GVM Based Copy-Dynamics Model for Electricity Load Forecast

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

GVM Based Copy-Dynamics Model for Electricity Load Forecast

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

Wang, X Zhou, Q

Permalink

Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Frontier Computing, 2019, 542, pp. 203-211
Issue Date:: 2019-01-01

Closed Access

	Filename	Description	Size
	GVM based copy-dynamics model for electricity load forecast.pdf	Submitted version	1.21 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	2018-07-03
dc.date.accessioned	2020-06-08T03:22:42Z
dc.date.available	2020-06-08T03:22:42Z
dc.date.issued	2019-01-01
dc.identifier.citation	Frontier Computing, 2019, 542, pp. 203-211
dc.identifier.isbn	9789811336478
dc.identifier.issn	1876-1100
dc.identifier.issn	1876-1119
dc.identifier.uri	http://hdl.handle.net/10453/141190
dc.description.abstract	© 2019, Springer Nature Singapore Pte Ltd. Electricity load forecast, as the core of electricity scheduling, plays a vita role in meeting the basic needs of modern human life. It has been widely studied in the past few decades. However, literature studies have shown that, as a problem of time series forecast, electricity forecast is prone to be influenced by many environmental factors, which result in lacking accuracy and stability in practice. In this paper, the General Vector Machine (GVM), a new type learning machine which was derived from Neural Network (NN) and Support Vector Machine (SVM), is applied into electricity load forecast. Meanwhile, copy-dynamics idea is introduced to electricity load forecast. Results reveal that, based on copy-dynamics, the maximum precision promotion of GVM reaches 71.7%, compared with BP. Hence, GVM and copy-dynamics models have great potential in electricity load forecast.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Frontier Computing
dc.relation.ispartof	International Conference on Frontier Computing
dc.relation.isbasedon	10.1007/978-981-13-3648-5_24
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	GVM Based Copy-Dynamics Model for Electricity Load Forecast
dc.type	Conference Proceeding
utslib.citation.volume	542
utslib.location.activity	Kuala Lumpur, Malaysia
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	*
pubs.consider-herdc	false
dc.date.updated	2020-06-08T03:22:37Z
pubs.finish-date	2018-07-06
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2018-07-03
pubs.volume	542
utslib.start-page	203
dc.location	Switzerland

Abstract:

© 2019, Springer Nature Singapore Pte Ltd. Electricity load forecast, as the core of electricity scheduling, plays a vita role in meeting the basic needs of modern human life. It has been widely studied in the past few decades. However, literature studies have shown that, as a problem of time series forecast, electricity forecast is prone to be influenced by many environmental factors, which result in lacking accuracy and stability in practice. In this paper, the General Vector Machine (GVM), a new type learning machine which was derived from Neural Network (NN) and Support Vector Machine (SVM), is applied into electricity load forecast. Meanwhile, copy-dynamics idea is introduced to electricity load forecast. Results reveal that, based on copy-dynamics, the maximum precision promotion of GVM reaches 71.7%, compared with BP. Hence, GVM and copy-dynamics models have great potential in electricity load forecast.

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

http://hdl.handle.net/10453/141190