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A novel hybrid approach based on wavelet transform and fuzzy ARTMAP network for predicting wind farm power production

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6 Author(s)
Ashraf Ul Haque ; Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada ; Paras Mandal ; Julian Meng ; Anurag K. Srivastava
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This paper presents a novel hybrid intelligent algorithm based on the wavelet transform (WT) and fuzzy ARTMAP (FA) network for forecasting the power output of a wind farm utilizing meteorological information such as wind speed, wind direction, and temperature. The prediction capability of the proposed hybrid WT+FA model is demonstrated by an extensive comparison with a benchmark persistence method, other soft computing models (SCMs) and hybrid models as well. The test results show a significant improvement in forecasting error through the application of a proposed hybrid WT+FA model. The proposed hybrid wind power forecasting strategy is applied to real life data from Kent Hill wind farm located in New Brunswick, Canada.

Published in:

Industry Applications Society Annual Meeting (IAS), 2012 IEEE

Date of Conference:

7-11 Oct. 2012