Mapping solar irradiation from ground- and satellite-based data over Thailand using a simple semi-empirical model
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Abstract
An approach for mapping solar irradiation from ground and satellite-based data for Thailand was developed. A simple semi-empirical model relating normalized monthly average global solar irradiation to a normalized monthly average of visibility, precipitable water, total ozone column, and cloud index was proposed. Visibility data collected at 80 Thai meteorological stations were employed to represent the influence of aerosols on solar irradiation. Precipitable water was derived from air temperature and relative humidity measured at these stations. An interpolation technique was used to fill out the gaps between stations. Ozone data were acquired from OMI/AURA satellite data. A cloud index, which is used to represent the influence of clouds on irradiation, was calculated from MTSAT-1R satellite data. The model was based on global irradiation measured at four solar radiation monitoring stations located in the main regions of Thailand. The model validation using independent data at 36 stations revealed that the monthly average global solar irradiation computed from the model and that obtained from the measurements were in good agreement, with the root mean square difference being 6.9%. After its validation, the model was used to create solar irradiation maps. These maps are similar to those obtained from sophisticated irradiation models.
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References
Bosch, J. L., Batlles, F. J., Zarzalejo, L. F., and López, G. (2010). Solar resources estimation combining digital terrain models and satellite images techniques. Renewable Energy, 35(12), 2853-2861.
Cano, D., Monget, J. M., Albuisson, M., Guillard, H., Regas, N., and Wald, L. (1986). A method for the determination of the global solar radiation from meteorological satellite data. Solar Energy, 37(1), 31-39.
Charuchittipan, D., Janjai, S., Pratummasoot, N., Buntoung, S., and Peengam, S. (2018). Mapping of cloud cover from satellite data over Thailand. Science, Engineering and Health Studies, 12(2), 69-76.
Department of Alternative Energy Development and Efficiency. (2007). Assessment of Solar Energy Potentials for Lao People’s Democratic Republic. Bangkok: Department of Alternative Energy Development and Efficiency, pp. 48-50.
Department of Alternative Energy Development and Efficiency. (2009). Assessment of Solar Energy Potentials for Myanmar. Bangkok: Department of Alternative Energy Development and Efficiency, pp. 71-72.
Dhakal, C. P. (2019). Interpreting the basic outputs (SPSS) of multiple linear regression. International Journal of Science and Research, 8(6), 1448-1452.
Duffie, J. A., and Beckmann, W. A. (2013). Solar Engineering of Thermal Process, 4th, New Jersey: John Wiley & Sons Inc., pp. 138-173.
Exell, R. H. B. (2007). Mapping solar radiation by meteorological satellite. Renewable Energy Review Journal, 6(1), 27-39.
Huang, G., Li, Z., Li, X., Liang, S., Yang, K., Wang, D., and Zhang, Y. (2019). Estimating surface solar irradiance from satellites: past, present, and future perspectives. Remote Sensing of Environment, 233, 111371.
Iqbal, M. (1983). An Introduction to Solar Radiation, Cambridge, MA: Academic Press, pp. 128-133.
Janjai, S., Laksanaboonsong, J., Nunez, M., and Thongsathitya, A. (2005). Development of a method for generating operational solar radiation maps from satellite data for a tropical environment. Solar Energy, 78(6), 739-751.
Janjai, S., Masiri, I., and Laksanaboonsong, J. (2013a). Satellite-derived solar resource maps for Myanmar. Renewable Energy, 53, 132-140.
Janjai, S., Masiri, I., Pattarapanitchai, S., and Laksanaboonsong, J. (2013b). Mapping global solar radiation from long-term satellite data in the tropics using an improved model. International Journal of Photoenergy, 2013, 210159.
Kämpfer, N. (2013). Introduction. In Monitoring Atmospheric Water Vapour: Ground-Based Remote Sensing and In-situ Methods (ISSI Scientific Report Series, 10) (Kämpfer, N., ed.), pp. 1-7. New York, NY: Springer.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2012). Introduction to Linear Regression Analysis, 5th, New Jersey: John Wiley & Sons, Inc., pp. 12-58.
Möser, W., and Raschke, E. (1984). Incident solar radiation over Europe estimated from METEOSAT data. Journal of Climate and Applied Meteorology, 23(1), 166-170.
Pinker, R. T., and Laszlo, I. (1992). Modeling surface solar irradiance for satellite applications on a global scale. Journal of Applied Meteorology and Climatology, 31(2), 194-211.
Polo, J., Zarzalejo, L. F., Cony, M., Navarro, A. A., Marchante, R., Martin, L., and Romero, M. (2011). Solar radiation estimations over India using Meteosat satellite images. Solar Energy, 85(9), 2395-2406.
Polo, J. (2015). Solar global horizontal and direct normal irradiation maps in Spain derived from geostationary satellites. Journal of Atmospheric and Solar-Terrestrial Physics, 130-131, 81-88.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1992). Numerical Recipes in Fortran 77, 2nd, Cambridge: Cambridge University Press, p. 994.
Salby, M. L. (1996). Fundamentals of Atmospheric Physics. New York: Academic Press, pp. 1-648.
Sorapipatana, C., Exell, R. H., and Borel, D. (1988). A bispectral method for determining global solar radiation from meteorological satellite data. Solar & Wind Technology, 5(3), 321-327.
Suwantragul, B., Watrabutr, W., Sitathani, K., Tia, V., and Namprakai, P. (1984). Solar and Wind Energy Potential Assessment of Thailand, Bangkok: King Mongkut’s Institute of Technology Thonburi Campus, pp. 423-433.
Wyser, K., O'Hirok, W., Gautier, C., and Jones, C. (2002). Remote sensing of surface solar irradiance with corrections for 3-D cloud effects. Remote Sensing of Environment, 80(2), 272-284.