A research of Monte Carlo optimized neural network for electricity load forecast

Yong, B; Huang, L; Li, F; Shen, J; Wang, X; Zhou, Q

A research of Monte Carlo optimized neural network for electricity load forecast

Yong, B Huang, L Li, F Shen, J

Wang, X Zhou, Q

Permalink

Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of Supercomputing, 2020, 76, (8), pp. 6330-6343
Issue Date:: 2020-08-01

Closed Access

	Filename	Description	Size
	1747493020961926.pdf	Published version	6.27 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yong, B
dc.contributor.author	Huang, L
dc.contributor.author	Li, F
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140
dc.contributor.author	Wang, X
dc.contributor.author	Zhou, Q
dc.date.accessioned	2020-10-19T19:29:33Z
dc.date.available	2020-10-19T19:29:33Z
dc.date.issued	2020-08-01
dc.identifier.citation	Journal of Supercomputing, 2020, 76, (8), pp. 6330-6343
dc.identifier.issn	0920-8542
dc.identifier.issn	1573-0484
dc.identifier.uri	http://hdl.handle.net/10453/143379
dc.description.abstract	© 2019, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we apply the Monte Carlo neural network (MCNN), a type of neural network optimized by Monte Carlo algorithm, to electricity load forecast. Meanwhile, deep MCNNs with one, two and three hidden layers are designed. Results have demonstrated that three-layer MCNN improves 70.35% accuracy for 7-week electricity load forecast, compared with traditional neural network. And five-layer MCNN improves 17.24% accuracy for 7-week forecast. This proves that MCNN has great potential in electricity load forecast.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of Supercomputing
dc.relation.isbasedon	10.1007/s11227-019-02828-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	A research of Monte Carlo optimized neural network for electricity load forecast
dc.type	Journal Article
utslib.citation.volume	76
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-19T19:29:22Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	76
utslib.citation.issue	8

Abstract:

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we apply the Monte Carlo neural network (MCNN), a type of neural network optimized by Monte Carlo algorithm, to electricity load forecast. Meanwhile, deep MCNNs with one, two and three hidden layers are designed. Results have demonstrated that three-layer MCNN improves 70.35% accuracy for 7-week electricity load forecast, compared with traditional neural network. And five-layer MCNN improves 17.24% accuracy for 7-week forecast. This proves that MCNN has great potential in electricity load forecast.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/143379