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Digital twins are becoming increasingly popular in the built environment, with building users asking for greater levels of data and digitisation, and the volume of research in the field growing. There remains a question as to whether the costs of digital twins are worth the benefits they provide, both in terms of the financial cost and the environmental cost of transferring, storing, analysing, and processing a consistently growing amount of data to feed digital twins. This paper provides a literature review of studies that assess the costs and benefits of digital twins. It considers the value of digital twins and how this value is generated. This review includes an analysis of the different definitions for digital twins given in the literature, the countries of authorship, and categories of sources within the relevant literature. The analysis of digital twin definitions showed that the use of the term varied depending on the application being considered. An assessment of the data harvested and associated benefits showed that environmental conditions, especially temperature, and occupancy data were the most harvested data types in the studies considered. It was found that there is potential for digital twins to provide benefits beyond their costs if used in the correct application. However, the studies relied on simulations to quantify benefits and did not quantify the costs of live data streams in digital twins, which highlights a research gap. For the studies analysed, implementations focusing on occupancy data to provide demand-led services, machine learning to optimise equipment setpoints, and data for predictive maintenance show the greatest potential for cost and energy savings.
Markiewicz et al. (Tue,) studied this question.