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Communication dans un congrès

Long Term Forecasting of Wind Speed for Wind Energy Application

Nihad Aghbalou 1 Abdérafi Charki 2 Saida Elazzouzi 3 Kamal Reklaoui 3
1 LIMOS - Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes
2 LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes
3 ENSAT - Ecole Nationale des Sciences Appliquées [Tanger]
Abstract : A novel method for long term forecasting of wind speed distribution is proposed based on the concept of training neural network. A phase space reconstruction method is used to track the evolution of the wind speed distribution function parameters in a dynamic system. Then, the neural network training and forecasting features are used to learn nonlinear model between historical data and next observation. Moreover, different estimators have been applied and compared to fit the annual distribution of the wind speed in the studied sites before applying the proposed approach. The proposed method shows a good performance and could be successfully applied in wind energy yield.
Keywords : Component Long term wind speed Time series forecasting Weibull distribution Wind energy assessment
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https://hal.univ-angers.fr/hal-02531116
Contributeur : Marie-Françoise Gerard <>
Soumis le : vendredi 3 avril 2020 - 14:32:59
Dernière modification le : jeudi 30 avril 2020 - 10:05:32

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UNIV-ANGERS | LARIS | LIMOS | CNRS | PRES_CLERMONT

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Nihad Aghbalou, Abdérafi Charki, Saida Elazzouzi, Kamal Reklaoui. Long Term Forecasting of Wind Speed for Wind Energy Application. 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), Dec 2018, Rabat, Morocco. pp.1-7, ⟨10.1109/IRSEC.2018.8702892⟩. ⟨hal-02531116⟩

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