Machine Learning Boosts Solar Forecasting Accuracy in Saudi Arabia

Managing electrical grids with high renewable penetration requires accurate forecasting of solar generation. A new study from Saudi Arabia's Jazan region demonstrates how machine learning can significantly improve photovoltaic power predictions, particularly in challenging tropical desert climates. Researchers trained and compared five algorithms—Decision Tree Regression, Multiple Linear Regression, Random Forest, k-Nearest Neighbors, and Extreme Gradient Boosting (XGBoost)—using six years of hourly meteorological and generation data collected between 2017 and 2022. Jazan's coastal location creates unique conditions rarely studied in Saudi Arabia's renewable energy context: high humidity, seasonal wind variations, and maritime influences that differ markedly from inland desert sites typically analyzed. These conditions make the region an excellent testbed for evaluating model robustness across diverse weather patterns. XGBoost emerged as the clear winner, achieving a coefficient of determination of 0.93, mean absolute error of 7.3 watts per square meter, and root mean square error of 20.38 watts per square meter. The ensemble method substantially outperformed traditional linear and simpler tree-based approaches. The superior performance of XGBoost reflects ensemble learning's inherent strength: combining multiple weak predictors reduces overfitting and improves generalization across varying meteorological conditions. For power system operators, this level of accuracy translates to better day-ahead scheduling, reduced reserve margins, and more efficient dispatch decisions. The findings are particularly relevant as Saudi Arabia accelerates its renewable energy transition under Vision 2030 initiatives. Grid operators can adopt gradient boosting techniques to improve forecast reliability across diverse climatic zones, enabling higher solar penetration without compromising grid stability. Future work should extend these models to additional regions and explore hybrid forecasting approaches combining machine learning with physical solar models for enhanced robustness.