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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1405

Title: A novel genetic-algorithm-based neural network for short-term load forecasting
Authors: Ling, S. H.
Leung, Frank H. F.
Lam, H. K.
Lee, Yim-shu
Tam, Peter K. S.
Subjects: Genetic algorithm (GA)
Neural network
Short-term load forecasting
Issue Date: Aug-2003
Publisher: IEEE
Citation: IEEE transactions on industrial electronics, Aug. 2003, v. 50, no. 4, p. 793-799.
Abstract: This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network.
Rights: © 2003 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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1405
ISSN: 0278-0046
Appears in Collections:EIE Journal/Magazine Articles

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