Optimization of NARX Neural Models Using Particle Swarm Optimization and Genetic Algorithms Applied to Identification of Photovoltaic Systems
| Autor: | José Medeiros de Araújo Junior, José Maria Pires de Menezes Júnior, Ronnyel Carlos Cunha Silva |
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| Rok vydání: | 2021 |
| Předmět: |
Nonlinear autoregressive exogenous model
Artificial neural network Optimization algorithm Renewable Energy Sustainability and the Environment Computer science 020209 energy 010401 analytical chemistry Photovoltaic system Energy Engineering and Power Technology Particle swarm optimization 02 engineering and technology computer.software_genre 01 natural sciences 0104 chemical sciences Identification (information) 0202 electrical engineering electronic engineering information engineering Data mining computer |
| Zdroj: | Journal of Solar Energy Engineering. 143 |
| ISSN: | 1528-8986 0199-6231 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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