Optimization of NARX Neural Models Using Particle Swarm Optimization and Genetic Algorithms Applied to Identification of Photovoltaic Systems

In this study, genetic algorithms (GAs) and particle swarm optimization (PSO) are used to make an automated choice of hyperparameters of the multilayer perceptron (MLP)-NARX, extreme learning machine (ELM)-NARX, and echo state network (ESN)-NARX 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 ESN-NARX and MLP-NARX were considered the best in identifying Hamburg and Teresina's photovoltaic systems, respectively.

Automated summary

In this study, genetic algorithms and particle swarm optimization were used to optimize the hyperparameters of neural models for identifying two photovoltaic systems. The results showed that PSO algorithm outperformed GA algorithm, and the neural models were more efficient and accurate than linear mathematical models. ESN-NARX and MLP-NARX neural models were considered the best in identifying the photovoltaic systems in Hamburg and Teresina, respectively.