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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm


Haque, AU and Mandal, P and Meng, J and Negnevitsky, M, Wind speed forecast model for wind farm based on a hybrid machine learning algorithm, International Journal of Sustainable Energy, 34, (1) pp. 35-51. ISSN 1478-6451 (2015) [Refereed Article]

Copyright Statement

Copyright 2013 Taylor and Francis

DOI: doi:10.1080/14786451.2013.826224


This paper presents a newstrategy for wind speed forecasting based on a hybrid machine learning algorithm, composed of a data filtering technique based on wavelet transform (WT) and a soft computing model based on the fuzzy ARTMAP (FA) network. The prediction capability of the proposed hybrid WT + FA model is demonstrated by an extensive comparison with some other existing wind speed forecasting methods. The results show a significant improvement in forecasting error through the application of a proposed hybrid WT + FA model. The proposed wind speed forecasting strategy is applied to real data acquired from the North Cape wind farm located in PEI, Canada.

Item Details

Item Type:Refereed Article
Keywords:fuzzy ARTMAP network, machine learning algorithm, short-term wind speed forecasting, wavelet transform
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Renewable energy
Objective Field:Renewable energy not elsewhere classified
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:88395
Year Published:2015 (online first 2013)
Deposited By:Engineering
Deposited On:2014-01-31
Last Modified:2017-11-06
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