A novel genetic-algorithm-based neural network for short-term load forecasting

Ling, SH; Leung, FHF; Lam, HK; Lee, YS; Tam, PKS

A novel genetic-algorithm-based neural network for short-term load forecasting

Ling, SH

Leung, FHF Lam, HK Lee, YS Tam, PKS

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2003, 50 (4), pp. 793 - 799
Issue Date:: 2003-08-01

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Lam, HK	en_US
dc.contributor.author	Lee, YS	en_US
dc.contributor.author	Tam, PKS	en_US
dc.date.issued	2003-08-01	en_US
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2003, 50 (4), pp. 793 - 799	en_US
dc.identifier.issn	0278-0046	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15343
dc.description.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.	en_US
dc.relation.ispartof	IEEE Transactions on Industrial Electronics	en_US
dc.relation.isbasedon	10.1109/TIE.2003.814869	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	A novel genetic-algorithm-based neural network for short-term load forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	50	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	WOS:000184376400021	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	50	en_US

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.

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http://hdl.handle.net/10453/15343