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.
王贺, 胡志坚, 陈珍, 仉梦林, 贺建波, 李晨. 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[J]. 电工技术学报, 2013, 28(9): 137-144.
Wang He, Hu Zhijian, Chen Zhen, Zhang Menglin, He Jianbo, Li Chen. A Hybrid Model for Wind Power Forecasting Based on Ensemble Empirical Mode Decomposition and Wavelet Neural Networks. Transactions of China Electrotechnical Society, 2013, 28(9): 137-144.
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2013, Vol. 28 
