Digital twins for smart asset management in the energy industry: State-of-the-art

Amin, U; Kim, D; Ahmed, FN; Ahmad, G; Hossain, MJ

Digital twins for smart asset management in the energy industry: State-of-the-art

Amin, U Kim, D Ahmed, FN Ahmad, G Hossain, MJ

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2025, 289
Issue Date:: 2025-09-15

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Field	Value	Language
dc.contributor.author	Amin, U
dc.contributor.author	Kim, D
dc.contributor.author	Ahmed, FN
dc.contributor.author	Ahmad, G
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2026-02-18T03:50:44Z
dc.date.available	2026-02-18T03:50:44Z
dc.date.issued	2025-09-15
dc.identifier.citation	Expert Systems with Applications, 2025, 289
dc.identifier.issn	0957-4174
dc.identifier.issn	1873-6793
dc.identifier.uri	http://hdl.handle.net/10453/193552
dc.description.abstract	With the most recent developments in the Internet of Things (IoT), Machine Learning, and Big Data, digital twins (DTs) are gaining popularity across several sectors. Since digital twin (DT) could be viewed as a cyber-physical system, many DT applications have been successfully implemented for the Industrial Internet of Things (IIoT). Following DT's success with Industry 4.0, DT is increasingly receiving the attention of academia and industry for smart asset management (SAM) in Energy 4.0. Nevertheless, there has been a notable absence of research papers specifically dedicated to reviewing the applications of DTs in the context of the SAM domain within the Energy 4.0 paradigm. Hence, this paper addresses this gap by thoroughly examining the latest advancements in DT research on SAM in Energy 4.0. It comprehensively explores the fundamental aspects of DTs, provides insights into their current progress within the Energy 4.0 framework, and elucidates the various applications of DTs within the SAM domain. Furthermore, the paper highlights the existing challenges and presents potential directions for future research endeavors in this field. This review will assist industry experts in implementing DT in SAM applications and provide new research directions for researchers.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2025.128358
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Digital twins for smart asset management in the energy industry: State-of-the-art
dc.type	Journal Article
utslib.citation.volume	289
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-18T03:50:40Z
pubs.publication-status	Published
pubs.volume	289

Abstract:

With the most recent developments in the Internet of Things (IoT), Machine Learning, and Big Data, digital twins (DTs) are gaining popularity across several sectors. Since digital twin (DT) could be viewed as a cyber-physical system, many DT applications have been successfully implemented for the Industrial Internet of Things (IIoT). Following DT's success with Industry 4.0, DT is increasingly receiving the attention of academia and industry for smart asset management (SAM) in Energy 4.0. Nevertheless, there has been a notable absence of research papers specifically dedicated to reviewing the applications of DTs in the context of the SAM domain within the Energy 4.0 paradigm. Hence, this paper addresses this gap by thoroughly examining the latest advancements in DT research on SAM in Energy 4.0. It comprehensively explores the fundamental aspects of DTs, provides insights into their current progress within the Energy 4.0 framework, and elucidates the various applications of DTs within the SAM domain. Furthermore, the paper highlights the existing challenges and presents potential directions for future research endeavors in this field. This review will assist industry experts in implementing DT in SAM applications and provide new research directions for researchers.

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http://hdl.handle.net/10453/193552