Prediction of Solar Energy Generation using Machine Learning

Author(s)	Raj Kumar Parida, Nakshatra Verma, Nirnay Singh
Country	India
Abstract	Solar forecasting plays an important role in the successful integration of renewable energy onto the grid. Environmental nature of solar energy poses a challenge to power planning and management. Machine learning method is, here, proposed to predict solar power generation from environmental factors such as ambient temperature, module temperature, and solar irradiation. Two solar power plants' data were merged [1], processed, and used to train the prediction model based on Linear Regression. The model provided a satisfactory R² value, a performance measure in representing the relationship between weather conditions and power generation. The results validate the capability of machine learning methods to enhance the reliability of solar forecasting and enhance grid stability and power planning.
Keywords	Solar Energy Forecasting, Machine Learning, Linear Regression, Renewable Energy, Solar Irradiation, AC Power Prediction
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-15
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.66777

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