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Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

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  • Liu, Hui
  • Tian, Hong-qi
  • Pan, Di-fu
  • Li, Yan-fei

Abstract

Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them.

Suggested Citation

  • Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    • Handle: RePEc:eee:appene:v:107:y:2013:i:c:p:191-208
    DOI: 10.1016/j.apenergy.2013.02.002
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