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Investigating using stochastic methods to generate training data for windpower prediction

journal contribution
posted on 2023-05-16, 21:19 authored by Potter, C, Michael NegnevitskyMichael Negnevitsky
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.

History

Publication title

Australian Journal of Electrical & Electronics Engineering

Pagination

137-145

ISSN

1448-837X

Department/School

School of Engineering

Publisher

Engineers Media

Place of publication

Crow Nest NSW, Australia

Rights statement

Copyright 2007 Institution of Engineers, Australia

Repository Status

  • Restricted

Socio-economic Objectives

Environmentally sustainable energy activities not elsewhere classified

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