Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/81157
Title: Short-term load forecasting using wavelet networks
Authors: Chang, C.S. 
Fu, W.
Yi, M.
Keywords: Load forecasting
Power systems operation/planning
Wavelet networks
Issue Date: 1998
Citation: Chang, C.S.,Fu, W.,Yi, M. (1998). Short-term load forecasting using wavelet networks. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications 6 (4) : 217-223. ScholarBank@NUS Repository.
Abstract: This paper describes an application of a type of neural network, called the wavelet network, to short-term load forecasting. Based on the wavelet transform theory, the recently developed wavelet network is proposed as an alternative to the feed-fonvard neural networks for approximating arbitrary nonlinear functions and for solving classification problems. Performance results are given for the daily peak and total load forecasting using data from a real power system. A comparison among results from the wavelet network, the radial-basis feedforward (RBF) neural network and back-propagation (BP) neural network shows that the wavelet network is encouraging.
Source Title: International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/81157
ISSN: 09691170
Appears in Collections:Staff Publications

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