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Solar energy production: Short-term forecasting and risk management

Author

Listed:
  • Cédric Join

    (CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques, NON-A - Non-Asymptotic estimation for online systems - Inria Lille - Nord Europe - Inria - Institut National de Recherche en Informatique et en Automatique - CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 - Centrale Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Michel Fliess

    (LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques)

  • Cyril Voyant

    (SPE - Sciences pour l'environnement - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique, Centre hospitalier d'Ajaccio)

  • Frédéric Chaxel

    (CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

Abstract

Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient and easily implementable. Numerous computer simulations are presented.

Suggested Citation

  • Cédric Join & Michel Fliess & Cyril Voyant & Frédéric Chaxel, 2016. "Solar energy production: Short-term forecasting and risk management," Post-Print hal-01272152, HAL.
    • Handle: RePEc:hal:journl:hal-01272152
    DOI: 10.1016/j.ifacol.2016.07.790
    Note: View the original document on HAL open archive server: https://polytechnique.hal.science/hal-01272152v3
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    Citations

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    Cited by:

    1. Michel Fliess & Cédric Join & Cyril Voyant, 2018. "Prediction bands for solar energy: New short-term time series forecasting techniques," Post-Print hal-01736518, HAL.
    2. Voyant, Cyril & Motte, Fabrice & Fouilloy, Alexis & Notton, Gilles & Paoli, Christophe & Nivet, Marie-Laure, 2017. "Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies," Energy, Elsevier, vol. 120(C), pages 199-208.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Voyant, Cyril & Notton, Gilles & Darras, Christophe & Fouilloy, Alexis & Motte, Fabrice, 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case," Energy, Elsevier, vol. 125(C), pages 248-257.

    More about this item

    Keywords

    confidence bands; time series; intelligent knowledge-based systems; forecasts; persistence; solar energy; volatility; risk; normality tests;
    All these keywords.

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