Exist Insight ISSUE 06

City Science, Exeter University and Hoare Lea join forces to revolutionise transport planning and building energy management using Data Science and Artificial Intelligence

  • City Science, Exeter University and Hoare Lea join forces to revolutionise transport planning and building energy management using Data Science and Artificial Intelligence image 1

Added: 12 June 2018

City Science are leading a £1m project to deliver digital twin technology that will seamlessly manage complex operations around congested transport networks and building energy management. The project will be delivered in partnership with the University of Exeter and Hoare Lea.

Digital Twins can help organisations to de-risk product design, undertake complex tasks and simulate changes to an operation much more cheaply than modifying a real system. Digital Twins enable designers to understand the benefits and impacts of large-scale developments such as new buildings or major transport infrastructure. Digital Twins can also help organisations manage complex operations such as a production line or a fleet of aircraft in real-time.

The innovation project will apply Artificial Intelligence (AI) developed by the University of Exeter to modelling systems developed by City Science and Hoare Lea to achieve significant improvements to cost and accuracy of Digital Twins. The project will make Digital Twin technology more accessible and valuable to enterprises, businesses and the public sector.

This project has been co-funded by Innovate UK, the UK’s innovation agency. Innovate UK drives productivity and economic growth by supporting businesses to develop and realise the potential of new ideas. The project will also be supported by the Environmental Futures and Big Data Impact Lab (part-funded by the ERDF).

Three areas of application will be developed for the Digital Twin technology, building on existing work from City Science, Hoare Lea and the University of Exeter. These include building energy management and design; transport planning and control; and enterprise analytics.

Laurence Oakes-Ash, CEO of City Science said: “Digital Twin technology enables cities, companies and organisations to predict in detail what’s going to happen, before it happens. If you’re planning a new road or managing a complex production process, understanding the impacts, being able to avoid negative consequences reduces risk and can save significant amounts of money. Data from sensors allows us to pair a real-world asset or system to a detailed mathematical model making this type of prediction a reality.  This project will make Digital Twin systems more accurate and cost-effective bringing the power of this advanced analytics to a much wider audience.”

Digital Twin technology is essential to a range of emerging technologies, in particular BIM Level 3.

Andrew Bullmore, Partner at Hoare Lea said: “The application of this technology within the built environment will create exciting new efficiencies. Hoare Lea has been at the forefront of BIM development for almost a decade, supporting clients through the building design process in this powerful environment. The addition of Digital Twin technology into the workflow process will allow us to take the benefits of BIM much further, beyond the design phase of a project and into the operation of the building over its lifetime. This will help us provide greater value to customers, ensuring the ongoing performance of buildings as designed and managing down energy use and lifetime cost.”

A team from the University of Exeter will develop machine learning methods to automate the creation of the Digital Twin and allow it to more closely reflect reality.

Professor Richard Everson, Director of the Institute of Data Science and AI at the University of Exeter added: “Digital twins are a transformational way of using the power of AI and data science to model our society, its infrastructure and environment, allowing us to ask important “what if” questions.  This exciting project will allow us to take full advantage of digital twins by efficiently calibrating them to accurately reflect reality, so that we can better understand the energy balance in buildings and congested transport networks.”

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