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Short-term wind speed forecasting using empirical mode decomposition and feature selection

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  • Zhang, Chi
  • Wei, Haikun
  • Zhao, Junsheng
  • Liu, Tianhong
  • Zhu, Tingting
  • Zhang, Kanjian

Abstract

Due to the non-linear and non-stationary characteristics of the wind speed time series, it is generally difficult to model and predict such series by single forecasting models. In this paper, two novel hybrid models, which combine empirical mode decomposition (EMD), feature selection with artificial neural network (ANN) and support vector machine (SVM), are proposed for short-term wind speed prediction. First, the original wind speed time series is decomposed into a set of sub-series by EMD. Second, the initial features (input variables) and targets are constructed from all the sub-series and the original series. Then, a feature selection process is introduced to constitute the relevant and informative features. Finally, a predictive model (ANN or SVM) is established using these selected features. The effectiveness of the proposed models has been assessed on the real datasets recorded from three wind farms in China. Compared with the single ANN, SVM, traditional EMD-based ANN, and traditional EMD-based SVM, the experimental results show that the proposed models have satisfactory performance, which are suitable for the wind speed prediction.

Suggested Citation

  • Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:727-737
    DOI: 10.1016/j.renene.2016.05.023
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    References listed on IDEAS

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