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

Zhenhai Guo, Weigang Zhao*, Haiyan Lu, Jianzhou Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

482 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)241-249
Number of pages9
JournalRenewable Energy
Volume37
Issue number1
DOIs
Publication statusPublished - Jan 2012
Externally publishedYes

Keywords

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

Fingerprint

Dive into the research topics of 'Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model'. Together they form a unique fingerprint.

Cite this