NDLI: Wavelet-GA-ANN Based Hybrid Model for Accurate Prediction of Short-Term Load Forecast

Content Provider	IEEE Xplore Digital Library
Author	Sinha, N. Loi Lei Lai Ghosh, P.K. Yingnan Ma
Copyright Year	2007
Description	Author affiliation: NIT, Silchar (Sinha, N.; Ghosh, P.K.)
Abstract	This paper proposes a hybrid model developed through wiser integration of wavelet transforms, floating point GA and artificial neural networks for prediction of short-term load. The use of wavelet transforms has added the capability of capturing of both global trend and hidden templates in loads, which is otherwise very difficult to incorporate into the prediction model of ANN. Auto-configuring RBF networks are used for predicting the wavelet coefficients of the future loads. Floating point GA (FPGA) is used for optimizing the RBF networks. The use of GA optimized RBF network has added to the model the online prediction capability of short-term loads accurately. The performance of the proposed model is validated using Queensland electricity demand data from the Australian National Electricity Market. Results demonstrate that the proposed model is more accurate as compared to RBF only model.
Starting Page	1
Ending Page	8
File Size	357347
Page Count	8
File Format	PDF
ISBN	9789860126075
DOI	10.1109/ISAP.2007.4441661
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-05
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ISAP
Subject Keyword	Wavelet transforms ANN Power system management RBF networks Artificial neural networks Genetic Algorithm Predictive models Power system modeling Load forecast Load forecasting Short-term load forecast Radial basis function networks Electricity supply industry Artificial intelligence Power generation
Content Type	Text
Resource Type	Article

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