Résumé

Global demand for electrical energy is in constant increase all over the world, leading to new renewable energy sources such as wind energy. Therefore, precise wind prediction is very important for efficient management of grid-connected wind farms. This article examines the use of discrete wavelet transform (DWT) with artificial neural networks (ANN) for wind speed forecasting. The wavelet transform is employed to smooth the wind speed time series for better prediction using neural
networks. Using the wind speed data of the region of Annaba situated in the east of Algeria, the obtained results show the db4 wavelet with 5-level decomposition outperforms all other wavelet
families in terms of forecasting accuracy.

Information

Item Type:	Conference
Divisions:	» Faculté des Sciences et de la Technologie
ePrint ID:	2473
Date Deposited:	2020-12-20
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/fr/publication/article/2473

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