Accurate forecasting of photovoltaic optimal points and efficiency using advanced hybrid machine learning models

Abstract

Abstract Accurate forecasting of photovoltaic performance is essential for improving solar energy management, optimizing operational schedules, and supporting investment decisions. This study proposes a structured data-driven forecasting framework that integrates standalone learners with a hybrid boosting–aggregation strategy to predict two critical photovoltaic performance indicators: the optimal peak operating time (NOPT) and the power conversion efficiency (PCE). The methodology involves systematic data preprocessing, feature normalization, model training using both single and hybrid learners, and performance validation under identical experimental conditions. Multiple data-driven algorithms were examined using comprehensive statistical metrics, including R², RMSE, and U95. Among all models, the hybrid XGBA framework demonstrated superior predictive performance, achieving R 2 values of 0.9954 for NOPT and 0.9970 for PCE, and consistently low errors across all evaluation criteria. Model robustness and generalization were further assessed through uncertainty-based evaluation metrics. Sensitivity analyses highlight key influential parameters such as Emin Emax , and Ap , revealing their substantial contributions to model outputs. The proposed hybrid model provides a robust and highly accurate predictive tool that can reduce operational uncertainties, enhance energy yield, and support data-driven decision-making for photovoltaic plant operators and energy sector stakeholders.