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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1658

Title: Genetic algorithm-based RBF neural network load forecasting model
Authors: Yang, Zhangang
Che, Yanbo
Cheng, K. W. Eric
Subjects: Load forecasting
RBF neural network
Real coding
Genetic algorithm
Convergence rate
Issue Date: 2007
Publisher: IEEE
Citation: PES 2007 : Power Engineering Society General Meeting, 2007, IEEE : 24-28 June, 2007, [p. 1-6].
Abstract: To overcome the limitation of the traditional load forecasting method, a new load forecasting system basing on radial basis Gaussian kernel function (RBF) neural network is proposed in this paper. Genetic algorithm adopting the real coding, crossover probability and mutation probability was applied to optimize the parameters of the neural network, and a faster convergence rate was reached. Theoretical analysis and simulations prove that this load forecasting model is more practical and has more precision than the traditional one.
Rights: © 2007 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: Conference Paper
URI: http://hdl.handle.net/10397/1658
ISBN: 1-4244-1298-6
Appears in Collections:EE Conference Papers & Presentations

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