Advanced Machine Learning Models for Prediction, and Performance Optimization in Renewable Energy

The research by Mohammed Alghassab, conducted at Shaqra University, significantly advances renewable energy systems through the application of advanced machine learning (ML) models—Random Forest, Support Vector Regressor, Gradient Boosting, and CatBoost. Key contributions include achieving high prediction accuracy (Gradient Boosting: 94.2% classification accuracy, 1.86 MW RMSE) and perfect scalability prediction (CatBoost: 1.0 accuracy) for solar, wind, hydro, and geothermal systems. These models enhance energy output forecasting, resource classification, and performance optimization by leveraging feature engineering and hyperparameter tuning. The study demonstrates a 12% accuracy improvement and 10% error reduction over baselines, supporting grid stability, cost-efficiency, and CO2 reduction. By addressing intermittency, scalability, and dependability challenges, the research aligns with global sustainability goals, fostering innovation in smart grids and policy-driven energy planning for a low-carbon future.