Intelligent Optimization and Power Forecasting for Photovoltaic‐Hydrogen Hybrid Energy Systems

Photovoltaic (PV) systems are increasingly integrated into modern energy infrastructures; however, their intermittent nature poses significant challenges for maintaining a reliable and efficient power supply. This study investigates a hybrid PV/battery/hydrogen energy system designed to enhance energy reliability and optimize system performance. The proposed approach combines machine learning techniques, including artificial neural networks (ANN), random forest (RF), and particle swarm optimization‐based backpropagation (BP‐PSO), to improve power predictionaccuracy and support optimal system operation. In addition, an energy management strategy is developed to coordinate the interaction between PV generation, battery storage, and hydrogen storage in order to ensure a continuous load supply while maximizing renewable energy utilization. The techno‐economic optimization framework is used to determine the optimal sizing of system components, minimizing performance losses and improving operational efficiency. Simulation results demonstrate that the proposed predictive and optimization framework significantly enhances the reliability and performance of the hybrid energy system. These findings highlight the potential of combining advanced machine learning methods with energy management strategies to support sustainable and efficient hybrid renewable energy systems.