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

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.
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Contributor : Michel Fliess Connect in order to contact the contributor
Submitted on : Saturday, March 17, 2018 - 5:52:59 PM
Last modification on : Friday, July 8, 2022 - 10:06:50 AM
Long-term archiving on: : Tuesday, September 11, 2018 - 5:58:40 AM


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Michel Fliess, Cédric Join, Cyril Voyant. Prediction bands for solar energy: New short-term time series forecasting techniques. Solar Energy, 2018, 166, pp.519-528. ⟨10.1016/j.solener.2018.03.049⟩. ⟨hal-01736518⟩



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