A Novel Two-Stage Residual-Corrected Stacking Framework for Photovoltaic Power Output Prediction
Proper prediction of photovoltaic (PV) power output is essential in ensuring the successful incorporation of solar energy in the smart grid systems and energy management systems. Current single-algorithm models are often ineffective to represent the compound non-linear interactions between meteorological variables, time variations and irradiance dynamics that cause solar generation variability. This paper presents the Solar-Adaptive Hybrid Ensemble (SAHE) which is a new two-stage stacking model that uses a new Random Forest (RF) base learner with an XG Boost residual-correction meta-learner, supplemented by solar-domain feature engineering such as clearness index, irradiance polynomial transforms, lagged target variables and cyclic temporal encodings. The SAHE framework has a Root Mean Squared Error (RMSE) equal to 23.8850 kWh, Mean Absolute Error (MAE) equal to 18.6632 kWh, coefficient of determination (R 2) equal to 0.8079, Mean Absolute Percentage Error (MAPE) equal to 8.7608% and Pearson Correlation Coefficient (PCC) equal to 0.9113 on the test set, compared to all comparison models, in all reported measures.