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

Author

Listed:
  • Nun Pitalúa-Díaz

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • Fernando Arellano-Valmaña

    (Facultad de Ingeniería, Universidad Autónoma del Carmen, Calle 56 No. 4 Esq. Avenida Concordia Col. Benito Juárez C.P. 24180 Cd. Del Carmen, Campeche, Mexico)

  • Jose A. Ruz-Hernandez

    (Facultad de Ingeniería, Universidad Autónoma del Carmen, Calle 56 No. 4 Esq. Avenida Concordia Col. Benito Juárez C.P. 24180 Cd. Del Carmen, Campeche, Mexico)

  • Yasuhiro Matsumoto

    (Departamento de Ingeniería Eléctrica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, La Laguna Ticoman, C.P. 07360 Ciudad de México, CDMX, Mexico)

  • Hussain Alazki

    (Facultad de Ingeniería, Universidad Autónoma del Carmen, Calle 56 No. 4 Esq. Avenida Concordia Col. Benito Juárez C.P. 24180 Cd. Del Carmen, Campeche, Mexico)

  • Enrique J. Herrera-López

    (Biotecnología Industrial, Sublínea Bioelectrónica, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco A.C., Camino Arenero 1227, Col. El Bajío del Arenal, C.P. 45019 Zapopan Jalisco, Mexico)

  • Jesús Fernando Hinojosa-Palafox

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • A. García-Juárez

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • Ricardo Arturo Pérez-Enciso

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

  • Enrique Fernando Velázquez-Contreras

    (Departamento de Ingeniería Industrial, Departamento de Ingeniería Química y Metalurgia, Departamento de Investigación en Física, Departamento de Investigación en Polímeros y Materiales, Universidad de Sonora, Blvd. Luis Encinas y Rosales S/N, Col. Centro. Hermosillo 83000, Sonora C.P., Mexico)

Abstract

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.

Suggested Citation

  • 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 Art, 2019. "An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico," Energies, MDPI, vol. 12(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2662-:d:247553
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    2. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    3. Yousri, Dalia & Thanikanti, Sudhakar Babu & Allam, Dalia & Ramachandaramurthy, Vigna K. & Eteiba, M.B., 2020. "Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models’ parameters," Energy, Elsevier, vol. 195(C).
    4. Ajith Gopi & Prabhakar Sharma & Kumarasamy Sudhakar & Wai Keng Ngui & Irina Kirpichnikova & Erdem Cuce, 2022. "Weather Impact on Solar Farm Performance: A Comparative Analysis of Machine Learning Techniques," Sustainability, MDPI, vol. 15(1), pages 1-28, December.

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