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Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN

Author(s): Zeng-Wei Zhenga, Yuan-Yi Chena,b, Xiao-Wei Zhoua, Mei-Mei Huoa*, Bo Zhaoc, Min-Yi Guod
a Department of Computer, Zhejiang University City College, Hangzhou 310015, China
b College of Computer Science and Technology, Zhejiang University,  Hangzhou 310027, China
c Institute of Zhejiang Electric Power Test & Research, Hangzhou 310014, China
d College of Computer Science and Technology, Shanghai Jiao Tong University ,  Shanghai 200240, China
International Journal of Smart Grid and Clean Energy, vol. 2, no. 2, May 2013: pp. 192–199
ISSN: 2315-4462
Digital Object Identifier: 10.12720/sgce.2.2.192-199 

Abstract: In order to effectively predict wind farm power with non-linear and non-stationary characteristics, a prediction model based on empirical mode decomposition (EMD) and radial basis function neural networks (RBFNN) was designed. The forecast model uses EMD to decompose the wind power into several intrinsic mode functions (IMF) and one residue. The RBFNN was used to construct a prediction model for each IMF component and the residue, the input variables of the prediction model are triple: wind speed, wind direction, and history wind power. All the prediction results of components were aggregated to obtain the ultimate prediction result. The simulation results show that compared with the traditional prediction method based on artificial neural networks, this method has high prediction precision and strong adaptability.
Keywords: Wind power prediction, empirical mode decomposition, radial basis function neural networks, Kalman filter, least squares method

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