PREDICTION OF SOLAR ENERGY GENERATION

Author(s)	Mr. Hemant Kumar Chaudhary, Dr. Sunil Kumar Patel, Mr. Rohtash Singh
Country	India
Abstract	The increasing global demand for renewable energy sources has made the prediction of solar energy generation critical. In this project, we propose a machine learning-based approach to forecast solar power output using environmental parameters such as ambient temperature, module temperature, and solar irradiation. The datasets from two plants were merged, cleaned, and preprocessed. Linear Regression was employed as the machine learning model to predict the AC power output. The model achieved a reasonable R² score, indicating good performance. Separate plots for each plant and combined analysis were created to visualize the relationship between weather conditions and power generation. The results demonstrate that simple machine learning models can effectively predict solar energy generation, enabling better energy management.
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	2026-01-25
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.67371
Short DOI	https://doi.org/hbmdr7

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