Ghoroghi, Ali, Petri, Ioan  ORCID: https://orcid.org/0000-0002-1625-8247, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400, Hodorog, Andrei ORCID: https://orcid.org/0000-0002-4701-5643, Fadli, Fodil and Elnour, Mariam
2025.
Artificial intelligence-augmented digital twins for energy management and comfort optimization in buildings.
IEEE Transactions on Industrial Informatics
10.1109/tii.2025.3641438
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Abstract
This research demonstrates the benefits of integrating digital twin (DT) technology with advanced artificial intelligence techniques for energy management and occupant comfort optimization in large-scale sports facilities. By implementing a high-fidelity DT framework at the Qatar University Sports and Events Complex, our approach achieves a 14.52% reduction in heating, ventilation, and air conditioning power consumption and an 85.03% improvement in thermal comfort, resulting in substantial cost savings and a notable reduction in carbon emissions. We unify an EnergyPlus-driven DT, artificial neural network surrogates, and multiobjective optimization (Nondominated sorting genetic algorithms II/III, Unified NSGA-III, and S-metric selection evolutionary multiobjective optimization algorithm) and validate the framework on a live facility, demonstrating real-world deployability with existing building management system infrastructures.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Additional Information: | License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/legalcode, Start Date: 2025-01-01 |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1551-3203 |
| Date of Acceptance: | 28 November 2025 |
| Last Modified: | 07 Jan 2026 11:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/183653 |
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