• DocumentCode
    2674746
  • Title

    Very short-term wind forecasting for Tasmanian power generation

  • Author

    Potter, Cameron ; Negnevitsky, Michael

  • Author_Institution
    Tasmania Univ., Hobart, Tas.
  • fYear
    0
  • fDate
    0-0 0
  • Abstract
    Summary form only given. This paper describes very short-term wind prediction for power generation, utilising a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make best use of transmission line capability and address concerns with system frequency in a reregulated system, accurate very short-term forecasts are essential. The research introduces a novel approach the application of an adaptive neural fuzzy inference system (ANFIS) to forecasting a wind time series. Over the very short-term forecast interval, both wind speed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed towards on oncoming wind. For this reason, this paper forecasts wind vectors, rather than wind speed or power output
  • Keywords
    fuzzy neural nets; inference mechanisms; load forecasting; power engineering computing; time series; wind power plants; wind turbines; Tasmanian power generation prediction; adaptive neural fuzzy inference system; short-term wind forecasting; transmission line capability; turbines; wind time series forecasting; wind vector forecasting; windpower generation; Australia; Frequency; Fuzzy systems; Power transmission lines; Turbines; Wind energy generation; Wind forecasting; Wind power generation; Wind speed;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society General Meeting, 2006. IEEE
  • Conference_Location
    Montreal, Que.
  • Print_ISBN
    1-4244-0493-2
  • Type

    conf

  • DOI
    10.1109/PES.2006.1709044
  • Filename
    1709044