基于集合经验模态分解和小波神经网络的短期风功率组合预测

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Abstract A hybrid model of wind power forecasting based on ensemble empirical mode decomposition and wavelet neural networks is proposed. Wind power signal is decomposed into several sequences by ensemble empirical mode decomposition to reduce the non-stationary of wind power. Secondly, mining the sequences characteristics by means of phase space reconstructed, and then an improved wavelet neural networks model of each component is established. Finally, the results of each component forecasting are superimposed to obtain the final forecasting result. The simulation results validate that the hybrid model has higher prediction accuracy and greater potential for engineering applications.

Key words： Wind power forecasting ensemble empirical mode decomposition(EEMD) wavelet neural networks(WNN) hybrid model

Received: 29 March 2013 Published: 25 March 2014

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TM315

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	Articles by authors
	Wang He
	Hu Zhijian
	Chen Zhen
	Zhang Menglin
	He Jianbo
	Li Chen

Cite this article:

Wang He,Hu Zhijian,Chen Zhen等. A Hybrid Model for Wind Power Forecasting Based on Ensemble Empirical Mode Decomposition and Wavelet Neural Networks[J]. Transactions of China Electrotechnical Society, 2013, 28(9): 137-144.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2013/V28/I9/137

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