Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA

Translated title of the contribution: Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA

L. O. Lara-Cerecedo, N. Pitalúa-Díaz*, J. F. Hinojosa-Palafox

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

To predict the electrical energy generation behavior in a photovoltaic system, we developed an adaptive neuro-fuzzy inference system (ANFIS) model which integrates an optimization through a genetic algorithm (GA). The evolutionary ANFIS-GA uses a geographical area’s solar radiation and ambient temperature. This model uses the capacity to classify and identify data patterns of neural networks, and through fuzzy modeling, it calculates the optimal membership functions and fuzzy rules. The ANFIS-GA model is developed using MATLAB® software and is trained with the acquired data weather station and the electrical power output of the photovoltaic system located in Hermosillo, Sonora, México. The above was compared under the same parameters with an ANFIS model based on a hybrid algorithm. Reach values of RSME of 259.41, MAE of 132.7, MAPE of 4.56 for the ANFIS-GA model; RSME of 295.26, MAE of 149.58, and MAPE of 6.98 for the ANFIS model, respectively. The results indicate that the ANFIS-GA model emulates the power output with better precision, thus providing a valuable planning tool to predict photovoltaic system behavior.

Translated title of the contributionComparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA
Original languageEnglish
Article numberEner2956
JournalRevista Mexicana de Ingeniera Quimica
Volume22
Issue number1
DOIs
StatePublished - 1 Jan 2023

Bibliographical note

Publisher Copyright:
© 2023, Universidad Autonoma Metropolitana. All rights reserved.

Keywords

  • ANFIS
  • Genetic algorithms
  • Photovoltaic systems
  • Solar power generation
  • Statistical methods

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