Digital Twin and Building Performance: A Review and Proposed Framework

Elham Mahamedi, Martin Wonders, Mohamad Kassem, Wai Lok Woo, David Greenwood

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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The use of Digital Twin (DT) as an emerging technology-led development encompasses data-driven methods which bring the benefit of enhancing better understanding of building performance and providing relevant information for decision making. Extensive reviews intersecting the DT concept with building performance are lacking. The aim of this paper is to present such a review and propose a framework for using DTs to develop predictive models to improve the performance of buildings. The review analyses recent studies on energy prediction performance and fault detection in building maintenance using data-driven models, and further identifies the remaining gaps in the literature. The framework incorporates artificial intelligence (AI), machine-learning (ML), and cloud computing technology in a scalable prototype solution to efficiently capture, process, and integrate real-time building data in a timely manner. The framework is expected to help decision-makers gain valuable insights into the building performance, which will then inform interventions for improving the energy efficiency.
Original languageEnglish
Title of host publicationProceedings of the 38th Annual ARCOM Conference, 5-7 September 2022, Glasgow, UK
EditorsApollo Tutesigensi, Christopher J. Neilson
Place of PublicationLondon
PublisherAssociation of Researchers in Construction Management (ARCOM)
Number of pages10
ISBN (Electronic)9780995546363
Publication statusPublished - 5 Sept 2022
EventThe 38th Annual ARCOM Conference 2022: Build Back Wiser - Glasgow Caledonian University, Glasgow, United Kingdom
Duration: 5 Sept 20227 Sept 2022


ConferenceThe 38th Annual ARCOM Conference 2022: Build Back Wiser
Abbreviated titleARCOM 2022
Country/TerritoryUnited Kingdom
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