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Investigating using stochastic methods to generate training data for windpower prediction

Citation

Potter, C and Negnevitsky, M, Investigating using stochastic methods to generate training data for windpower prediction, Australian Journal of Electrical & Electronics Engineering, 3, (2) pp. 137-145. ISSN 1448-837X (2007) [Refereed Article]


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Copyright Statement

Copyright 2007 Institution of Engineers, Australia

Official URL: http://www.engineersaustralia.org.au//resources-an...

Abstract

This paper investigates the potential capability of stochastic methods to generate data for unndpower prediction purposes. Stochastic models have been used to develop data before, however, this paper shows that a simplistic single histogram model will not suffice for windpower purposes. The need to generate data is important as often there is only a short period of data available for use in developing a prediction system. This need is then exaggerated if information from multiple wind turbines is desired for the data input. © Institution of Engineers.

Item Details

Item Type:Refereed Article
Keywords:stochastic methods, training data, windpower prediction
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Control Systems, Robotics and Automation
Objective Division:Energy
Objective Group:Environmentally Sustainable Energy Activities
Objective Field:Environmentally Sustainable Energy Activities not elsewhere classified
Author:Potter, C (Mr Potter)
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:50794
Year Published:2007
Deposited By:Engineering
Deposited On:2007-08-01
Last Modified:2012-12-03
Downloads:0

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