Solar Radiation Prediction Using Ml and Python
Year: 2025
Type: article
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.
Authors: U.VENKATA TEJA -, M.SAI KIRAN -, V.KARTHIKEYA -, E.MURALI -, T.KUMANAN -
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408883960