Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model

作者：Guo, Zhenhai; Zhao, Weigang^*; Lu, Haiyan; Wang, Jianzhou

来源：Renewable Energy, 2012, 37(1): 241-249.

DOI：10.1016/j.renene.2011.06.023

• Wind speed multi-step forecasting
• Empirical mode decomposition
• Feed-forward neural network
• High frequency
• Partial autocorrelation function

In this paper, a modified EMD-FNN model (empirical mode decomposition (EMD) based feed-forward neural network (FNN) ensemble learning paradigm) is proposed for wind speed forecasting. The nonlinear and non-stationary original wind speed series is first decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EMD technique for a deep insight into the data structure. Then these sub-series except the high frequency are forecasted respectively by FNN whose input variables are selected by using partial autocorrelation PACF). Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original wind speed series. Further more, the developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China.

• 出版日期2012-1
• 单位中国科学院大气物理研究所; 兰州大学