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Open AccessArticle

Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas

1
Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247667, India
2
Institute for Environmental Research and Sustainable Development, National Observatory of Athens (IERSD/NOA), 15236 Athens, Greece
3
Physikalisch Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC), CH-7260 Davos, Switzerland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 254; https://doi.org/10.3390/rs12020254
Received: 10 December 2019 / Revised: 26 December 2019 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Renewable Energy Mapping)
Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions. View Full-Text
Keywords: radiative transfer model; cloud optical thickness; aerosol optical depth; solar energy radiative transfer model; cloud optical thickness; aerosol optical depth; solar energy
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MDPI and ACS Style

Masoom, A.; Kosmopoulos, P.; Bansal, A.; Kazadzis, S. Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas. Remote Sens. 2020, 12, 254.

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