Elghaish, F;
Matarneh, S;
Hosseini, MR;
Tezel, A;
Mahamadu, AM;
Taghikhah, F;
(2024)
Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda.
Smart and Sustainable Built Environment
10.1108/SASBE-03-2024-0096.
(In press).
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Abstract
Purpose: Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases. Design/methodology/approach: This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio. Findings: Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction. Practical implications: The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing. Originality/value: This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference.
| Type: | Article |
|---|---|
| Title: | Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1108/SASBE-03-2024-0096 |
| Publisher version: | http://dx.doi.org/10.1108/sasbe-03-2024-0096 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Green & Sustainable Science & Technology, Science & Technology - Other Topics, Digital twins, AI for net zero, Machine learning, Decarbonisation pathways, Emission analytics, Digital ecosystem, Sustainable built environment, ENERGY MANAGEMENT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10196652 |
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