In this work, genetic algorithms (GA) and particle swarm optimization (PSO) are used to make an automated choice of hyperparameters of the MLP-NARX, ELM-NARX, and ESNNARX neural models applied to the identification of two photovoltaic systems: one installed in Teresina, in Brazil, and another in Hamburg, Germany. The automatic optimization process results showed that the PSO algorithm presents superior performance compared to the GA algorithm. Likewise, the identification carried out aimed to estimate the power generated by photovoltaic systems from two different approaches: linear mathematical models and neural identification models. Thus, the neural models implemented are more efficient and accurate than the linear mathematical models compared. From accuracy, the neural models ESNNARX and MLP-NARX were considered the best in identifying Hamburg and Teresina's photovoltaic systems, respectively.