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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
Source: 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.
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Type: Conference Paper
ISBN: 1-4244-1298-6
Appears in Collections:EE Conference Papers & Presentations

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