• DocumentCode
    3405329
  • Title

    Short-term prediction of wind power combining GM(1,1) model with cloud model

  • Author

    Xiaojuan Han ; Fangyuan Meng ; Zhihui Song ; Xiangjun Li

  • Author_Institution
    Coll. of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China
  • fYear
    2012
  • fDate
    15-17 Aug. 2012
  • Firstpage
    191
  • Lastpage
    195
  • Abstract
    This paper proposes a new method to predict wind power of wind farm using the combination of GM(1,1) model and cloud model. The original wind power signals are decomposed into high frequency part and low frequency part by wavelet decomposition. Cloud model is constructed to predict wind power of high frequency part and GM(1,1) model is used to predict wind power of low frequency part. The predicted power can be obtained by high frequency part and low frequency part. The simulation example shows that the method proposed in this paper is obviously better than single predicting method and the effectiveness of the method is verified by the predicting results.
  • Keywords
    queueing theory; wavelet transforms; wind power plants; GM(1,1) model; cloud model; high frequency part; low frequency part; short-term prediction; wavelet decomposition; wind farm; wind power; wind power signals; Computational modeling; Entropy; Forecasting; Generators; Predictive models; Wind power generation; Wind speed; GM(1,1); cloud model; combination prediction; power prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automation and Logistics (ICAL), 2012 IEEE International Conference on
  • Conference_Location
    Zhengzhou
  • ISSN
    2161-8151
  • Print_ISBN
    978-1-4673-0362-0
  • Electronic_ISBN
    2161-8151
  • Type

    conf

  • DOI
    10.1109/ICAL.2012.6308195
  • Filename
    6308195