Hybrid Stacking with Targeted Residual Learning for PV Forecasting

Abstract

Photovoltaic (PV) power forecasting is essential for the reliable integration of solar energy into modern power systems. Although recent machine learning and ensemble-learning models achieve high forecasting accuracy, their performance often deteriorates under variable weather conditions, leading to large prediction errors and reduced reliability. This limitation highlights the need for forecasting frameworks that explicitly address error heterogeneity rather than focusing solely on global performance metrics.This study proposes a hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy. The stacking architecture integrates three complementary ensemble models, namely Random Forest, Extra Trees, and XGBoost, whose outputs are combined through a Ridge regression meta-learner. To improve forecasting robustness, a residual-learning mechanism is introduced to identify difficult observations based on prediction errors and irradiance conditions. A dedicated correction model is subsequently applied to these hard-to-predict samples.The proposed framework is evaluated using real-world PV generation and meteorological data collected from a desert-climate photovoltaic installation. Experimental results show that ensemble-based models achieve excellent overall forecasting accuracy, with Extra Trees providing the best global performance. Furthermore, weather-regime and sensitivity analyses reveal that forecasting errors are strongly influenced by irradiance variability and atmospheric conditions. The findings demonstrate that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photovoltaic forecasting systems.