An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico

Nun Pitalúa-Díaz, Fernando Arellano-Valmaña, Jose A. Ruz-Hernandez, Yasuhiro Matsumoto, Hussain Alazki, Enrique J. Herrera-López, Jesús Fernando Hinojosa-Palafox, A. García-Juárez, Ricardo Arturo Pérez-Enciso, Enrique Fernando Velázquez-Contreras

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error.

Original languageEnglish
Article number2662
Issue number14
StatePublished - 11 Jul 2019

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© 2019 by the authors.


  • Gradient descent
  • Photovoltaic system
  • Statistical method
  • Sustainable development


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