ESTIMATION OF SOLAR RADIATION AND ROOFTOP SOLAR POWER POTENTIAL IN HIMACHAL PRADESH
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Date
2024-03-24
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UHF Nauni
Abstract
The present investigation entitled “Estimation of Solar Radiation and Rooftop Solar Power Potential in Himachal
Pradesh” was conducted during 2020-2023 in the Department of Environmental Science, College of Forestry, Dr. Y.S
Parmar University of Horticulture and Forestry, Nauni, Solan, H.P.The study focuses on the estimation of solar radiation in
hilly areas, specifically Solan and Palampur, using various empirical models based on sunshine hours and temperature.
Emphasizing sunshine hours as a critical parameter, a comprehensive statistical analysis involving 82 global models was
conducted to determine the most suitable model for these regions, culminating in the development of a new linear regression
model. The model demonstrated exceptional precision, evidenced by significant coefficient of determination (R²) values of
0.97 and 0.99 for Solan and Palampur, respectively. Nash-Sutcliffe efficiency (NSE) values of 0.90 and 0.95, along with
correlation coefficient (r) values of 0.99. Furthermore, an analysis of 30 existing temperature-based empirical models was
also undertaken for Solan and Palampur, resulting in the development of a novel model with high precision, as evidenced by
coefficient of determination values of 0.93 and 0.85 for Solan and Palampur.The accuracy of both the developed models was
further demonstrated by commendably low values for mean squared error (MSE), root mean square error (RMSE), mean
absolute percentage error (MAPE), mean bias error (MBE), and standard deviation. Both the models emerged as the best-fit
solution for accurately estimating global solar radiation in Himalayan region.The new model based on sunshine hours was
then used to estimate GSR for the Kullu while new model based on temperature was used to estimate GSRin eight other hilly
locations where temperature data is available, but sunshine hour and global solar radiation data are unavailable, including
Kullu, Bhuntar, Shimla, Dharamshala, Sundernagar, Kalpa, Una, and Nahan. The study also employed Artificial Neural
Networks (ANN) for the estimation of solar radiation, considering input parameters such as latitude, longitude, altitude,
extraterrestrial solar radiation, sunshine hours, and maximum and minimum temperature. Two models were developed, each
with one hidden layer. The first model, based on sunshine hours, utilized five neurons in the hidden layer, resulting in an
impressive coefficient of determination (R²) of 0.99. The second model, focusing on temperature, incorporated ten neurons
in the hidden layer and achieved an R² of 0.93. Moreover, the study conducts a comprehensive analysis of rooftop solar
power potential in Solan City, focusing on 6,102 manually digitized rooftops with a cumulative area of 2,820,000 square
meters. Emphasis on south-facing areas, totaling 1,210,000 square meter, highlights significant solar exposure optimization.
The calculated annual rooftop solar power potential was an impressive 1,35,520 kilowatts, indicating substantial energy
contribution. Additionally, the research examined the influence of weather parameters on solar radiation in Solan and
Palampur, and mapped solar energy potential across Himachal Pradesh, revealing a seasonal variation with peak potential in
May and a low in December. Overall, this study provides a holistic assessment of solar resources, rooftop solar potential, and
the impact of weather parameters in Himachal Pradesh, contributing valuable insights for sustainable energy planning in
hilly regions.