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Open AccessArticle

Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

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, Penteli, 15236 Athens, Greece
3
Electrical and Electronics Engineering Department, National Institute of Technology Surathkal, Karnataka 575025, India
4
CleanMax Solar Energy Solutions Ltd., Karnataka 560082, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1921; https://doi.org/10.3390/rs12121921
Received: 24 April 2020 / Revised: 7 June 2020 / Accepted: 11 June 2020 / Published: 14 June 2020
(This article belongs to the Special Issue Remote Sensing for Smart Renewable Cities)
This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations. View Full-Text
Keywords: solar radiation estimation; PV energy production; clouds and aerosols impact; financial losses; rooftop photovoltaic; azimuthal shadows solar radiation estimation; PV energy production; clouds and aerosols impact; financial losses; rooftop photovoltaic; azimuthal shadows
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MDPI and ACS Style

Masoom, A.; Kosmopoulos, P.; Kashyap, Y.; Kumar, S.; Bansal, A. Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques. Remote Sens. 2020, 12, 1921.

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