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Load forecasting using fuzzy wavelet neural networks

Amina, Mahdi and Kodogiannis, Vassilis (2011) Load forecasting using fuzzy wavelet neural networks. In: Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ), Taipei, 27-30 June 2011. IEEE, pp. 1033-1040. ISBN 9781424473151

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Official URL: http://dx.doi.org/10.1109/FUZZY.2011.6007492

Abstract

Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. This paper presents the development of a novel fuzzy wavelet neural network model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. In the proposed scheme, a wavelet neural network has replaced the classic TSK model in the consequent part, while subtractive clustering has been applied to the definition of fuzzy rules. The forecasting error statistical results, corresponding to the minimum and maximum load time-series, indicate that the proposed load forecasting model provides more accurate forecasts, compared to conventional neural networks models.

Item Type:Book Section
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:10701
Deposited On:10 Jul 2012 15:48
Last Modified:25 Jun 2013 12:09

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