Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO

Luis O. Lara-Cerecedo, Jesús F. Hinojosa (), Nun Pitalúa-Díaz, Yasuhiro Matsumoto and Alvaro González-Angeles
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Luis O. Lara-Cerecedo: Departamento de Ingeniería Química y Metalurgia, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo 83000, Mexico
Jesús F. Hinojosa: Departamento de Ingeniería Química y Metalurgia, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo 83000, Mexico
Nun Pitalúa-Díaz: Departamento de Ingeniería Industrial, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro, Hermosillo 83000, Mexico
Yasuhiro Matsumoto: Departamento de Ingeniería Eléctrica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, San Pedro Zacatenco, Ciudad de México 07360, Mexico
Alvaro González-Angeles: Facultad de Ingeniería, Universidad Autónoma de Baja California, Blvd. Benito Juárez S/N, Mexicali 21280, Mexico

Energies, 2023, vol. 16, issue 16, 1-26

Abstract: The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab ® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.

Keywords: photovoltaic systems; intelligent models; ANFIS; particle swarm algorithm; ambiental variables; electrical energy prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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