An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors

Lu, X; Qiu, J; Lei, G; Zhu, J

An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors

Lu, X Qiu, J Lei, G

Zhu, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Power Systems, 2023, PP, (99), pp. 1-11
Issue Date:: 2023-01-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Lu, X
dc.contributor.author	Qiu, J
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Zhu, J
dc.date.accessioned	2024-02-08T11:25:49Z
dc.date.available	2024-02-08T11:25:49Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Power Systems, 2023, PP, (99), pp. 1-11
dc.identifier.issn	0885-8950
dc.identifier.issn	1558-0679
dc.identifier.uri	http://hdl.handle.net/10453/175499
dc.description.abstract	Accurate prediction of electricity prices under uncertainties is an important and challenging problem for all electricity market participants. This paper proposes a novel generative model-based prediction interval construction method for day-ahead electricity prices. A conditional time series generative adversarial network is proposed to generate realistic and diverse electricity price scenarios. With these generated price scenarios, prediction intervals can be combined. After that, a threshold select machine is proposed to truncate the threshold of random noise input to adjust the quality of prediction intervals, balancing the reliability and sharpness. Finally, shortwave irradiance, wind speed, and temperature are taken into account in the threshold select machine, further improving the reliability and the sharpness of the prediction intervals. Case studies verify the effectiveness and superiority of the proposed method.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Power Systems
dc.relation.isbasedon	10.1109/TPWRS.2023.3301442
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.title	An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
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 Electrical and Data Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-08-03T00:00:00+1000Z
dc.date.updated	2024-02-08T11:25:47Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Accurate prediction of electricity prices under uncertainties is an important and challenging problem for all electricity market participants. This paper proposes a novel generative model-based prediction interval construction method for day-ahead electricity prices. A conditional time series generative adversarial network is proposed to generate realistic and diverse electricity price scenarios. With these generated price scenarios, prediction intervals can be combined. After that, a threshold select machine is proposed to truncate the threshold of random noise input to adjust the quality of prediction intervals, balancing the reliability and sharpness. Finally, shortwave irradiance, wind speed, and temperature are taken into account in the threshold select machine, further improving the reliability and the sharpness of the prediction intervals. Case studies verify the effectiveness and superiority of the proposed method.

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

http://hdl.handle.net/10453/175499