Rapid, Precise Parameter Optimization and Performance Prediction for Multi-Diode Photovoltaic Model Using Puma Optimizer

Photovoltaic (PV) technology is essential for achieving net-zero emissions by 2050. PV system efficiency is highly sensitive to irradiance, temperature, and shading. However, accurate parameter identification is critical for modeling, as PV models often exhibit multi-modal and strongly coupled characteristics. In addition, commercial datasheets typically lack sufficient parameter information, making precise parameter extraction difficult and limiting the accuracy of maximum power point predictions. To address these challenges, this research employs a novel metaheuristic algorithm called Puma Optimizer (PO) to optimize the parameters of multiple PV models. The PO’s performance is benchmarked against four advanced metaheuristic algorithms using convergence curves, error bars, and boxplots to evaluate its robustness. Results show that PO demonstrates strong adaptability and reliable performance in PV parameter optimization. Lastly, the research analyzes parameter sensitivity to help reduce computational resource usage. Visual analysis confirms that the PO parameter optimization approach provides an effective and practical solution for enhanced energy management and stable grid integration as solar adoption continues to expand.