An Enhanced Hybrid TLBO–ANN Framework for Accurate Photovoltaic Power Prediction Under Varying Environmental Conditions

Abstract

This study presents an enhanced hybrid TLBO–ANN model for daily photovoltaic (PV) power generation prediction. By combining the strong nonlinear modeling capacity of Artificial Neural Networks (ANN) with the robust optimization capability of the Teaching–Learning-Based Optimization (TLBO) algorithm, the proposed framework effectively improves prediction accuracy and generalization performance. The model was trained using real meteorological and power generation data and validated on a grid-connected PV power plant in Türkiye. Results indicate that the hybrid TLBO–ANN approach outperforms the conventional ANN by achieving 39.97% and 37.46% improvements on the test subset and overall dataset, respectively. The improved convergence behavior and avoidance of local minima by TLBO contribute to this enhanced accuracy. Overall, the proposed hybrid model provides a powerful and practical tool for reliable PV power forecasting, which can facilitate better grid integration, operational planning, and energy management in renewable energy systems.