NDLI: Research on short-term load forecasting model based on wavelet decomposition and neural network

Content Provider	IEEE Xplore Digital Library
Author	Changhao Xia Bangjun Lei Changguo Rao Zizheng He
Copyright Year	2011
Description	Author affiliation: Institute of Intelligent Vision and Image Information, Electrical Engineering and Renewable Energy School, China Three Gorges University, Yichang Hubei 443002 China (Changhao Xia; Bangjun Lei; Changguo Rao; Zizheng He)
Abstract	This paper gives a method which bases on the wavelet decomposition and the neural network to predict the short-time load. Using wavelet transform, the load sequence is decomposed into sub-sequences on different scales, then using appropriate artificial neural network models the sub-sequences of forecasting date are predicted. Finally, by means of restructuring from the sub-sequences, the final forecasting results of the load sequence are obtained. The actual load data of electric network in Yichang, Hubei, China are applied to build the model. The instance shows that the proposed method is possessed of higher forecasting accuracy and better adaptability than back propagation (BP) neural network forecasting methods.
Starting Page	830
Ending Page	834
File Size	271849
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022226
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Load forecasting load forecasting wavelet transform wavelet decomposition Forecasting neural network Multiresolution analysis Load modeling
Content Type	Text
Resource Type	Article

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