SOLAR RADIATION PREDICTION USING ML AND PYTHON

Author(s)	U.VENKATA TEJA, M.SAI KIRAN, V.KARTHIKEYA, E.MURALI, T.KUMANAN
Country	India
Abstract	Accurate solar radiation prediction is essential for optimizing solar energy systems. Traditional methods often lack precision, especially in dynamic weather conditions. This study proposes a machine learning-based approach using Random Forest Regressor and XGBoost Regressor, leveraging meteorological variables such as temperature, humidity, wind speed, cloud cover, and time-based factors. The dataset undergoes preprocessing and feature selection before training. The models are evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) scores. Results demonstrate the effectiveness of these models in predicting solar radiation, providing valuable insights for solar energy management.
Keywords	Keywords: Solar Radiation, Machine Learning, Python, Renewable Energy, Prediction Model, XGBoost, Random Forest
Field	Computer Applications
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-03-22
Cite This	SOLAR RADIATION PREDICTION USING ML AND PYTHON - U.VENKATA TEJA, M.SAI KIRAN, V.KARTHIKEYA, E.MURALI, T.KUMANAN - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2713
DOI	https://doi.org/10.71097/IJSAT.v16.i1.2713
Short DOI	https://doi.org/g892d2

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