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. © 2006 IEEE.

Item Type:	Refereed Article
Research Division:	Engineering
Research Group:	Control engineering, mechatronics and robotics
Research Field:	Field robotics
Objective Division:	Energy
Objective Group:	Environmentally sustainable energy activities
Objective Field:	Environmentally sustainable energy activities not elsewhere classified
UTAS Author:	Potter, CW (Mr Cameron Potter)
UTAS Author:	Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:	42646
Year Published:	2006
Web of Science® Times Cited:	231
Deposited By:	Engineering
Deposited On:	2006-08-01
Last Modified:	2007-11-16
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