On meteorological forecasts for energy management and large historical data: A first look

Abstract : This communication is devoted to a comparison between various meteorological forecasts, for the purpose of energy management, via different time series techniques. The first group of methods necessitates a large number of historical data. The second one does not and is much easier to implement, although its performances are today only slightly inferior. Theoretical justifications are related to methods stemming from a new approach to time series, artificial neural networks, computational intelligence and machine learning. Several numerical simulations are provided and discussed.
Type de document :
Communication dans un congrès
International Conference on Renewable Energies and Power Quality (ICREPQ'15), Mar 2015, La Coruña, Spain. 2015
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https://hal-polytechnique.archives-ouvertes.fr/hal-01093635
Contributeur : Michel Fliess <>
Soumis le : mercredi 10 décembre 2014 - 21:39:26
Dernière modification le : mardi 24 février 2015 - 01:04:50
Document(s) archivé(s) le : samedi 15 avril 2017 - 07:11:11

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ICREPQ_2015.pdf
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  • HAL Id : hal-01093635, version 1

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Cyril Voyant, Cédric Join, Michel Fliess, Marie-Laure Nivet, Marc Muselli, et al.. On meteorological forecasts for energy management and large historical data: A first look. International Conference on Renewable Energies and Power Quality (ICREPQ'15), Mar 2015, La Coruña, Spain. 2015. <hal-01093635v1>

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