• Paras Thakkar
• Nidhi Sinha
• Rutuja Kulkarni
• Bahubali Shiragapur

The study proposes a deep learning approach to predict solar radiation by utilizing data from meteorological measurement stations and satellite imagery. Accurately estimating solar radiation is crucial for successful integration of solar photovoltaics into existing electrical grid systems. Solar energy is widely favored as a renewable energy source. The study assessed the performance of deep learning-based predictive models and regression models across different climate conditions and provided recommendations for further research. The model achieved highly accurate half hourly insolation forecasts worldwide, but encountered errors during sunrise and sunset periods. Incorporating elements associated with sunrise and sunset could have potentially prevented these mistakes.

Solar Energy, INSAT3D, RNN, LSTM, Deep Learning

"DEEP LEARNING BASED MODEL FOR PREDICTION OF INCOMING SOLAR RADIATION USING SATELLITE DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p285-p289, May-2023, Available :http://www.jetir.org/papers/JETIR2305G38.pdf

"DEEP LEARNING BASED MODEL FOR PREDICTION OF INCOMING SOLAR RADIATION USING SATELLITE DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp285-p289, May-2023, Available at : http://www.jetir.org/papers/JETIR2305G38.pdf