Title :
Online clustering for wind speed forecasting based on combination of RBF neural network and persistence method
Author :
Qin, Xiao ; Jiang, Cong ; Wang, Jun
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
This paper proposes an online clustering algorithm for wind speed forecasting. The algorithm combines the persistence method and the RBF neural network, and chooses an appropriate method according to different wind conditions. Computer simulations demonstrate that this algorithm can more accurately predict wind speed than either of the single methods and therefore is more effective for wind speed forecasting.
Keywords :
digital simulation; forecasting theory; geophysics computing; pattern clustering; power engineering computing; wind; wind power; RBF neural network; computer simulation; online clustering; persistence method; wind power; wind speed forecasting; Artificial neural networks; Clustering algorithms; Forecasting; Prediction algorithms; Wind forecasting; Wind power generation; Wind speed; Clustering algorithm; Combination forecasting; On-line prediction; Wind speed prediction;
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968687
