Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study

Cédric Join 1, 2, 3 Cyril Voyant 4, 5 Michel Fliess 3, 6 Marc Muselli 4 Marie-Laure Nivet 4 Christophe Paoli 7 Frédéric Chaxel 2
1 NON-A - Non-Asymptotic estimation for online systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 CRAN - Centre de Recherche en Automatique de Nancy
3 AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques
4 SPE - Sciences pour l'environnement
5 Unité de Radiothérapie
6 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
7 Département de Génie Informatique
Abstract : This communication is devoted to solar irradiance and irradiation short-term forecasts, which are useful for electricity production. Several different time series approaches are employed. Our results and the corresponding numerical simulations show that techniques which do not need a large amount of historical data behave better than those which need them, especially when those data are quite noisy.
Keywords : trends quick fluctuations machine learning multilayer perceptron big data. time series electricity production solar irradiance and irradiation big data Meteorological forecasts
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Conference papers
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  • HAL Id : hal-01068569, version 1
  • ARXIV : 1409.7476

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Cédric Join, Cyril Voyant, Michel Fliess, Marc Muselli, Marie-Laure Nivet, et al.. Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study. 3rd International Symposium on Environment-Friendly Energies and Applications, EFEA 2014, Nov 2014, Paris, France. ⟨hal-01068569⟩

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