Solar Radiation Prediction Based on Hybrid Machine Learning Technique

Abstract

Solar radiation forecasting is crucial for optimizing solar power generation systems and integrating solar energy into the energy grid. Traditional physical models have limitations in accuracy due to the complexity of atmospheric processes. This research presents a novel hybrid machine learning approach that combines Random Forest and XGBoost algorithms for solar radiation prediction. The proposed hybrid model leverages the strengths of both the algorithms to enhance prediction accuracy. Simulation results demonstrate the superior performance of the hybrid model compared to conventional models, offering a promising solution for efficient solar energy utilization and a greener future.