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Prediction bands for solar energy: New short-term time series forecasting techniques

Author

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  • Michel Fliess

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

  • Cédric Join

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

  • Cyril Voyant

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

Abstract

Short-term forecasts and risk management for photovoltaic energy is studied via a new standpoint on time series: a result published by P. Cartier and Y. Perrin in 1995 permits, without any probabilistic and/or statistical assumption, an additive decomposition of a time series into its mean, or trend, and quick fluctuations around it. The forecasts are achieved by applying quite new estimation techniques and some extrapolation procedures where the classic concept of "seasonalities" is fundamental. The quick fluctuations allow to define easily prediction bands around the mean. Several convincing computer simulations via real data, where the Gaussian probability distribution law is not satisfied, are provided and discussed. The concrete implementation of our setting needs neither tedious machine learning nor large historical data, contrarily to many other viewpoints.

Suggested Citation

  • Michel Fliess & Cédric Join & Cyril Voyant, 2018. "Prediction bands for solar energy: New short-term time series forecasting techniques," Post-Print hal-01736518, HAL.
    • Handle: RePEc:hal:journl:hal-01736518
    DOI: 10.1016/j.solener.2018.03.049
    Note: View the original document on HAL open archive server: https://polytechnique.hal.science/hal-01736518
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    References listed on IDEAS

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    Cited by:

    1. Voyant, Cyril & Notton, Gilles & Duchaud, Jean-Laurent & Gutiérrez, Luis Antonio García & Bright, Jamie M. & Yang, Dazhi, 2022. "Benchmarks for solar radiation time series forecasting," Renewable Energy, Elsevier, vol. 191(C), pages 747-762.
    2. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    3. Zamani Gargari, Milad & Ghaffarpour, Reza, 2020. "Reliability evaluation of multi-carrier energy system with different level of demands under various weather situation," Energy, Elsevier, vol. 196(C).
    4. Koussaila Hamiche & Michel Fliess & Cédric Join & Hassane Abouaïssa, 2019. "Bullwhip effect attenuation in supply chain management via control-theoretic tools and short-term forecasts: A preliminary study with an application to perishable inventories," Post-Print hal-02050480, HAL.

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    Keywords

    time series; prediction bands; volatility; persistence; quick fluctuations; normality tests; risk; short-term forecasts; Solar energy; mean;
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