Impact of Optimization Algorithm Choice on Nonlinear Global Model for Photovoltaic Energy Generation Forecasting

Abstract

The article explores the relevance of choosing the optimization algorithm to obtain accurate parameter estimates in photovoltaic (PV) systems, with the aim of improving the energy efficiency of solar energy. Advances in photovoltaic module analysis models have resulted in the development of global non-linear models (GNLM), which offer a more accurate representation of the I-V characteristics under various environmental conditions. Metaheuristic algorithms have stood out for their ability to handle the complexity of these nonlinear models. Therefore, the careful choice of the optimization algorithm is fundamental to guarantee consistent and reliable results in the estimation of the model parameters, contributing to maximizing energy efficiency. The study seeks to investigate whether different optimization tools can improve the accuracy and efficiency of parameter estimation, resulting in improved modeling and performance prediction of PV systems in different conditions.

Keywords

• energy production
• gnlm
• nmaep
• optimization algorithms
• photovoltaic