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, S. H.
Lam, H. K.
Leung, Frank H. F.
Tam, Peter K. S.
Subjects: 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.
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.
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Type: Conference Paper
URI: http://hdl.handle.net/10397/1400
ISBN: 0-7803-7278-6
Appears in Collections:EIE Conference Papers & Presentations

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