October 15th 2020

Digital Twins in the Built Environment – Vision & Challenges

Digital Twins in the Built Environment – Vision & Challenges

You’ve probably by now already heard of Digital Twins – the virtual representation of a physical object or system across its life-cycle. It uses real-time data and other sources to enable learning, and reasoning: dynamically recalibrating for improved decision-making.*

This is a standard definition, but let’s dig a bit deeper. Within the built environment: what does that mean? How can you create one? Who should use it? What are the challenges? And what benefits do they deliver? You also might be wondering how Digital Twins are different from BIM or BMS systems. I’m going to cover all of this in a series of blogs.

At IES, we see Digital Twins as a key tool in helping the built environment decarbonize. This might come as no surprise, given that our vision is better buildings: smarter cities and our mission is to improve every building of every city to secure a healthy, sustainable and resource-efficient future.

When you look at how to decarbonise the built environment, looking at addressing individual building energy efficiency in isolation is part of the solution, but what we see is that buildings are fast becoming active elements rather than passive consumers in a complex network of energy, water, waste, and transport services. 

This means that in order to drastically reduce our impact on climate and reach zero-carbon targets we need to be able to model and understand these complex interactions at both the building and community scale. Currently, there is lots of data but no real information. Digital Twins enable the new forms of modelling, analytics and engagement required to help address such challenges.

IDC predicts in their report “FutureScape Worldwide Smart Cities 2020”, that by 2023 25% of smart cities will use digital twin platforms to automate processes for increasingly complex interconnected ecosystems of assets and products.

However, that doesn’t mean the journey will be an easy and straightforward one. When it comes to Digital Twin’s, it’s smart manufacturing and Industry 4.0 that have been driving the way. But the Built Environment, though currently going through its own massive digitisation, throws up very specific challenges to creating Digital Twins.

The main issue is uncertainty: buildings are used and inhabited by humans, things change daily and every building to all intent and proposes is a one-off ‘prototype’. 

Then you have the plethora of legacy systems and control technology installed in buildings. Not only do you have multiple systems, but they tend to be set up differently on different buildings, by different people, at different times with different data naming conventions and interoperability. This causes real issues when you try to gather and make sense of data from different systems and buildings. Which, when the use of real-time data is one of the fundamental building blocks of a Digital Twin is a major hurdle to overcome.

IoT sensors are helping cut through this, by collecting and communicating data within a much-simplified architecture. And you can also use AI and Machine Learning to automate and address some of these issues in data collection. 

Which brings me on to the physics-based thermal simulation element. Often overlooked in Digital Twin conversations linked to the built environment, it’s actually utilised extensively within manufacturing Digital Twins. It means that not only can you use real-time data taken via sensors from the asset in real-life, but that you can also start to make use of data simulated by virtual or mirror sensors within the Digital Twin model itself.

Now, this is both an opportunity and a challenge with the construction industry. It’s had access to performance-based modelling tools for more than a decade now, but this physics-based simulation is traditionally done only at design for regulation and code compliance, it’s not fully integrated into the building lifecycle, nor has it been developed to enable the multi-year modelling needed to plan across a building or community lifecycle.

If Digital Twins in the Built Environment are going to be an essential tool in addressing the current climate crisis, achieving zero carbon targets and promoting sustainable, healthier buildings. Then the live digital twin of a physical building needs to respond and behave like its real world counterpart, connecting groups of buildings together with energy and transport systems. It is only with the addition of the physics-based simulation aspect that this can be truly achieved.

Then a Digital Twin can function as a problem-solver and provide the decision support information needed to improve performance, influence future design and reduce risk.

For the last 26 years’ IES technology has helped make almost one million buildings more energy efficient. However, one building at a time is too slow. To decarbonise the built environment rapidly we recognised the need to address operational performance at the community level.

Read more about our latest technology innovation, the Intelligent Communities Lifecycle Digital Twin here

* Source IBM