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

Zhenhai Guo

doi:10.1016/j.renene.2011.06.023

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

2012-01-01

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DOI：10.1016/j.renene.2011.06.023

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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 function (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. (C) 2011 Elsevier Ltd. All rights reserved.

Keywords:

Wind speed multi-step forecasting

Empirical mode decomposition

Feed-forward neural network

High frequency

Partial autocorrelation function

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

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