• DocumentCode
    114217
  • Title

    Short term wind speed prediction using artificial neural networks

  • Author

    Lodge, Alexandra ; Xiao-Hua Yu

  • Author_Institution
    Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA
  • fYear
    2014
  • fDate
    26-28 April 2014
  • Firstpage
    539
  • Lastpage
    542
  • Abstract
    As an alternative to fossil fuels, wind is a plentiful, clean, and renewable natural resource for energy. Essentially, power generation from wind depends on wind speed; thus, wind speed prediction becomes increasingly important for modern wind farm management and supply-demand balancing in the Smart Grid. However, wind speed is generally very difficult to estimate, due to its non-stationary and intermittent nature. In this paper, an approach based on artificial neural network (ANN) is developed. The neural network is trained and tested using data from the National Wind Technology Center. Computer simulation results show that the proposed neural network model can successfully predict wind speed in real-time.
  • Keywords
    neural nets; power engineering computing; wind power; ANN; National Wind Technology Center; artificial neural networks; fossil fuels; renewable natural resource; short term wind speed prediction; smart grid; supply-demand balancing; wind farm management; wind power generation; Artificial neural networks; Biological neural networks; Temperature measurement; Training; Wind speed; Wind speed prediction; artificial neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Technology (ICIST), 2014 4th IEEE International Conference on
  • Conference_Location
    Shenzhen
  • Type

    conf

  • DOI
    10.1109/ICIST.2014.6920535
  • Filename
    6920535