Advanced Solar Power Forecasting: A Hybrid/Ensemble Approach Utilizing Geographic and Meteorological Data

Photovoltaic (PV) systems are pivotal in the global energy transition, where accurate solar power forecasting is critical. Traditional forecasting has leaned heavily on solar irradiance data, yet such reliance carries inherent uncertainties and measurement complexities, presenting significant forecasting challenges. This paper introduces a novel hybrid/ensemble model that reduces dependence on solar irradiance data, utilizing geographic, meteorological, and temporal data to predict solar power output. Combining the strengths of XGBoost and LightGBM algorithms through a linear regression meta-model, our approach demonstrates improved prediction accuracy, evidenced by a mean absolute error (MAE) of 0.033, and an R-squared value of 0.693. This study advances solar power forecasting, enhancing PV system efficiency, and reliability, and promoting sustainable energy investments.