You could describe a Digital Twin as the enhancement of a static BIM model with the element of time, and for use at design stages to better plan and execute construction projects by anticipating issues on site before they happen. The ‘Twin’ could then be passed on to the client to assist with ongoing management and maintenance.

But the real opportunity for digital twins in the Built Environment is their ability to help us realise zero-carbon goals not only for individual buildings, but also for communities. The path to a decentralised, decarbonised and digitalised energy landscape requires a transition to low-carbon energy systems, integration of renewables, and storage solutions, and data analytics to anticipate demand and empower users.

In this vision, buildings become an active element within the energy landscape, consuming as well producing. And by linking a Digital Twin to the real building and the energy network though sensors we can create true Twins at any scale. Twins that bring an understanding of the physics that dictate real-world conditions (such as energy flows, environmental conditions, and material attributes), and Twins which will evolve over the asset’s lifetime using machine learning and AI applied to operational data to identify patterns in use, identify where the operation of the building, group of buildings or network does not match expectation and start to ask why? Such Digital Twins can also be used to plan changes and steps needed for climate mitigation and resilience strategies.

But where do you get started? A Digital Twin can be as simple or as complex as you need it to be. But, that doesn’t mean there are no barriers to adoption that we as an industry need to overcome.

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