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Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

Author

Listed:
  • A. Bassam

    (Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico)

  • O. May Tzuc

    (Posgrado en Energías Renovables, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico)

  • M. Escalante Soberanis

    (Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico)

  • L. J. Ricalde

    (Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico)

  • B. Cruz

    (Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico)

Abstract

Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.

Suggested Citation

  • A. Bassam & O. May Tzuc & M. Escalante Soberanis & L. J. Ricalde & B. Cruz, 2017. "Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System," Sustainability, MDPI, vol. 9(8), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:8:p:1399-:d:108384
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    Cited by:

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    2. Ahmad Manasrah & Mohammad Masoud & Yousef Jaradat & Piero Bevilacqua, 2022. "Investigation of a Real-Time Dynamic Model for a PV Cooling System," Energies, MDPI, vol. 15(5), pages 1-15, March.
    3. Daniel Gonzalez Montoya & Juan David Bastidas-Rodriguez & Luz Adriana Trejos-Grisales & Carlos Andres Ramos-Paja & Giovanni Petrone & Giovanni Spagnuolo, 2018. "A Procedure for Modeling Photovoltaic Arrays under Any Configuration and Shading Conditions," Energies, MDPI, vol. 11(4), pages 1-17, March.
    4. 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.
    5. Orozco-Gutierrez, M.L. & Spagnuolo, G. & Ramos-Paja, C.A. & Ramirez-Scarpetta, J.M & Ospina-Agudelo, B., 2019. "Enhanced simulation of total cross tied photovoltaic arrays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 49-64.
    6. Reza Salehi & Santhana Krishnan & Mohd Nasrullah & Sumate Chaiprapat, 2023. "Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    7. Serrano-Luján, L. & Toledo, C. & Colmenar, J.M. & Abad, J. & Urbina, A., 2022. "Accurate thermal prediction model for building-integrated photovoltaics systems using guided artificial intelligence algorithms," Applied Energy, Elsevier, vol. 315(C).
    8. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.

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