Abstract
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 language | English |
---|---|
Title of host publication | Proceedings of the 38th Annual ARCOM Conference, 5-7 September 2022, Glasgow, UK |
Editors | Apollo Tutesigensi, Christopher J. Neilson |
Place of Publication | London |
Publisher | Association of Researchers in Construction Management (ARCOM) |
Pages | 681-690 |
Number of pages | 10 |
ISBN (Electronic) | 9780995546363 |
Publication status | Published - 5 Sept 2022 |
Event | The 38th Annual ARCOM Conference 2022: Build Back Wiser - Glasgow Caledonian University, Glasgow, United Kingdom Duration: 5 Sept 2022 → 7 Sept 2022 https://www.arcom.ac.uk/-docs/conf/ARCOM-2022_Call_for_papers.pdf |
Conference
Conference | The 38th Annual ARCOM Conference 2022: Build Back Wiser |
---|---|
Abbreviated title | ARCOM 2022 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 5/09/22 → 7/09/22 |
Internet address |