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This paper describes very short-term wind prediction for power generation, utilizing 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 the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach—the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed 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 toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind power into electrical power systems is likely to require significant improvements to transmission infrastructure over coming years. Planning for these developments may involve modeling of power systems using estimates of the time-varying power output of proposed wind farms. Such estimates may be derived from wind measurements, although it is rarely the case that measurements are available simultaneously for all sites of interest. In the present work, a method is developed to produce synthetic time-series of wind power at several locations based on a measured time-series of wind speed from a reference site. The stochastic properties of the modeled time-series are shown to be comparable with those of measured data from south-eastern Australia. It is anticipated that with appropriate tuning for local conditions, the method could be applied in other geographical regions. [ABSTRACT FROM PUBLISHER]