solar radiation (in MJ rnm2)are given for TMY for the period 1977-1993 and for the most unfavorable values of WS for each month of the period. Also, in Figs 4-6, the mean hourly values of global solar radiation for three representative months, January for Winter (November, December, January and February), July for Summer (June, July, August and September) and April for the transient seasons (March, April, May and October) were presented. In the same figures, the mean hourly values are given for the years with the most unfavorable (large) values of FS radiation statistics. It can be verified that the values of global solar radiation for the unfavorable years show
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YEAR 1.2 .
--I i
I--
Total Mean
-F-S Best .._._. F-S Worst
-4
0 1
3
2
5
4
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6
7
l0
Q
12
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Monlhr
Fig. 3. Annual variation of monthly mean hourly value of global solar radiation for the whole period of 17 years (dashed line), for the selected TMY (solid line) and for the worst year, composed of the worst months of the period (dotted line).
JANUARY _..... ,*. ..’ *.... I.
1.6* 1.4.3 Ii
l-2 .-
5
0.6 .-
8
0,6 --
..+.
TOt6lMean
-F-SBest _..... F-S Worst
0.2 --
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: 18
ID
1 20
f 21
Fig. 4. Monthly variation of hourly mean value of global solar radiation for the whole period of 17 years (dashed line), for the selected TMY (solid line) and for the worst year (dotted line) for January.
APRIL
+.
Total
Mean
-
F-S Best
......
F-S Worst
‘.T., .*. 4
5
6
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9
:
i
:
:
:
:
i
:
i
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IQ
Hotw~
Fig. 5. As in Fig. 4 but for April.
*19
20
I 21
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JULY 3.5
4
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HOW8
Fig. 6. As in Fig. 4 but for July.
considerable deviations from the long-range mean values as well as from the values of the TMY. From Figs 4-6 it can be seen that the daily values of global solar radiation for the years chosen as TMY are quite normally distributed with respect to the corresponding mean monthly values (dashed lines). It can also be noticed from the above figures that the maximum variation of global solar radiation appears in the transient seasons.
4. Conclusions The TRY program is a flexible and self-contained tool. Being menu driven, it is user friendly and allows for extensive data filtering. It also provides some data processing, while its main function, TMY generation, is applicable to any subset of data, regarding both time period and number of meteorological parameters considered. The user is able to experiment with the weighting factors and, at last, have the calculated CDFs as an output for further use (e.g. with other statistical tests). From application of the program for the generation of a TMY for the AMA, one can see that the methodology implemented leads to acceptable results.
Acknowledgements This work was partly funded by the CEC (DG XII) PASCOOL Project, contract JOU2-CT92-0013. The authors would like to thank the European Community and also NOA for providing the meteorological data analyzed in this paper.
References Bahadori, M. N. and Chamberlain, M. J. (1986). A simplification of weather data to evaluate daily and
monthly energy needs of residential buildings. Solar Energy, 36, 499-507. Festa, R. and Ratto, C. F. (1993) Proposal of a numerical procedure to select Reference Years. Solar Energy 50, 9-17. Feuermann, D., Gordon, J. M. and Zarmi, Y. (1985) A Typical Meteorological Day (TMD) approach for predicting the long-term performance of solar energy systems. Solar Energy 35, 63-69. Filkenstein, J. M. and Schafer, R. E. (1971) Improved goodness of fit tests. Biometrica 58, 641-645. Flocas, A. A. (1963) Estimation and prediction of global solar radiation over Greece. Solar Energy 24, 63-70. Hall, I. J., Prairie, R. R., Anderson, H. E. and Boes, E. C. (1978) Generation of Typical Meteorological Years for 26 SOLMET stations. Sandia Luboratories Report SAND 78-1601, Albuquerque, New Mexico. Kouremenos, D. A., Antonopoulos, K. A. and Domazakis, E. S. (1985) Solar radiation correlations for the Athens-Greece area. Solar Energy 35, 259-269. Lalas, D. P., Pissimanis, D. K. and Notaridou, V. A. (1982) Methods of estimation of the intensity of solar radiation on a tilted surface and tabulated data for 30”, 45’ and 60” in Greece. Technica Chronica, Scientific Journal of the Technical Chamber of Greece, Section B 2, 129-178. Mosalam Shaltout, M. A. and Tadros, M. T. Y. (1994) Typical solar radiation year for Egypt. Renewable Energy 4, 387-393. Notaridou, V. and Lalas, D. P. (1979) The distribution of global and net radiation over Greece. SoZar Energy 22, 505-514. Pet&is, M. (1995) A Sofnyare Tool for the Creation of a Typical Meteorological Year. Final Report for the PASCOOL Project of CEC. Petrie, W. R. and McClintock, M. (1978) Determining typical weather for use in solar energy simulations. Solar Energy 21, 55-59.
M. Petrakis et aLlSoftware tool for creating a meteorological Pissimanis, D., Karras, G., Notaridou, V. and Gavra, K. (1988) The generation of a ‘Typical Meteorological year’ for the city of Athens. Solar Energy 40, 405-411.
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Schweitzer, S. (1978) A possible ‘average’ weather year on Israel’s coastal plain for solar system simulations, Technical Note. Solar Energy 21, 5 11-5 15.