- Email: [email protected]
Analysis of a solarimetric database for Mexico and comparison with the CSR model
Renewable Energy 75 (2015) 21e29
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Analysis of a solarimetric database for Mexico and comparison with the CSR model n c, D. Riveros-Rosas a, *, C.A. Arancibia-Bulnes b, R. Bonifaz a, M.A. Medina a, R. Peo M. Valdes a noma de M Instituto de Geofísica, Universidad Nacional Auto exico, Ciudad Universitaria, M exico D.F. 04510, Mexico noma de M Instituto de Energías Renovables, Universidad Nacional Auto exico, Temixco, Morelos 62580, Mexico c Plataforma Solar de Hermosillo, Departamento de Ingeniería Industrial, Universidad de Sonora, Hermosillo, Sonora 83000, Mexico a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 December 2013 Accepted 8 September 2014 Available online
An analysis of the solar radiation database from the network of meteorological stations of the Mexican National Weather Service was carried out. The database includes global irradiance measurements from the oldest 136 stations distributed in the Mexican territory. The consistency of data acquisition from the launch of the stations until 2010 was checked, and visual inspection of graphs of daily irradiance data was carried out, for the first three years operation, to ensure quality and reliability of the data. The results indicate that less than half of the stations have an adequate regularity for data records. With a limited number of selected stations that passed the applied quality criteria, evaluation of hourly and daily global irradiations was carried out. These results were compared to satellite derived data for Mexico, based on NREL's CSR model. The results of the comparison show a good agreement between measured and modeled daily global solar irradiation with an average RMSE of 6.6%. Based on the selected stations, a daily irradiation mean of 5.5 kWh/m2/day is estimated for the country. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Solar energy resource Solarimetric database CSR model
1. Introduction In recent years the solar industry is experiencing a growth not seen before. Solar energy technologies, both photovoltaic and thermal, are being deployed at accelerated rates around the world. Reliable solar resource information is a crucial input to the successful planning of solar projects, and therefore increased effort is necessary to evaluate this resource where enough quality data is lacking. It is a common place to say that Mexico has a very strong potential for the utilization of solar technologies due to its high insolation, which is by far its largest renewable energy resource. The country is located in a privileged position in the northern hemisphere, between latitudes 14 320 and 32 430 , including the line of the tropic of Cancer, having nearly 2 million square kilometers area. In early works, it was estimated that average solar irradiance over this territory is higher than the 4.6 KWh/m2/day [1]. However, to date there is a lack of precise knowledge about this resource. Therefore, in order to realize the very important potential
* Corresponding author. E-mail address: driveros@geofisica.unam.mx (D. Riveros-Rosas). http://dx.doi.org/10.1016/j.renene.2014.09.013 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
of the country, it is essential to gain a better knowledge of the distribution of solar radiation on the territory. Several maps and tables of solar radiation in Mexico have been published by different authors [1e6]. However, all of them are based on estimating methods of different types, which have been compared to ground measured solarimetric data for only a handful of locations in the country. Therefore, it is presumed that uncertainties of these models are high [7]. As new and more precise methods are being developed for the indirect evaluation of the solar resource by several researchers [8e12], there is an increasing need of validated data that can be used for comparison purposes. The Mexican National Weather Service (SMN; Servicio Meteorgico Nacional) has several networks of sensors to measure olo climate parameters. Two decades ago SMN initiated an automatic network of atmospheric monitoring stations (EMAS), each of them with a thermopile pyranometer for measuring global solar radiation. The location of these stations is not optimal for the needs of solar resource assessment, because their distribution was made with hydro-meteorological criteria. However, their large number (currently, 136 stations) provides a wide coverage of the different natural regions (Fig. 1) and could fulfill this purpose. Unfortunately, a periodic calibration program was not established for the radiometers in this network, and the equipment has not been calibrated since installation. However, it is to be expected that the first
22
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Fig. 1. Location of EMAS in the Mexican territory.
years of operation of each sensor represent reliable information that could be used for the calibration of empirical model parameters related to geographic and climatic conditions for each station [13]. Two years ago, a project to analyze the existing database from the EMAS and to calibrate the radiometers was funded by the Secretaría de Energía (SENER) and the Consejo Nacional de Ciencia y Tecnología (CONACYT). This is a joint project between SMN and the noma de M Instituto de Geofísica of Universidad Nacional Auto exico (IGF-UNAM), which is recognized as a Regional Radiation Center for the region RA-IV by the World Meteorological Organization, and keeps an absolute radiometer calibrated with the set of radiometers from the world radiation center [14]. One of the main conclusions to date is that the database as a whole shows many problems and inconsistencies. A second project, financed by UNAM is in course, to identify a subset of good quality data from this database that can be used for the purpose of preliminary validation of solar radiation models. In the present work preliminary results from the assessment of the SMN solarimetric database are presented, as well as a comparison with data calculated with the Climatological Solar Radiation model (CSR), which available to the public from the SWERA web site, maintained by NREL [15]. The comparison is made for monthly and yearly averages of daily global horizontal irradiation, for each station. 2. Methodology 2.1. Analysis of SMN data The database (from 1998 to 2011) [16] was obtained from a total of 136 solarimetric stations that record global solar irradiance at 10 min intervals. The installed radiometers sensors are mainly Kipp & Zonen CMP11 pyranometers, each one, factory calibrated at the moment of installation. Table 1 shows the names and locations of the stations considered in this paper and Fig. 1 shows the distribution of stations in the Mexican territory. A first step was to identify inconsistent data such as out of range dates, and also null, unphysically high, and negative irradiances (beyond instrument uncertainty). As a result of this, some records were eliminated from several stations, amounting to 0.11% of the total records. Subsequently, the remaining data was checked for completeness; i.e., to identify missing records. Important gaps were identified on the data in this procedure, and a table of percent records
from the first three years of operation of each station was generated. This enabled to identify the stations which had a regular operation during that period of time. For further analysis, it was preferred to utilize stations with more than 80% of their full data. Nevertheless, some stations with fewer records but that showed very good consistency were also considered. The next analysis consisted in a visual inspection of the daily curves. Graphs of global irradiance were obtained for a specific day, for every week of the considered period, in order to visually inspect the quality of the data in the database for each station. Some criteria to check were the following: - Acceptable behavior of maxima and minima values over the three years, according with the altitude of the station. For low altitude stations, systematic global irradiance values over 1000 W/m2 are not expected, as neither are values over 1300 W/ m2, for altitudes higher than 1000 m over sea level. - Identify errors in the acquisition data process. - Acceptable seasonal behavior according to the solar geometry for clear days along the years, according the geographical position of the station. - Identify regular shadows over the sensor due to presence of buildings, trees, and other structures in the surroundings of the station. - Whenever possible, a visual inspection of the station site was carried out in order to verify the true physical situation of the sensor and its surroundings, to verify the findings of the preceding point. Similar criteria can be found in other works that validate the quality of daily solar radiation data [17,18]. One of the basic principles is that the daily irradiation cannot pass the irradiation received on the top of the atmosphere and for clear days must be, in a first approximation, similar to the simulation of clear skies irradiation. This analysis allowed identifying stations whose records from the first three operation years can be used for the development of solar resource assessment projects [19]. 2.2. Comparison with CSR model From the above described process 35 stations were considered suitable and monthly averages of hourly and daily irradiation over the corresponding three year period were calculated for them. For
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
23
Table 1 Percentage of days with complete records in the first three years of operation of the EMAS stations of the SMN. Name (State)
% Daily total
Name (State)
% Daily total
Villa Ahumada (Chihuahua) nagas (Coahuila) Cuatro Cie moc (Chihuahua) Ciudad Cuauhte Ojinaga (Chihuahua) San Luis Rio Colorado (Sonora) Tlapa De Comonfort (Guerrero) Nicolas Bravo (QROO) Chinatú (Chihuahua) Atoyac (Guerrero) Maguarichi (Chihuahua) Sonoyta (Sonora) El Vergel (Chihuahua) San Quintín (BC) La Flor (Durango) Santa Rosalía (BCS) San Juanico (BCS) Matamoros (Tamaulipas) Huejutla (Hidalgo) Urique (Chihuahua) ENCB II IPN (DF) Nogales (Sonora) Guachochi (Chihuahua) Angamacutiro (Michoac an) Mexicali (BC) La Florida (Zacatecas) n (BCS) Cd. Constitucio Los Colomos (Jalisco) Cancún (QROO) Presa Allende (Guanajuato) San Juan De Guadalupe (Durango) Campeche (Campeche) Chetumal (QROO) Huamantla (Tlaxcala) n (Jalisco) Tizapa Tezontle (DF) Zacatecas (Zacatecas) Atlacomulco (Edomex) Zimap an (Hidalgo) Huauchinango (Puebla) Alamos (Sonora) Presa La Cangrejera (Veracruz) Villa Ocampo (Durango) Tuxpan (Veracruz) Pachuca (Hidalgo) Dzilam (Yucat an) Monclova (Campeche) Bahía de los Angeles (BC) Jalapa (Veracruz) n (Veracruz) Cd. Alema Venustiano Carranza (Coahuila) Yohaltum (Campeche) Acayucan (Veracruz) Cd. Del Carmen (Campeche) Jocotepec (Jalisco) Chinipas (Chihuahua) UNITEC Tecamachalco (Puebla) Gustavo Díaz Ordaz (BCS) Nevado De Toluca (Edomex) Celestún (Yucat an) Jaumave (Tamaulipas) Cabo San Lucas (BCS) Basaseachi (Chihuahua) taro) Huimilpan (Quere Altamira (Tamaulipas) pez Zamora (BC) Presa Emilio Lo n (Jalisco) Río Tomatla Ciudad Fernandez (SLP) Caborca (Sonora)
93.16% 91.52% 90.61% 90.25% 89.61% 89.52% 89.52% 89.15% 88.61% 88.51% 88.51% 88.33% 88.24% 88.15% 87.97% 87.24% 87.15% 86.87% 86.14% 86.14% 86.14% 86.05% 85.87% 85.69% 85.41% 84.78% 84.59% 84.50% 84.32% 84.23% 84.14% 84.05% 83.77% 83.68% 83.41% 83.41% 83.32% 82.95% 82.95% 82.86% 82.68% 82.59% 82.50% 82.41% 82.13% 82.04% 81.86% 81.86% 81.13% 80.86% 80.58% 80.40% 80.31% 80.22% 80.13% 80.13% 77.94% 77.30% 75.84% 74.20% 72.38% 71.74% 71.29% 70.46% 69.37% 69.28% 69.28% 68.73%
n) Rio Lagartos (Yucata Izúcar De Matamoros (Puebla) Alvarado (Veracruz) Petacalco (Guerrero) El Fuerte (Sinaloa) n) Presa El Cuchillo (Nuevo Leo Ciudad Delicias (Chihuahua) Ecoguardas (DF) Ciudad Mante (Tamaulipas) Huichapan (Hidalgo) Puerto Angel (Oaxaca) Paraíso (Tabasco) Nueva Rosita (Coahuila) Sian Ka'an (QROO) Acaponeta (Nayarit) Parque Ixta-Popo (Edomex) Bahía De Kino (Sonora) Escuintla (Chiapas) Acapulco (Guerrero) n (Veracruz) Centro De Previsio rida (Yucata n) Me Morelos-Muzquiz (Coahuila) Zacualtipan (Hidalgo) Villagr an (Tamaulipas) Oxkutzcab (Yucat an) Zihuatanejo (Guerrero) Jose Maria Morelos (QROO) Imta (Morelos) Cerro Catedral (Edomex) Presa Madín (Edomex) Calvillo (Aguascalientes) Citlaltepec (Veracruz) Cemcas (Edomex) Santa Cecilia (Coahuila) Pinotepa Nacional (Oaxaca) Rumorosa (BC) nez (Chihuahua) Jime Cd. Valles (SLP) Iguala (Guerrero) n) Uruapan (Michoaca cora (Sonora) Ye Cd. Altamirano (Guerrero) ~ a (Bc) Catavin Tepoztl an (Morelos) Nochistl an (Oaxaca) Las Vegas (Durango) Esc arcega (Campeche) Palenque (Chiapas) Chapala (Jalisco) Matias Romero (Oaxaca) Agustín Melgar (Durango) Calakmul (Campeche) SMN (DF) Cozumel (QROO) n) Apatzing an (Michoaca Matehuala (Slp) Tantaquin (Yucat an) n (Quintana Roo) La Unio Presa Abelardo L. Rodriguez (BC) Miahuatl an (Oaxaca) Tres Marías (Morelos) San Fernando (Tamaulipas) ENCB IPN (DF) n (Puebla) Teziutla Ixtlan Del Rio (Nayarit) rdoba (Veracruz) Co Obispo (Sinaloa) San Juan (Sinaloa)
67.91% 67.73% 66.00% 65.91% 65.63% 65.54% 63.45% 63.35% 62.99% 62.81% 62.08% 62.08% 61.53% 60.71% 60.62% 59.89% 58.80% 56.34% 56.15% 54.42% 52.96% 52.51% 50.50% 50.50% 50.14% 48.86% 44.76% 43.39% 42.84% 42.11% 41.66% 41.66% 40.66% 40.47% 39.93% 39.74% 39.56% 39.29% 39.02% 39.02% 38.74% 38.29% 37.56% 37.56% 35.82% 34.64% 30.36% 28.90% 28.62% 28.62% 27.44% 25.71% 25.71% 25.52% 24.79% 23.52% 22.79% 18.05% 17.87% 17.59% 16.77% 15.77% 14.59% 7.47% 6.56% 1.73% 1.00% 0.00%
24
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
resolution. This model is simplified version of METSTAT model used to produce the National Solar Radiation Database (NSRDB) for the United States [21]. The CSR model uses as inputs climatological parameters including cloud cover, aerosol optical depth, precipitable water vapor, ozone, surface albedo and atmospheric pressure. In order to carry out the comparison, data were obtained from the SWERA web site for the geographic coordinates of each of the considered stations. The compared data were monthly average global daily irradiation (IGH,m for measured data and IGH-CSR,m for CSR data), obtained for each month (m) as an average over all days available (N) with daily global irradiation (IGHd) in that month, from the three years of measured data from SMN.
IGH;m ¼ Fig. 2. Yearly total records for 21 solarimetric stations from SMN.
this purpose, data for each hour was integrated from the database for each station. The monthly averaged daily irradiation values so obtained were compared to satellite derived data for Mexico, based on the CSR model, which are reported on the SWERA web site maintained by NREL [15]. We think this is pertinent, as the mentioned database is widely available to the public at no cost, and therefore is representative of the type of information derived from satellite models, that exists for Mexico to date. The results presented in the SWERA database have been obtained by application of the CSR model [20] to data for the period 1985e1991, and have a 40 km spatial
N 1 X I N i¼1 GHd;i
(1)
The parameters evaluated for comparison were the yearly root mean square error (RMSE), and the yearly mean normalized bias error (MNBE), given by.
"
12 2 1 X RMSE ¼ IGH;m IGHCSR;m 12 m¼1
#1=2 (2)
and
"
# 12 IGHCSR;m IGH;m 1 X MNBE ¼ 100% 12 m¼1 IGH;m
Fig. 3. Daily number of records for three stations from SMN over a 3 year period.
(3)
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
25
Fig. 4. EMAS stations from SMN selected for comparison.
3. Results and discussion 3.1. Validation of data from SMN In the initial review of the data contained in the database from SMN, approximately 40 million records of solar radiation were found, from which 45,871 records were found with inconsistent date stamps (0.11%). Fig. 2 shows the results for 21 stations from the compiled tables. Stations shown are those that had a higher number of data for the time considered in the database, from 1999 to 2011. The graph shows an overall decline in the number of records from the period 2004e2007. A similar process was performed to obtain daily frequency tables in all stations during the first 3 years of operation, as shown in
Table 1. This table contains the 84 stations that showed more than 60% of days with complete records, 56 of them had more than 80% complete records. Fig. 3 shows some examples of typical stations with different percentages of records, such as Villa Ahumada, with 93.16%, Atlacomulco, with 83.32%, and Eco-Guardas, with 63.35%. Once the stations that recorded more than 80% of data had been identified, graphs of daily irradiance were made for a specific day of the week during the first 3 years. Some stations were discarded because they presented measurement errors or regular shading during certain parts of the year. A few stations with as low as 56% of records were taken into account because, despite of the gaps in the data, they were good according to the other criteria, and gave consistent averages. These was the case for Bahia Kino, SON (59%), Puerto Angel, OAX (62%), Presa el Cuchillo, NL (65%), Altamira, TAMPS (70%) and Jaumave, TAMPS (74%).
Table 2 EMAS stations from SMN selected for the present study. Name
Lat. N
Long W
Alt. (m.s.l)
Initial date
Final date
Name
Lat. N
Long W
Alt. (m.s.l)
Initial date
Final date
BAHIA DE LOS ANGELES MEXICALI SAN QUINTIN CD. CONSTITUCION SANTA ROSALIA CAMPECHE CD. DEL CARMEN MONCLOVA YOHALTUM CIUDAD CUAUHTEMOC CHINIPAS MAGUARICHI URIQUE CUATRO CIENEGAS VENUSTIANO CARRANZA TEZONTLE (DF) SAN JUAN DE GUADALUPE TLAPA DE COMONFORT
28.896 32.667 30.532 25.010 27.338 19.836 18.648 18.057 19.014 28.397 27.393 27.858 27.216 26.990 27.519 19.385 24.631 17.549
113.560 115.291 115.937 111.663 112.274 90.507 91.823 90.821 90.311 106.839 108.536 107.994 107.917 102.038 100.617 99.100 102.774 98.563
10 50 32 28 53 11 8 100 80 2100 431 1663 577 725 264 2358 1531 1060
04/05/2000 11/29/1999 04/01/2000 11/29/1999 11/29/1999 11/29/1999 11/29/1999 04/08/2008 04/08/2008 04/02/2008 05/04/1999 05/25/1999 04/28/1999 06/06/2008 06/05/2008 02/27/2000 07/10/2008 04/10/2008
04/06/2003 11/29/2002 04/02/2003 11/29/2002 11/29/2002 11/29/2002 11/29/2002 04/09/2011 04/09/2011 04/03/2011 05/04/2002 05/25/2002 04/28/2002 06/07/2011 06/06/2011 02/27/2003 07/11/2011 04/11/2011
PRESA ALLENDE PACHUCA JOCOTEPEC TIZAPAN ANGAMACUTIRO PRESA EL CUCHILLO PUERTO ANGEL
20.848 20.097 20.283 20.169 20.125 25.733 15.671 18.866 18.501 27.022 28.750 31.298 25.886 22.388 18.189 21.391 22.747 32.424
100.825 98.714 103.416 103.044 101.723 99.321 96.497 97.722 88.328 108.938 111.137 110.914 97.519 97.959 96.098 88.904 102.506 114.798
1915 2423 1506 1503 1730 134 91 2047 14 409 160 1275 4 3 107 2 2270 39
03/11/2000 03/04/2000 06/20/1999 07/19/1999 11/29/1999 11/29/1999 09/30/1999 05/29/1999 11/29/1999 05/11/1999 01/06/2004 03/19/2008 11/29/1999 05/20/1999 11/29/1999 04/07/2008 11/29/1999 03/19/2008
03/12/2003 03/05/2003 06/20/2002 07/19/2002 11/29/2002 11/29/2002 09/30/2002 05/29/2002 11/29/2002 05/11/2002 01/06/2007 03/20/2011 11/29/2002 05/20/2002 11/29/2002 04/08/2011 11/29/2002 03/19/2011
U. T. DE TECAMACHALCO CHETUMAL ALAMOS BAHIA DE KINO NOGALES MATAMOROS ALTAMIRA CD. ALEMAN DZILAM ZACATECAS SAN LUIS RIO COLORADO
26
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Fig. 5. Monthly average of daily irradiation, for selected stations, from measured data and CSR model. Irradiation units in kWh/m2.
Fig. 6. Locations for selected stations and their RMSE value by categories.
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Some stations presented staggered data on most of their records, so they were not taken into account for the comparison with CSR model. Of all the considered stations, 36 were found to show an adequate quality of their data, and were used for calculation of average daily irradiation as well as hourly averages. 3.2. Comparison with CSR model The 36 selected stations are shown in Fig. 4, and in Table 2 the analyzed periods of time are presented. It is necessary to point out that the period of time spanned by CSR differs significantly from the periods of the measured data. In general, the comparison shows a good agreement between the measured and model values, as can be observed from the representative examples presented in Fig. 5. The statistical comparison gives RMSE values which average 6.6% ± 2.0%. The highest n, VER, with 11.6%, being dispersion was observed for Ciudad Alema this the only case with a deviation above 10%. The lowest RMSE was for Mexicali, BC, with 3.0%. The MNBE (bias) oscillated from 7.3% to 8.8%. It is interesting to see that data suggest a trend to increasing RMSE as the latitude diminishes, as can be observed in Figs. 6 and 7. A similar trend is observed with the negative values of the MNBE. Both trends could be associated with a more complex weather at lower, rainier, latitudes, as compared to the mostly arid north
Fig. 7. (Upper) Percent RMSE for every station with respect to the CSR model as a function of latitude. (Lower) Percent RMSE with respect to the CSR model as a function of average annual rainfall.
27
latitudes of the country. In Table 3 the results are synthesized for all stations. From the selected stations, the yearly averaged daily irradiation on the country can be estimated as 5.5 ± 0.5 kWh/m2/day. This value more or less corresponds with different estimations that have been made in the past, and in particular gives a deviation of just 0.6% from the CSR model, considering the same locations. The mean difference in yearly average between the selected stations is of n, with 3.4 ± 2.3%. The largest difference corresponds to Cd. Alema n, with 0.3%. 7.8%, while the lowest corresponds to Cd. Constitucio It is interesting to note how the differences between the modeled and measured values are distributed in Mexico. It may be reasonable to try to explain the accuracy of the model according with the geographical conditions on the country. A classification of the stations according to their RMSE values has been done (Fig. 6), where three characteristics zones can be identified. In the first zone, corresponding to the northwest of Mexico (the states of Sonora and Baja California), the RMSE values are smaller than 5% (First two categories in Fig. 6). The second zone covers the mountainous areas of central and northern-central Mexico, where the RMSE values are in the range of 5e7% (Categories 3 and 4 in Fig. 6). Finally, the third zone corresponds to the Yucatan Peninsula, where values are in the range of 4e7%. The first area can be identified as part of a geomorphologically well defined climatic unit, which is shared with the southwest of the United States territory. Thus, the low error level found in this zone may be due to the fact that the CSR model has been tuned mainly with data for the United States. The second zone covers regions that are not similar to their counterparts in the USA, as most of Mexico is located in an intertropical region between two large bodies of water (Gulf of Mexico and Pacific Ocean). The climatic factors may affect the spatial and temporal distribution of solar radiation at the surface. As seen in Fig. 5 (middle row), for stations on this region, differences between daily irradiation surface values and model values can be either positive or negative. There are even stations with bimodal distributions, resulting from the wide variety of climates in the region. Finally, in the area located in the Yucatan peninsula, the diversity of RMSE values is remarkable and contrasts with the uniformity of the environment and climate, where the spatiotemporal distribution of solar radiation is mainly latitudinal. Two factors could be the reason for this phenomenon: first, the geology is mainly limestone, and second, the stations are in areas that vary from devastated vegetation, abundant vegetation, or sites dominated by pastures and farmland. This can affect the albedo and the atmospheric components distribution, causing an underestimation of the intensity of solar radiation by the model. This can be appreciated in the graphs for that region in Fig. 5 (bottom row) where, no matter the value of RMSE, surface measured values are always greater than predicted by the model. We think that the distribution of RMS errors in the map may be due to local climatic conditions of each region (altitude, rainfall, slop orientation, sea proximity). For instance, stations with low RMSE values are mainly in a physiographic unit in the northwest region of the country. Furthermore in the mountainous regions of the RMSE values of the stations are high. We also found higher values in stations close to the coast as is the case of Ciudad del Carmen and Puerto Angel. As an example of the influence of local physical conditions, we can see in Fig. 7, the graphs of annual average RMS values against latitude and total precipitation. In each graph we can't indentify a well defined behavior, but a trend with the respective parameters is observed. Another explanation may have to do with the variation of resolution of satellite images due the relative position of geostationary sensor to different regions in Mexico. However, if this was so the resolution should systematically decrease as we move away from the nadir of the satellite, in
28
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Table 3 Statistics for measured data and CRS model. Name (State)
GHI SMN kWh/m2
GHI CSR-NREL kWh/m2
Annual difference
MNBE (BIAS)
RMSE
Bahía De Los Angeles (BC) Mexicali (BC) San Quintín (BC) n (BCS) Cd. Constitucio Santa Rosalía (BCS) Campeche (CAMP) Cd. Del Carmen (CAMP) Monclova (CAMP) Yohaltum (CAMP) Ciudad Cuauhtemoc (CHI) Chinipas (CHI) Maguarichi (CHI) Urique (CHI) Cuatro Cienegas (COAH) Venustiano Carranza (COAH) Tezontle (DF) San Juan De Guadalupe (DGO) Tlapa De Comonfort (GRO) Presa Allende (GTO) Pachuca (HGO) Jocotepec (JAL) n (JAL) Tizapa Angamacutiro (MICH) Presa El Cuchillo (NL) Puerto Angel (OAX) U. T de Tecamachalco (PUE) Chetumal (QROO) Alamos (SON) Bahia De Kino (SON) Nogales (SON) San Luis Rio Colorado (SON) Matamoros (TAMPS) Altamira (TAMPS) Cd. Aleman (VER) Dzilam (YUC) Zacatecas (ZAC)
5.99 5.63 5.22 5.93 6.06 5.52 5.69 5.16 5.30 5.77 5.49 5.58 5.49 5.91 5.39 4.88 6.02 5.78 5.74 5.79 5.96 5.92 5.83 5.13 5.96 5.91 5.47 5.80 5.65 5.73 5.50 4.72 4.98 4.70 5.51 5.67
5.917 5.686 5.349 5.946 5.918 5.165 5.288 5.043 5.416 5.880 5.777 5.675 5.737 5.726 5.426 5.216 6.159 5.982 5.704 5.362 5.615 5.646 5.892 5.214 5.529 5.627 5.152 5.829 5.712 5.887 5.750 4.860 5.097 5.066 5.254 5.504
1.28% 1.07% 2.43% 0.29% 2.29% 6.36% 7.08% 2.31% 2.11% 1.87% 5.19% 1.71% 4.56% 3.10% 0.59% 6.93% 2.29% 3.51% 0.63% 7.41% 5.85% 4.68% 1.12% 1.64% 7.21% 4.78% 5.75% 0.50% 1.05% 2.79% 4.54% 2.90% 2.44% 7.79% 4.70% 2.91%
1.2% 1.3% 2.3% 0.2% 2.6% 6.3% 6.7% 2.0% 2.0% 1.4% 5.5% 0.9% 4.4% 3.9% 0.6% 7.2% 1.8% 3.1% 0.4% 7.3% 6.1% 4.9% 1.2% 3.2% 7.2% 4.7% 5.1% 0.3% 1.7% 2.6% 4.2% 4.5% 3.4% 8.80% 4.5% 2.9%
3.7% 3.0% 5.2% 3.4% 3.3% 7.5% 9.2% 6.1% 4.7% 8.1% 8.0% 6.7% 7.2% 5.4% 5.5% 9.0% 7.5% 6.4% 6.4% 8.3% 8.8% 6.8% 6.3% 6.7% 9.3% 6.4% 8.0% 6.6% 4.4% 7.0% 5.7% 7.2% 5.1% 11.6% 6.2% 8.0%
both axes (latitude and longitude). This is not evident in Fig. 6, as pixel resolution should decrease in the west direction, whereas RMS values show a better agreement between the modeled and measured values for this region. Note that obtaining more conclusive results requires a larger amount of reliable solarimetric data over the whole country. It is expected that such data will be available in the next few years, as a consequence of the pyranometers calibration program in progress.
4. Conclusions A preliminary analysis of the database from the EMAS stations of the SMN was carried out. Large gaps were identified in the database in the period 2004e2007. This problem is generalized in most of stations. From the analysis of daily number of records, for the first three years of operation of every station, those with higher than 80% complete data were identified. The number of stations that fulfilled this requirement was 56 (41%), of a total of 136. After examining the daily irradiance graphs for this group of stations, we determined that 36 of them (26%) appear to have valid data, free from systematic errors, and are therefore appropriate for comparison purposes. The daily totals and monthly averages were processed for those stations. From these stations, an average daily irradiation of 5.5 ± 0.5 kWh/m2/day is estimated for the country. The selected data was compared with results of the CSR model, obtained from the SWERA web site, for the same locations. Yearly RMSE values with respect to measured data average 6.6% ± 2.0%. The MNBE (bias) oscillated from 7.3% to 8.8%.
A conclusion of the error analysis is the need for reliable surface observations, to achieve tuning of the models to improve their performance. The different climate elements are crucial to the spatiotemporal distribution of solar radiation. These elements are critical in Mexico where short distances may represent considerable variations in altitude and vegetation. The above analysis is preliminary, and more conclusive results are expected in the future, as the whole network of radiometers is being calibrated and installation errors are being corrected. Acknowledgments noma de Me xico for Our thanks to Universidad Nacional Auto their financial support through grant PAPITT A100312-2. References pez S. “Radiacio n global en la república mexicana mediante [1] Almanza R, Lo n”. Series Instituto de Ingeniería, vol. 357. UNAM; 1975 [in datos de insolacio Spanish]. s M. Satellite derived solar irradiance over Mexico. [2] Galindo I, Castro S, Valde ATMOSFERA 1991;4(3):189e201. s M. Me xico, “Atlas de Radiacio n Solar”. Programa Uni[3] Galindo I, Valde xico: UNAM; 1992 [in Spanish]. versitario de Energía. Me n Solar Global en la Republica Mexicana: [4] Galindo I, Cifuentes G. “Radiacio xico: UNAM; Valores Horarios Medios”. Programa Universitario de Energía. Me 1996 [in Spanish]. [5] Estrada-Cajigal V, Almanza R. “Irradiaciones global directa y difusa, en su n directa normal en la perficies horizontales e inclinadas, así como irradiacio republica mexicana” Instituto de Ingeniería. UNAM SID-646; 2005 [in Spanish]. n de los recursos energe ticos renovables [6] Laboratorio Nacional para la evaluacio xico, IIE, Me xico. 2011. http://sag01.iie.org.mx/eolicosolar/Default.aspx. en Me
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29 n M, Riveros-Rosas D, Arancibia-Bulnes CA, Bonifaz R. “The solar [7] Valdes-Barro resource assessment in Mexico: State of Art” ISES Solar World Congress 2013, xico, Nov 4e7. 2013. Cancun, Me [8] Gueymard Christian A, Myers Daryl R. Evaluation of conventional and highperformance routine solar radiation measurements for improved solar resource, climatological trends, and radiative modeling. Sol Energy 2009;83: 171e85. [9] Gueymard Christian A, Wilcox Stephen M. Assessment of spatial and temporal variability in the US solar resource from radiometric measurements and predictions from models using ground-based or satellite data. Sol Energy 2011;85:1068e84. [10] Nottrott Anders, Kleissl Jan. Validation of the NSRDBeSUNY global horizontal irradiance in California. Sol Energy 2010;84:1816e27. ~ igo, Gasto n Martín, Ferna ndez-Peruchena Carlos, Moreno Sara, [11] Pagola In Ramírez Lourdes. New methodology of solar radiation evaluation using free access databases in specific locations. Renew Energy 2010;35:2792e8. David, Zelenka Antoine. [12] Perez Richard, Moore Kathleen, Wilcox Steve, Renne Forecasting solar radiation e preliminary evaluation of an approach based upon the national forecast database. Sol Energy 2007;81:809e12. pez G. Solar resources estimation [13] Bosch JL, Batlles FJ, Zarzalejo LF, Lo combining digital terrain models and satellite images techniques. Renew Energy 2010;35:2853e61.
29
[14] Finsterle W. “WMO International Pyrheliometer comparison IPC-XI 27 September e 15 October 2010 Davos, Switzerland Final Report” WMO IOM. Report No. 108. 2011. [15] Solar and wind energy resource assessment. USA: NREL; 2006. http://maps. nrel.gov/swera. [16] Valdes M, Riveros-Rosas D, Bonifaz R. “Solarimetric Network for Solar Radiation Assessment in Mexico” SolarPACES2012, Marrakech, Morocco, Sep. 11e14. 2012. [17] Polo J, Zarzalejo LF, Ramirez L, Espinar B. Iterative filtering of ground data for qualifying statical models for solar irradiance estimation from satellite data. Sol Energy 2006;80:240e7. L, Me nard L, Wald L. A web service for controlling the [18] Geiger M, Diabate quality of measurements of global solar irradiation. Sol Energy 2002;73(6): 475e80. n Solar, Instituto de Geo[19] Energía solar disponible, Observatorio de Radiacio física. http://www.geofisica.unam.mx/ors/energiaSolarprueb1.php [20] Maxwell EL, George RL, Wilcox SM. A climatological solar radiation model. In: Proceedings of the 1998 American Solar Energy Society Annual Conference. Albuquerque, NM June 14e17; 1998. [21] Maxwell EL. METSTAT-the solar radiation model used in the Production of the National Solar Radiation Data Base (NSRDB). Sol Energy 1998;62(4):263e79.
Contents lists available at ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Analysis of a solarimetric database for Mexico and comparison with the CSR model n c, D. Riveros-Rosas a, *, C.A. Arancibia-Bulnes b, R. Bonifaz a, M.A. Medina a, R. Peo M. Valdes a noma de M Instituto de Geofísica, Universidad Nacional Auto exico, Ciudad Universitaria, M exico D.F. 04510, Mexico noma de M Instituto de Energías Renovables, Universidad Nacional Auto exico, Temixco, Morelos 62580, Mexico c Plataforma Solar de Hermosillo, Departamento de Ingeniería Industrial, Universidad de Sonora, Hermosillo, Sonora 83000, Mexico a
b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 December 2013 Accepted 8 September 2014 Available online
An analysis of the solar radiation database from the network of meteorological stations of the Mexican National Weather Service was carried out. The database includes global irradiance measurements from the oldest 136 stations distributed in the Mexican territory. The consistency of data acquisition from the launch of the stations until 2010 was checked, and visual inspection of graphs of daily irradiance data was carried out, for the first three years operation, to ensure quality and reliability of the data. The results indicate that less than half of the stations have an adequate regularity for data records. With a limited number of selected stations that passed the applied quality criteria, evaluation of hourly and daily global irradiations was carried out. These results were compared to satellite derived data for Mexico, based on NREL's CSR model. The results of the comparison show a good agreement between measured and modeled daily global solar irradiation with an average RMSE of 6.6%. Based on the selected stations, a daily irradiation mean of 5.5 kWh/m2/day is estimated for the country. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Solar energy resource Solarimetric database CSR model
1. Introduction In recent years the solar industry is experiencing a growth not seen before. Solar energy technologies, both photovoltaic and thermal, are being deployed at accelerated rates around the world. Reliable solar resource information is a crucial input to the successful planning of solar projects, and therefore increased effort is necessary to evaluate this resource where enough quality data is lacking. It is a common place to say that Mexico has a very strong potential for the utilization of solar technologies due to its high insolation, which is by far its largest renewable energy resource. The country is located in a privileged position in the northern hemisphere, between latitudes 14 320 and 32 430 , including the line of the tropic of Cancer, having nearly 2 million square kilometers area. In early works, it was estimated that average solar irradiance over this territory is higher than the 4.6 KWh/m2/day [1]. However, to date there is a lack of precise knowledge about this resource. Therefore, in order to realize the very important potential
* Corresponding author. E-mail address: driveros@geofisica.unam.mx (D. Riveros-Rosas). http://dx.doi.org/10.1016/j.renene.2014.09.013 0960-1481/© 2014 Elsevier Ltd. All rights reserved.
of the country, it is essential to gain a better knowledge of the distribution of solar radiation on the territory. Several maps and tables of solar radiation in Mexico have been published by different authors [1e6]. However, all of them are based on estimating methods of different types, which have been compared to ground measured solarimetric data for only a handful of locations in the country. Therefore, it is presumed that uncertainties of these models are high [7]. As new and more precise methods are being developed for the indirect evaluation of the solar resource by several researchers [8e12], there is an increasing need of validated data that can be used for comparison purposes. The Mexican National Weather Service (SMN; Servicio Meteorgico Nacional) has several networks of sensors to measure olo climate parameters. Two decades ago SMN initiated an automatic network of atmospheric monitoring stations (EMAS), each of them with a thermopile pyranometer for measuring global solar radiation. The location of these stations is not optimal for the needs of solar resource assessment, because their distribution was made with hydro-meteorological criteria. However, their large number (currently, 136 stations) provides a wide coverage of the different natural regions (Fig. 1) and could fulfill this purpose. Unfortunately, a periodic calibration program was not established for the radiometers in this network, and the equipment has not been calibrated since installation. However, it is to be expected that the first
22
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Fig. 1. Location of EMAS in the Mexican territory.
years of operation of each sensor represent reliable information that could be used for the calibration of empirical model parameters related to geographic and climatic conditions for each station [13]. Two years ago, a project to analyze the existing database from the EMAS and to calibrate the radiometers was funded by the Secretaría de Energía (SENER) and the Consejo Nacional de Ciencia y Tecnología (CONACYT). This is a joint project between SMN and the noma de M Instituto de Geofísica of Universidad Nacional Auto exico (IGF-UNAM), which is recognized as a Regional Radiation Center for the region RA-IV by the World Meteorological Organization, and keeps an absolute radiometer calibrated with the set of radiometers from the world radiation center [14]. One of the main conclusions to date is that the database as a whole shows many problems and inconsistencies. A second project, financed by UNAM is in course, to identify a subset of good quality data from this database that can be used for the purpose of preliminary validation of solar radiation models. In the present work preliminary results from the assessment of the SMN solarimetric database are presented, as well as a comparison with data calculated with the Climatological Solar Radiation model (CSR), which available to the public from the SWERA web site, maintained by NREL [15]. The comparison is made for monthly and yearly averages of daily global horizontal irradiation, for each station. 2. Methodology 2.1. Analysis of SMN data The database (from 1998 to 2011) [16] was obtained from a total of 136 solarimetric stations that record global solar irradiance at 10 min intervals. The installed radiometers sensors are mainly Kipp & Zonen CMP11 pyranometers, each one, factory calibrated at the moment of installation. Table 1 shows the names and locations of the stations considered in this paper and Fig. 1 shows the distribution of stations in the Mexican territory. A first step was to identify inconsistent data such as out of range dates, and also null, unphysically high, and negative irradiances (beyond instrument uncertainty). As a result of this, some records were eliminated from several stations, amounting to 0.11% of the total records. Subsequently, the remaining data was checked for completeness; i.e., to identify missing records. Important gaps were identified on the data in this procedure, and a table of percent records
from the first three years of operation of each station was generated. This enabled to identify the stations which had a regular operation during that period of time. For further analysis, it was preferred to utilize stations with more than 80% of their full data. Nevertheless, some stations with fewer records but that showed very good consistency were also considered. The next analysis consisted in a visual inspection of the daily curves. Graphs of global irradiance were obtained for a specific day, for every week of the considered period, in order to visually inspect the quality of the data in the database for each station. Some criteria to check were the following: - Acceptable behavior of maxima and minima values over the three years, according with the altitude of the station. For low altitude stations, systematic global irradiance values over 1000 W/m2 are not expected, as neither are values over 1300 W/ m2, for altitudes higher than 1000 m over sea level. - Identify errors in the acquisition data process. - Acceptable seasonal behavior according to the solar geometry for clear days along the years, according the geographical position of the station. - Identify regular shadows over the sensor due to presence of buildings, trees, and other structures in the surroundings of the station. - Whenever possible, a visual inspection of the station site was carried out in order to verify the true physical situation of the sensor and its surroundings, to verify the findings of the preceding point. Similar criteria can be found in other works that validate the quality of daily solar radiation data [17,18]. One of the basic principles is that the daily irradiation cannot pass the irradiation received on the top of the atmosphere and for clear days must be, in a first approximation, similar to the simulation of clear skies irradiation. This analysis allowed identifying stations whose records from the first three operation years can be used for the development of solar resource assessment projects [19]. 2.2. Comparison with CSR model From the above described process 35 stations were considered suitable and monthly averages of hourly and daily irradiation over the corresponding three year period were calculated for them. For
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
23
Table 1 Percentage of days with complete records in the first three years of operation of the EMAS stations of the SMN. Name (State)
% Daily total
Name (State)
% Daily total
Villa Ahumada (Chihuahua) nagas (Coahuila) Cuatro Cie moc (Chihuahua) Ciudad Cuauhte Ojinaga (Chihuahua) San Luis Rio Colorado (Sonora) Tlapa De Comonfort (Guerrero) Nicolas Bravo (QROO) Chinatú (Chihuahua) Atoyac (Guerrero) Maguarichi (Chihuahua) Sonoyta (Sonora) El Vergel (Chihuahua) San Quintín (BC) La Flor (Durango) Santa Rosalía (BCS) San Juanico (BCS) Matamoros (Tamaulipas) Huejutla (Hidalgo) Urique (Chihuahua) ENCB II IPN (DF) Nogales (Sonora) Guachochi (Chihuahua) Angamacutiro (Michoac an) Mexicali (BC) La Florida (Zacatecas) n (BCS) Cd. Constitucio Los Colomos (Jalisco) Cancún (QROO) Presa Allende (Guanajuato) San Juan De Guadalupe (Durango) Campeche (Campeche) Chetumal (QROO) Huamantla (Tlaxcala) n (Jalisco) Tizapa Tezontle (DF) Zacatecas (Zacatecas) Atlacomulco (Edomex) Zimap an (Hidalgo) Huauchinango (Puebla) Alamos (Sonora) Presa La Cangrejera (Veracruz) Villa Ocampo (Durango) Tuxpan (Veracruz) Pachuca (Hidalgo) Dzilam (Yucat an) Monclova (Campeche) Bahía de los Angeles (BC) Jalapa (Veracruz) n (Veracruz) Cd. Alema Venustiano Carranza (Coahuila) Yohaltum (Campeche) Acayucan (Veracruz) Cd. Del Carmen (Campeche) Jocotepec (Jalisco) Chinipas (Chihuahua) UNITEC Tecamachalco (Puebla) Gustavo Díaz Ordaz (BCS) Nevado De Toluca (Edomex) Celestún (Yucat an) Jaumave (Tamaulipas) Cabo San Lucas (BCS) Basaseachi (Chihuahua) taro) Huimilpan (Quere Altamira (Tamaulipas) pez Zamora (BC) Presa Emilio Lo n (Jalisco) Río Tomatla Ciudad Fernandez (SLP) Caborca (Sonora)
93.16% 91.52% 90.61% 90.25% 89.61% 89.52% 89.52% 89.15% 88.61% 88.51% 88.51% 88.33% 88.24% 88.15% 87.97% 87.24% 87.15% 86.87% 86.14% 86.14% 86.14% 86.05% 85.87% 85.69% 85.41% 84.78% 84.59% 84.50% 84.32% 84.23% 84.14% 84.05% 83.77% 83.68% 83.41% 83.41% 83.32% 82.95% 82.95% 82.86% 82.68% 82.59% 82.50% 82.41% 82.13% 82.04% 81.86% 81.86% 81.13% 80.86% 80.58% 80.40% 80.31% 80.22% 80.13% 80.13% 77.94% 77.30% 75.84% 74.20% 72.38% 71.74% 71.29% 70.46% 69.37% 69.28% 69.28% 68.73%
n) Rio Lagartos (Yucata Izúcar De Matamoros (Puebla) Alvarado (Veracruz) Petacalco (Guerrero) El Fuerte (Sinaloa) n) Presa El Cuchillo (Nuevo Leo Ciudad Delicias (Chihuahua) Ecoguardas (DF) Ciudad Mante (Tamaulipas) Huichapan (Hidalgo) Puerto Angel (Oaxaca) Paraíso (Tabasco) Nueva Rosita (Coahuila) Sian Ka'an (QROO) Acaponeta (Nayarit) Parque Ixta-Popo (Edomex) Bahía De Kino (Sonora) Escuintla (Chiapas) Acapulco (Guerrero) n (Veracruz) Centro De Previsio rida (Yucata n) Me Morelos-Muzquiz (Coahuila) Zacualtipan (Hidalgo) Villagr an (Tamaulipas) Oxkutzcab (Yucat an) Zihuatanejo (Guerrero) Jose Maria Morelos (QROO) Imta (Morelos) Cerro Catedral (Edomex) Presa Madín (Edomex) Calvillo (Aguascalientes) Citlaltepec (Veracruz) Cemcas (Edomex) Santa Cecilia (Coahuila) Pinotepa Nacional (Oaxaca) Rumorosa (BC) nez (Chihuahua) Jime Cd. Valles (SLP) Iguala (Guerrero) n) Uruapan (Michoaca cora (Sonora) Ye Cd. Altamirano (Guerrero) ~ a (Bc) Catavin Tepoztl an (Morelos) Nochistl an (Oaxaca) Las Vegas (Durango) Esc arcega (Campeche) Palenque (Chiapas) Chapala (Jalisco) Matias Romero (Oaxaca) Agustín Melgar (Durango) Calakmul (Campeche) SMN (DF) Cozumel (QROO) n) Apatzing an (Michoaca Matehuala (Slp) Tantaquin (Yucat an) n (Quintana Roo) La Unio Presa Abelardo L. Rodriguez (BC) Miahuatl an (Oaxaca) Tres Marías (Morelos) San Fernando (Tamaulipas) ENCB IPN (DF) n (Puebla) Teziutla Ixtlan Del Rio (Nayarit) rdoba (Veracruz) Co Obispo (Sinaloa) San Juan (Sinaloa)
67.91% 67.73% 66.00% 65.91% 65.63% 65.54% 63.45% 63.35% 62.99% 62.81% 62.08% 62.08% 61.53% 60.71% 60.62% 59.89% 58.80% 56.34% 56.15% 54.42% 52.96% 52.51% 50.50% 50.50% 50.14% 48.86% 44.76% 43.39% 42.84% 42.11% 41.66% 41.66% 40.66% 40.47% 39.93% 39.74% 39.56% 39.29% 39.02% 39.02% 38.74% 38.29% 37.56% 37.56% 35.82% 34.64% 30.36% 28.90% 28.62% 28.62% 27.44% 25.71% 25.71% 25.52% 24.79% 23.52% 22.79% 18.05% 17.87% 17.59% 16.77% 15.77% 14.59% 7.47% 6.56% 1.73% 1.00% 0.00%
24
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
resolution. This model is simplified version of METSTAT model used to produce the National Solar Radiation Database (NSRDB) for the United States [21]. The CSR model uses as inputs climatological parameters including cloud cover, aerosol optical depth, precipitable water vapor, ozone, surface albedo and atmospheric pressure. In order to carry out the comparison, data were obtained from the SWERA web site for the geographic coordinates of each of the considered stations. The compared data were monthly average global daily irradiation (IGH,m for measured data and IGH-CSR,m for CSR data), obtained for each month (m) as an average over all days available (N) with daily global irradiation (IGHd) in that month, from the three years of measured data from SMN.
IGH;m ¼ Fig. 2. Yearly total records for 21 solarimetric stations from SMN.
this purpose, data for each hour was integrated from the database for each station. The monthly averaged daily irradiation values so obtained were compared to satellite derived data for Mexico, based on the CSR model, which are reported on the SWERA web site maintained by NREL [15]. We think this is pertinent, as the mentioned database is widely available to the public at no cost, and therefore is representative of the type of information derived from satellite models, that exists for Mexico to date. The results presented in the SWERA database have been obtained by application of the CSR model [20] to data for the period 1985e1991, and have a 40 km spatial
N 1 X I N i¼1 GHd;i
(1)
The parameters evaluated for comparison were the yearly root mean square error (RMSE), and the yearly mean normalized bias error (MNBE), given by.
"
12 2 1 X RMSE ¼ IGH;m IGHCSR;m 12 m¼1
#1=2 (2)
and
"
# 12 IGHCSR;m IGH;m 1 X MNBE ¼ 100% 12 m¼1 IGH;m
Fig. 3. Daily number of records for three stations from SMN over a 3 year period.
(3)
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
25
Fig. 4. EMAS stations from SMN selected for comparison.
3. Results and discussion 3.1. Validation of data from SMN In the initial review of the data contained in the database from SMN, approximately 40 million records of solar radiation were found, from which 45,871 records were found with inconsistent date stamps (0.11%). Fig. 2 shows the results for 21 stations from the compiled tables. Stations shown are those that had a higher number of data for the time considered in the database, from 1999 to 2011. The graph shows an overall decline in the number of records from the period 2004e2007. A similar process was performed to obtain daily frequency tables in all stations during the first 3 years of operation, as shown in
Table 1. This table contains the 84 stations that showed more than 60% of days with complete records, 56 of them had more than 80% complete records. Fig. 3 shows some examples of typical stations with different percentages of records, such as Villa Ahumada, with 93.16%, Atlacomulco, with 83.32%, and Eco-Guardas, with 63.35%. Once the stations that recorded more than 80% of data had been identified, graphs of daily irradiance were made for a specific day of the week during the first 3 years. Some stations were discarded because they presented measurement errors or regular shading during certain parts of the year. A few stations with as low as 56% of records were taken into account because, despite of the gaps in the data, they were good according to the other criteria, and gave consistent averages. These was the case for Bahia Kino, SON (59%), Puerto Angel, OAX (62%), Presa el Cuchillo, NL (65%), Altamira, TAMPS (70%) and Jaumave, TAMPS (74%).
Table 2 EMAS stations from SMN selected for the present study. Name
Lat. N
Long W
Alt. (m.s.l)
Initial date
Final date
Name
Lat. N
Long W
Alt. (m.s.l)
Initial date
Final date
BAHIA DE LOS ANGELES MEXICALI SAN QUINTIN CD. CONSTITUCION SANTA ROSALIA CAMPECHE CD. DEL CARMEN MONCLOVA YOHALTUM CIUDAD CUAUHTEMOC CHINIPAS MAGUARICHI URIQUE CUATRO CIENEGAS VENUSTIANO CARRANZA TEZONTLE (DF) SAN JUAN DE GUADALUPE TLAPA DE COMONFORT
28.896 32.667 30.532 25.010 27.338 19.836 18.648 18.057 19.014 28.397 27.393 27.858 27.216 26.990 27.519 19.385 24.631 17.549
113.560 115.291 115.937 111.663 112.274 90.507 91.823 90.821 90.311 106.839 108.536 107.994 107.917 102.038 100.617 99.100 102.774 98.563
10 50 32 28 53 11 8 100 80 2100 431 1663 577 725 264 2358 1531 1060
04/05/2000 11/29/1999 04/01/2000 11/29/1999 11/29/1999 11/29/1999 11/29/1999 04/08/2008 04/08/2008 04/02/2008 05/04/1999 05/25/1999 04/28/1999 06/06/2008 06/05/2008 02/27/2000 07/10/2008 04/10/2008
04/06/2003 11/29/2002 04/02/2003 11/29/2002 11/29/2002 11/29/2002 11/29/2002 04/09/2011 04/09/2011 04/03/2011 05/04/2002 05/25/2002 04/28/2002 06/07/2011 06/06/2011 02/27/2003 07/11/2011 04/11/2011
PRESA ALLENDE PACHUCA JOCOTEPEC TIZAPAN ANGAMACUTIRO PRESA EL CUCHILLO PUERTO ANGEL
20.848 20.097 20.283 20.169 20.125 25.733 15.671 18.866 18.501 27.022 28.750 31.298 25.886 22.388 18.189 21.391 22.747 32.424
100.825 98.714 103.416 103.044 101.723 99.321 96.497 97.722 88.328 108.938 111.137 110.914 97.519 97.959 96.098 88.904 102.506 114.798
1915 2423 1506 1503 1730 134 91 2047 14 409 160 1275 4 3 107 2 2270 39
03/11/2000 03/04/2000 06/20/1999 07/19/1999 11/29/1999 11/29/1999 09/30/1999 05/29/1999 11/29/1999 05/11/1999 01/06/2004 03/19/2008 11/29/1999 05/20/1999 11/29/1999 04/07/2008 11/29/1999 03/19/2008
03/12/2003 03/05/2003 06/20/2002 07/19/2002 11/29/2002 11/29/2002 09/30/2002 05/29/2002 11/29/2002 05/11/2002 01/06/2007 03/20/2011 11/29/2002 05/20/2002 11/29/2002 04/08/2011 11/29/2002 03/19/2011
U. T. DE TECAMACHALCO CHETUMAL ALAMOS BAHIA DE KINO NOGALES MATAMOROS ALTAMIRA CD. ALEMAN DZILAM ZACATECAS SAN LUIS RIO COLORADO
26
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Fig. 5. Monthly average of daily irradiation, for selected stations, from measured data and CSR model. Irradiation units in kWh/m2.
Fig. 6. Locations for selected stations and their RMSE value by categories.
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Some stations presented staggered data on most of their records, so they were not taken into account for the comparison with CSR model. Of all the considered stations, 36 were found to show an adequate quality of their data, and were used for calculation of average daily irradiation as well as hourly averages. 3.2. Comparison with CSR model The 36 selected stations are shown in Fig. 4, and in Table 2 the analyzed periods of time are presented. It is necessary to point out that the period of time spanned by CSR differs significantly from the periods of the measured data. In general, the comparison shows a good agreement between the measured and model values, as can be observed from the representative examples presented in Fig. 5. The statistical comparison gives RMSE values which average 6.6% ± 2.0%. The highest n, VER, with 11.6%, being dispersion was observed for Ciudad Alema this the only case with a deviation above 10%. The lowest RMSE was for Mexicali, BC, with 3.0%. The MNBE (bias) oscillated from 7.3% to 8.8%. It is interesting to see that data suggest a trend to increasing RMSE as the latitude diminishes, as can be observed in Figs. 6 and 7. A similar trend is observed with the negative values of the MNBE. Both trends could be associated with a more complex weather at lower, rainier, latitudes, as compared to the mostly arid north
Fig. 7. (Upper) Percent RMSE for every station with respect to the CSR model as a function of latitude. (Lower) Percent RMSE with respect to the CSR model as a function of average annual rainfall.
27
latitudes of the country. In Table 3 the results are synthesized for all stations. From the selected stations, the yearly averaged daily irradiation on the country can be estimated as 5.5 ± 0.5 kWh/m2/day. This value more or less corresponds with different estimations that have been made in the past, and in particular gives a deviation of just 0.6% from the CSR model, considering the same locations. The mean difference in yearly average between the selected stations is of n, with 3.4 ± 2.3%. The largest difference corresponds to Cd. Alema n, with 0.3%. 7.8%, while the lowest corresponds to Cd. Constitucio It is interesting to note how the differences between the modeled and measured values are distributed in Mexico. It may be reasonable to try to explain the accuracy of the model according with the geographical conditions on the country. A classification of the stations according to their RMSE values has been done (Fig. 6), where three characteristics zones can be identified. In the first zone, corresponding to the northwest of Mexico (the states of Sonora and Baja California), the RMSE values are smaller than 5% (First two categories in Fig. 6). The second zone covers the mountainous areas of central and northern-central Mexico, where the RMSE values are in the range of 5e7% (Categories 3 and 4 in Fig. 6). Finally, the third zone corresponds to the Yucatan Peninsula, where values are in the range of 4e7%. The first area can be identified as part of a geomorphologically well defined climatic unit, which is shared with the southwest of the United States territory. Thus, the low error level found in this zone may be due to the fact that the CSR model has been tuned mainly with data for the United States. The second zone covers regions that are not similar to their counterparts in the USA, as most of Mexico is located in an intertropical region between two large bodies of water (Gulf of Mexico and Pacific Ocean). The climatic factors may affect the spatial and temporal distribution of solar radiation at the surface. As seen in Fig. 5 (middle row), for stations on this region, differences between daily irradiation surface values and model values can be either positive or negative. There are even stations with bimodal distributions, resulting from the wide variety of climates in the region. Finally, in the area located in the Yucatan peninsula, the diversity of RMSE values is remarkable and contrasts with the uniformity of the environment and climate, where the spatiotemporal distribution of solar radiation is mainly latitudinal. Two factors could be the reason for this phenomenon: first, the geology is mainly limestone, and second, the stations are in areas that vary from devastated vegetation, abundant vegetation, or sites dominated by pastures and farmland. This can affect the albedo and the atmospheric components distribution, causing an underestimation of the intensity of solar radiation by the model. This can be appreciated in the graphs for that region in Fig. 5 (bottom row) where, no matter the value of RMSE, surface measured values are always greater than predicted by the model. We think that the distribution of RMS errors in the map may be due to local climatic conditions of each region (altitude, rainfall, slop orientation, sea proximity). For instance, stations with low RMSE values are mainly in a physiographic unit in the northwest region of the country. Furthermore in the mountainous regions of the RMSE values of the stations are high. We also found higher values in stations close to the coast as is the case of Ciudad del Carmen and Puerto Angel. As an example of the influence of local physical conditions, we can see in Fig. 7, the graphs of annual average RMS values against latitude and total precipitation. In each graph we can't indentify a well defined behavior, but a trend with the respective parameters is observed. Another explanation may have to do with the variation of resolution of satellite images due the relative position of geostationary sensor to different regions in Mexico. However, if this was so the resolution should systematically decrease as we move away from the nadir of the satellite, in
28
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29
Table 3 Statistics for measured data and CRS model. Name (State)
GHI SMN kWh/m2
GHI CSR-NREL kWh/m2
Annual difference
MNBE (BIAS)
RMSE
Bahía De Los Angeles (BC) Mexicali (BC) San Quintín (BC) n (BCS) Cd. Constitucio Santa Rosalía (BCS) Campeche (CAMP) Cd. Del Carmen (CAMP) Monclova (CAMP) Yohaltum (CAMP) Ciudad Cuauhtemoc (CHI) Chinipas (CHI) Maguarichi (CHI) Urique (CHI) Cuatro Cienegas (COAH) Venustiano Carranza (COAH) Tezontle (DF) San Juan De Guadalupe (DGO) Tlapa De Comonfort (GRO) Presa Allende (GTO) Pachuca (HGO) Jocotepec (JAL) n (JAL) Tizapa Angamacutiro (MICH) Presa El Cuchillo (NL) Puerto Angel (OAX) U. T de Tecamachalco (PUE) Chetumal (QROO) Alamos (SON) Bahia De Kino (SON) Nogales (SON) San Luis Rio Colorado (SON) Matamoros (TAMPS) Altamira (TAMPS) Cd. Aleman (VER) Dzilam (YUC) Zacatecas (ZAC)
5.99 5.63 5.22 5.93 6.06 5.52 5.69 5.16 5.30 5.77 5.49 5.58 5.49 5.91 5.39 4.88 6.02 5.78 5.74 5.79 5.96 5.92 5.83 5.13 5.96 5.91 5.47 5.80 5.65 5.73 5.50 4.72 4.98 4.70 5.51 5.67
5.917 5.686 5.349 5.946 5.918 5.165 5.288 5.043 5.416 5.880 5.777 5.675 5.737 5.726 5.426 5.216 6.159 5.982 5.704 5.362 5.615 5.646 5.892 5.214 5.529 5.627 5.152 5.829 5.712 5.887 5.750 4.860 5.097 5.066 5.254 5.504
1.28% 1.07% 2.43% 0.29% 2.29% 6.36% 7.08% 2.31% 2.11% 1.87% 5.19% 1.71% 4.56% 3.10% 0.59% 6.93% 2.29% 3.51% 0.63% 7.41% 5.85% 4.68% 1.12% 1.64% 7.21% 4.78% 5.75% 0.50% 1.05% 2.79% 4.54% 2.90% 2.44% 7.79% 4.70% 2.91%
1.2% 1.3% 2.3% 0.2% 2.6% 6.3% 6.7% 2.0% 2.0% 1.4% 5.5% 0.9% 4.4% 3.9% 0.6% 7.2% 1.8% 3.1% 0.4% 7.3% 6.1% 4.9% 1.2% 3.2% 7.2% 4.7% 5.1% 0.3% 1.7% 2.6% 4.2% 4.5% 3.4% 8.80% 4.5% 2.9%
3.7% 3.0% 5.2% 3.4% 3.3% 7.5% 9.2% 6.1% 4.7% 8.1% 8.0% 6.7% 7.2% 5.4% 5.5% 9.0% 7.5% 6.4% 6.4% 8.3% 8.8% 6.8% 6.3% 6.7% 9.3% 6.4% 8.0% 6.6% 4.4% 7.0% 5.7% 7.2% 5.1% 11.6% 6.2% 8.0%
both axes (latitude and longitude). This is not evident in Fig. 6, as pixel resolution should decrease in the west direction, whereas RMS values show a better agreement between the modeled and measured values for this region. Note that obtaining more conclusive results requires a larger amount of reliable solarimetric data over the whole country. It is expected that such data will be available in the next few years, as a consequence of the pyranometers calibration program in progress.
4. Conclusions A preliminary analysis of the database from the EMAS stations of the SMN was carried out. Large gaps were identified in the database in the period 2004e2007. This problem is generalized in most of stations. From the analysis of daily number of records, for the first three years of operation of every station, those with higher than 80% complete data were identified. The number of stations that fulfilled this requirement was 56 (41%), of a total of 136. After examining the daily irradiance graphs for this group of stations, we determined that 36 of them (26%) appear to have valid data, free from systematic errors, and are therefore appropriate for comparison purposes. The daily totals and monthly averages were processed for those stations. From these stations, an average daily irradiation of 5.5 ± 0.5 kWh/m2/day is estimated for the country. The selected data was compared with results of the CSR model, obtained from the SWERA web site, for the same locations. Yearly RMSE values with respect to measured data average 6.6% ± 2.0%. The MNBE (bias) oscillated from 7.3% to 8.8%.
A conclusion of the error analysis is the need for reliable surface observations, to achieve tuning of the models to improve their performance. The different climate elements are crucial to the spatiotemporal distribution of solar radiation. These elements are critical in Mexico where short distances may represent considerable variations in altitude and vegetation. The above analysis is preliminary, and more conclusive results are expected in the future, as the whole network of radiometers is being calibrated and installation errors are being corrected. Acknowledgments noma de Me xico for Our thanks to Universidad Nacional Auto their financial support through grant PAPITT A100312-2. References pez S. “Radiacio n global en la república mexicana mediante [1] Almanza R, Lo n”. Series Instituto de Ingeniería, vol. 357. UNAM; 1975 [in datos de insolacio Spanish]. s M. Satellite derived solar irradiance over Mexico. [2] Galindo I, Castro S, Valde ATMOSFERA 1991;4(3):189e201. s M. Me xico, “Atlas de Radiacio n Solar”. Programa Uni[3] Galindo I, Valde xico: UNAM; 1992 [in Spanish]. versitario de Energía. Me n Solar Global en la Republica Mexicana: [4] Galindo I, Cifuentes G. “Radiacio xico: UNAM; Valores Horarios Medios”. Programa Universitario de Energía. Me 1996 [in Spanish]. [5] Estrada-Cajigal V, Almanza R. “Irradiaciones global directa y difusa, en su n directa normal en la perficies horizontales e inclinadas, así como irradiacio republica mexicana” Instituto de Ingeniería. UNAM SID-646; 2005 [in Spanish]. n de los recursos energe ticos renovables [6] Laboratorio Nacional para la evaluacio xico, IIE, Me xico. 2011. http://sag01.iie.org.mx/eolicosolar/Default.aspx. en Me
D. Riveros-Rosas et al. / Renewable Energy 75 (2015) 21e29 n M, Riveros-Rosas D, Arancibia-Bulnes CA, Bonifaz R. “The solar [7] Valdes-Barro resource assessment in Mexico: State of Art” ISES Solar World Congress 2013, xico, Nov 4e7. 2013. Cancun, Me [8] Gueymard Christian A, Myers Daryl R. Evaluation of conventional and highperformance routine solar radiation measurements for improved solar resource, climatological trends, and radiative modeling. Sol Energy 2009;83: 171e85. [9] Gueymard Christian A, Wilcox Stephen M. Assessment of spatial and temporal variability in the US solar resource from radiometric measurements and predictions from models using ground-based or satellite data. Sol Energy 2011;85:1068e84. [10] Nottrott Anders, Kleissl Jan. Validation of the NSRDBeSUNY global horizontal irradiance in California. Sol Energy 2010;84:1816e27. ~ igo, Gasto n Martín, Ferna ndez-Peruchena Carlos, Moreno Sara, [11] Pagola In Ramírez Lourdes. New methodology of solar radiation evaluation using free access databases in specific locations. Renew Energy 2010;35:2792e8. David, Zelenka Antoine. [12] Perez Richard, Moore Kathleen, Wilcox Steve, Renne Forecasting solar radiation e preliminary evaluation of an approach based upon the national forecast database. Sol Energy 2007;81:809e12. pez G. Solar resources estimation [13] Bosch JL, Batlles FJ, Zarzalejo LF, Lo combining digital terrain models and satellite images techniques. Renew Energy 2010;35:2853e61.
29
[14] Finsterle W. “WMO International Pyrheliometer comparison IPC-XI 27 September e 15 October 2010 Davos, Switzerland Final Report” WMO IOM. Report No. 108. 2011. [15] Solar and wind energy resource assessment. USA: NREL; 2006. http://maps. nrel.gov/swera. [16] Valdes M, Riveros-Rosas D, Bonifaz R. “Solarimetric Network for Solar Radiation Assessment in Mexico” SolarPACES2012, Marrakech, Morocco, Sep. 11e14. 2012. [17] Polo J, Zarzalejo LF, Ramirez L, Espinar B. Iterative filtering of ground data for qualifying statical models for solar irradiance estimation from satellite data. Sol Energy 2006;80:240e7. L, Me nard L, Wald L. A web service for controlling the [18] Geiger M, Diabate quality of measurements of global solar irradiation. Sol Energy 2002;73(6): 475e80. n Solar, Instituto de Geo[19] Energía solar disponible, Observatorio de Radiacio física. http://www.geofisica.unam.mx/ors/energiaSolarprueb1.php [20] Maxwell EL, George RL, Wilcox SM. A climatological solar radiation model. In: Proceedings of the 1998 American Solar Energy Society Annual Conference. Albuquerque, NM June 14e17; 1998. [21] Maxwell EL. METSTAT-the solar radiation model used in the Production of the National Solar Radiation Data Base (NSRDB). Sol Energy 1998;62(4):263e79.