SOLAR RADIATION PREDICTION USING ML AND PYTHON

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.