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|Title: ||A novel genetic-algorithm-based neural network for short-term load forecasting|
|Authors: ||Ling, S. H.|
Leung, Frank H. F.
Lam, H. K.
Tam, Peter K. S.
|Subjects: ||Genetic algorithm (GA)|
Short-term load forecasting
|Issue Date: ||Aug-2003 |
|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.|
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|Type: ||Journal/Magazine Article|
|Appears in Collections:||EIE Journal/Magazine Articles|
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