Understanding crowd energy consumption behaviors

Liu, X; Cheng, X; Yang, Y; Huo, H; Liu, Y; Nielsen, PS

Understanding crowd energy consumption behaviors

Liu, X Cheng, X Yang, Y Huo, H

Liu, Y Nielsen, PS

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Publication Type:: Conference Proceeding
Citation:: Advances in Database Technology - EDBT, 2023, 26, (3), pp. 799-802
Issue Date:: 2023-03-20

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Field	Value	Language
dc.contributor.author	Liu, X
dc.contributor.author	Cheng, X
dc.contributor.author	Yang, Y
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.contributor.author	Liu, Y
dc.contributor.author	Nielsen, PS
dc.date.accessioned	2024-02-07T21:08:19Z
dc.date.available	2024-02-07T21:08:19Z
dc.date.issued	2023-03-20
dc.identifier.citation	Advances in Database Technology - EDBT, 2023, 26, (3), pp. 799-802
dc.identifier.issn	2367-2005
dc.identifier.uri	http://hdl.handle.net/10453/175446
dc.description.abstract	Understanding crowd behavior is crucial for energy demand-side management. In this paper, we employ the fluid dynamics concept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration.
dc.language	en
dc.relation.ispartof	Advances in Database Technology - EDBT
dc.relation.isbasedon	10.48786/edbt.2023.68
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Understanding crowd energy consumption behaviors
dc.type	Conference Proceeding
utslib.citation.volume	26
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 Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-07T21:08:18Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	3

Abstract:

Understanding crowd behavior is crucial for energy demand-side management. In this paper, we employ the fluid dynamics concept potential flow to model the energy demand shift patterns of the crowd in both temporal and spatial dimensions. To facilitate the use of the proposed method, we implement a visual analysis platform that allows users to interactively explore and interpret the shift patterns. The effectiveness of the proposed method will be evaluated through a hands-on experience with a real case study during the conference demonstration.

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http://hdl.handle.net/10453/175446