Ghaemi, Afrouz, Rezgui, Yacine  ORCID: https://orcid.org/0000-0002-5711-8400, Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247, Beach, Thomas ORCID: https://orcid.org/0000-0001-5610-8027 and Ghoroghi, Ali
2025.
AI and digital twin applications in building energy management: a state-of-the-art review.
Presented at: 2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC),
Valencia, Spain,
16-19 June 2025.
Proceedings of the International Conference on Engineering, Technology, and Innovation.
IEEE,
pp. 1-11.
10.1109/ice/itmc65658.2025.11106601
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Abstract
Purpose: This paper examines the integration of Artificial Intelligence (AI) and digital twin technologies in commercial and residential buildings, focusing on self-learning systems and intelligent energy management solutions. Methods: A comprehensive review synthesizes recent developments in AI applications, digital twin architectures, and building automation systems, with emphasis on autonomous control and occupant-centric optimization. Results: The analysis reveals promising trends in AI-enhanced digital twins for building systems, highlighting their capability to learn from occupant behavior, make data-driven decisions, optimize performance in real-time, and seamlessly integrate with other building technologies to create an efficient, comfortable environment. Conclusions: The findings demonstrate the transformative potential of AI-driven digital twins in modern buildings, offering practical guidance for implementing these technologies while prioritizing occupant comfort, energy efficiency, and sustainability through self-learning building systems.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Publisher: | IEEE |
| ISBN: | 9798331585358 |
| ISSN: | 2334-315X |
| Date of First Compliant Deposit: | 16 September 2025 |
| Date of Acceptance: | 26 May 2025 |
| Last Modified: | 19 Sep 2025 16:06 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/180509 |
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