Prediction of Electricity Production from Wind and Solar Energy by Employing Regression Models

Abstract

Accurately predicting electricity production from renewable energy sources is critical for sustainable energy systems. Wind and solar energy play key roles in reducing greenhouse gas emissions and reliance on fossil fuels. However, forecasting their production is challenging due to weather variability and seasonal changes. This study employs advanced regression models to predict energy production, including Linear Regression, Gradient Boosting, AdaBoost, XGBoost, and Random Forest. The dataset comprises hourly wind and solar energy data from France in 2020, containing 59,807 samples. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 metrics. Random Forest demonstrated the best performance, with an MAE of 219.30 MW, RMSE of 391.11 MW, and R2 of 0.97. XGBoost also showed strong results, achieving an MAE of 729.43 MW, RMSE of 1200.45 MW, and R2 of 0.75. These results highlight the Random Forest model's superior accuracy and reliability in capturing complex patterns. This work contributes to improving the integration of renewable energy into power grids by enhancing forecasting precision.