Combined General Vector Machine for Single Point Electricity Load Forecast

Yong, B; Wei, Y; Shen, J; Li, F; Jiang, X; Zhou, Q

Combined General Vector Machine for Single Point Electricity Load Forecast

Yong, B Wei, Y Shen, J Li, F Jiang, X Zhou, Q

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Frontier Computing, 2020, 551, pp. 283-291
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yong, B
dc.contributor.author	Wei, Y
dc.contributor.author	Shen, J
dc.contributor.author	Li, F
dc.contributor.author	Jiang, X
dc.contributor.author	Zhou, Q
dc.date	2019-07-09
dc.date.accessioned	2021-05-10T03:49:15Z
dc.date.available	2021-05-10T03:49:15Z
dc.date.issued	2020-01-01
dc.identifier.citation	Frontier Computing, 2020, 551, pp. 283-291
dc.identifier.isbn	9789811532498
dc.identifier.issn	1876-1100
dc.identifier.issn	1876-1119
dc.identifier.uri	http://hdl.handle.net/10453/148809
dc.description.abstract	General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GVM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GVM, BPNN, SVM and ARIMA are proposed and verified. Results show that GVM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Frontier Computing
dc.relation.ispartof	International Conference on Frontier Computing
dc.relation.ispartofseries	Lecture Notes in Electrical Engineering
dc.relation.isbasedon	10.1007/978-981-15-3250-4_33
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Combined General Vector Machine for Single Point Electricity Load Forecast
dc.type	Conference Proceeding
utslib.citation.volume	551
utslib.location.activity	Japan
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 Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-10T03:49:13Z
pubs.finish-date	2019-07-12
pubs.place-of-publication	Singapore
pubs.publication-status	Published
pubs.start-date	2019-07-09
pubs.volume	551
dc.location	Singapore

Abstract:

General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GVM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GVM, BPNN, SVM and ARIMA are proposed and verified. Results show that GVM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.

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http://hdl.handle.net/10453/148809