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Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network

Author

Listed:
  • Yaser I. Alamin

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Mensah K. Anaty

    (School of Renewable Energies and Petroleum Studies, Technopolis, International University of Rabat, LERMA, Sala el Jadida 11000, Morocco
    Renewable Energy and Environment Laboratory, Faculty of Science, Ibn Tofail University, Kenitra 14000, Morocco)

  • José Domingo Álvarez Hervás

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Khalid Bouziane

    (School of Renewable Energies and Petroleum Studies, Technopolis, International University of Rabat, LERMA, Sala el Jadida 11000, Morocco)

  • Manuel Pérez García

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Reda Yaagoubi

    (School of Geomatics and Surveying Engineering, Hassan II Agronomic and Veterinary Institute, Rabat 10101, Morocco)

  • María del Mar Castilla

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Merouan Belkasmi

    (School of Renewable Energies and Petroleum Studies, Technopolis, International University of Rabat, LERMA, Sala el Jadida 11000, Morocco)

  • Mohammed Aggour

    (Renewable Energy and Environment Laboratory, Faculty of Science, Ibn Tofail University, Kenitra 14000, Morocco)

Abstract

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.

Suggested Citation

  • Yaser I. Alamin & Mensah K. Anaty & José Domingo Álvarez Hervás & Khalid Bouziane & Manuel Pérez García & Reda Yaagoubi & María del Mar Castilla & Merouan Belkasmi & Mohammed Aggour, 2020. "Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network," Energies, MDPI, vol. 13(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3493-:d:381059
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    References listed on IDEAS

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    1. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).

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    Keywords

    HCPV; power prediction; RBF; ANN;
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