• DocumentCode
    128749
  • Title

    Ultra-short-term wind power prediction using BP neural network

  • Author

    Jinxuan Li ; Jiandong Mao

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Beifang Univ. of Nat., Yinchuan, China
  • fYear
    2014
  • fDate
    9-11 June 2014
  • Firstpage
    2001
  • Lastpage
    2006
  • Abstract
    As a newly renewable energy, wind power is one of the fastest growing and the most mature power generation technology. Based on the historical numerical weather prediction and corresponding wind power output data, an ultra short-term wind power prediction method for future four hours is presented and a prediction model based on BP neural network is established. Some experiments are performed. The results show that the prediction method is feasible and has important reference value for similar wind power prediction system.
  • Keywords
    load forecasting; neural nets; power generation planning; renewable energy sources; weather forecasting; wind power plants; BP neural network; historical numerical weather prediction; power generation technology; renewable energy; ultrashort-term wind power prediction method; Biological neural networks; Predictive models; Wind forecasting; Wind power generation; Wind speed; BP Neural Network; Power System; Ultra-Short-Term; Wind Power Prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4799-4316-6
  • Type

    conf

  • DOI
    10.1109/ICIEA.2014.6931497
  • Filename
    6931497