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Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

Author

Listed:
  • L. Cabezón

    (Bluetab, IBM Company, 28020 Madrid, Spain)

  • L. G. B. Ruiz

    (Department of Software Engineering, University of Granada, 18071 Granada, Spain)

  • D. Criado-Ramón

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

  • E. J. Gago

    (Engineering Construction and Project Management, School of Civil Engineering, University of Granada, 18071 Granada, Spain)

  • M. C. Pegalajar

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain)

Abstract

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

Suggested Citation

  • L. Cabezón & L. G. B. Ruiz & D. Criado-Ramón & E. J. Gago & M. C. Pegalajar, 2022. "Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study," Energies, MDPI, vol. 15(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8732-:d:978572
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    References listed on IDEAS

    as
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    4. J. R. S. Iruela & L. G. B. Ruiz & M. I. Capel & M. C. Pegalajar, 2021. "A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm," Energies, MDPI, vol. 14(13), pages 1-22, July.
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    Cited by:

    1. Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
    2. Mateusz Sumorek & Adam Idzkowski, 2023. "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data," Energies, MDPI, vol. 16(17), pages 1-23, September.
    3. L. G. B. Ruiz & M. C. Pegalajar, 2023. "Advances in Energy Efficiency through Neural-Network-Based Models," Energies, MDPI, vol. 16(5), pages 1-3, February.

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