Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1400
Title: A novel GA-based neural network for short-term load forecasting
Authors: Ling, SH
Lam, HK
Leung, FHF 
Tam, PKS
Keywords: Electric load forecasting
Genetic algorithms
Learning systems
Mathematical models
Transfer functions
Issue Date: 2002
Publisher: IEEE
Source: IJCNN'02 : proceedings of the 2002 International Joint Conference on Neural Networks : May 12-17, 2002, Honolulu, Hawaii, p. 2761-2766 How to cite?
Abstract: This paper presents a GA-based neural network with a novel neuron model. In this model, the neuron has two activation transfer functions and exhibits a node-by-node relationship in the hidden layer. This neural network provides a better performance than a traditional feed-forward neural network and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by GA with arithmetic crossover and non-uniform mutation. An application on short-term load forecasting is given to show the merits of the proposed neural network.
URI: http://hdl.handle.net/10397/1400
ISBN: 0-7803-7278-6
Rights: © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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