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

Michel Fliess (), Cédric Join () and Cyril Voyant ()
Additional contact information
Michel Fliess: AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques, 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
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 - Centre Inria de l'Université de Lille - Inria - Institut National de Recherche en Informatique et en Automatique
Cyril Voyant: SPE - Laboratoire « Sciences pour l’Environnement » (UMR CNRS 6134 SPE) - CNRS - Centre National de la Recherche Scientifique - Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli], Centre hospitalier d'Ajaccio

Post-Print from HAL

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.

Keywords: time series; prediction bands; volatility; persistence; quick fluctuations; normality tests; risk; short-term forecasts; Solar energy; mean (search for similar items in EconPapers)
Date: 2018-05-15
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-for and nep-rmg
Note: View the original document on HAL open archive server: https://polytechnique.hal.science/hal-01736518v1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published in Solar Energy, 2018, 166, pp.519-528. ⟨10.1016/j.solener.2018.03.049⟩

Downloads: (external link)
https://polytechnique.hal.science/hal-01736518v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01736518

DOI: 10.1016/j.solener.2018.03.049

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().