Chameleon Swarm Algorithm Applied to Extract the Parameters of Photovoltaic Cells and Panels

Forecasting the energy output of photovoltaic modules is an important consideration for prosumers and investors. The forecast accuracy depends on the precise determination of key external and internal parameters. This study proposes applying the metaheuristic Chameleon Swarm Algorithm (CSA) to extract the parameters for two photovoltaic cells, RTC France and aSi:H amorphous silicon, and one photovoltaic module, PWP 201. The CSA is applied to the most widely used models, which derive from the equivalent circuits for the photovoltaic cells and modules, namely with one, two, and three diodes. Six statistical metrics are used to verify and validate the performance of the CSA. The study also reports the following metrics: mean, standard deviation, minimum, maximum and interquartile range to evaluate the reliability and robustness of the algorithm. It is compared with several algorithms from 2023 to 2025 and some very good algorithms from outside of this interval. Computing time is also analyzed depending on the photovoltaic cells and module, the three models, and the number of iterations. The results for the main statistical metrics, root mean square error (RMSE), and computing time prove that the CSA is one of the most effective and reliable. The obtained RMSE is the best in some cases or equal, and the computing time is very low, thus certifying that the CSA is competitive against, and in several cases improves upon, previously reported best-known algorithms' results.