Wind power forecasting using advanced neural networks models

Abstract : In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of the power output profile of a wind park. This model outperforms simple methods like persistence, as well as classical methods in the literature. The architecture of a forecasting model is optimised automatically by a new algorithm, that substitutes the usually applied trial-and-error method. Finally, the online implementation of the developed model into an advanced control system for the optimal operation and management of a real autonomous wind-diesel power system, is presented.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01441822
Contributeur : Magalie Prudon <>
Soumis le : vendredi 20 janvier 2017 - 11:15:27
Dernière modification le : jeudi 30 août 2018 - 18:54:02

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Georges Kariniotakis, Georges Stavrakakis, Eric Nogaret. Wind power forecasting using advanced neural networks models. IEEE Transactions on Energy Conversion, Institute of Electrical and Electronics Engineers, 1996, 11 (4), pp.762 - 767. ⟨10.1109/60.556376⟩. ⟨hal-01441822⟩

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