Garden communities: using ‘digital twins’ in your data strategy

Out-Law Guide | 08 Apr 2022 | 1:55 pm | 3 min. read

Creating a digital representation of a garden community as part of your data strategy can provide powerful new insights into how to make best use of and improve facilities and performance.

Digital ‘twins’ of assets such as homes, offices, energy grids, transport systems, parkland and other facilities can be used to model and analyse how they change over time and under various conditions, by combining machine learning and advanced analytics with an understanding of real-world conditions.

Data collected from the asset, together with contextual data, can be fed into 2D and 3D models and made accessible to many levels of user, allowing it to be managed, analysed, tracked and shared.

How do digital twins work?

Digital twins are effectively a combination of capabilities brought together on a shared platform, to be used for many different purposes and by a variety of users.

The concept of a digital twin evolved as more data sources and digital tools became available to those working with buildings and the built environment. Sensors in buildings can provide real-time, detailed data on assets and processes, which can be combined with contextual data on demographics, infrastructure, weather patterns, building design, social sentiment analysis and transport patterns. Machine learning and artificial intelligence (AI) capabilities can be used to analyse these vast data sources and their complex interdependencies to provide insights to decision makers.

AI can also be used to fill gaps in operational data, and to improve data quality. Dynamic simulation modelling tools that use the fundamental principles of physics, building information modelling (BIM), energy networks, infrastructure data and developments in computer-aided design (CAD) can be used to create models that accurately replicate real-world behaviour, and these can then be used to analyse different scenarios for improved system design and optimisation.

Digital ‘twins’ of assets such as homes, offices, energy grids, transport systems, parkland and other facilities can be used to model and analyse how they change over time and under various conditions

Visualisation tools like these improve the ability of decision makers to work with sophisticated datasets and advanced analytics tools, allowing them to understand the real-world implications of proposed changes across a complex network of assets and relationships. They also make new forms of stakeholder engagement possible, by providing the means for users at all levels to understand the impacts and potential of new approaches.

Potential uses of digital twins

A digital twin approach can be used to support the move towards a zero carbon community. A cross-sector digital representation of the community can be built, drawing on data from buildings, energy networks, planning, environmental systems, transport infrastructure and services and socioeconomic indicators. This model can then be used to simulate the impact of changes, anticipate ripple effects through different community assets and services and understand potential impacts on the community’s goals.

For more complex environments such as city centre districts, university campuses and transport hubs, digital twins can be used to combine multiple assets and model the relationships between them. The ability to run different scenarios and explore their impacts across the system as a whole enables new insights that enhance the ability to take a more holistic approach to issues such as building design, energy strategy and transportation planning. Cross-sector benefits of proposed changes can also be better understood – for example, providing community charging points for electric vehicles (EVs) may change demand profiles for energy, raise issues around grid reliability, open opportunities for vehicle-to-grid services and have implications for community transport planning and operation. Similarly, increased use of public transport or better bicycle or walking options could change the likely demand profile for street design, demand for public transport and cycling, and affect where EV charging is best targeted.

Simulating how the system as a whole will respond to change improves decision-making for a range of questions, such as the costs and benefits of different storage technologies, the impact of increased EV charging on local power networks and how changes in residents’ behaviour may alter demands on the system. Decision makers can look at costs and benefits across the whole system for planned initiatives such as district heating, local energy production, community energy storage and increased EV charging capacity. It also allows for ‘what if’ analysis of the future impact of changes to the system. The community can look at alternative scenarios for investing in building energy efficiency programmes, distributed energy resources such as solar PV or energy storage, and behavioural change programmes to prioritise activities and investments.

Digital twins also allow for the development of community-scale systems which can be monitored, systems modified and alternative paths chosen. Significant system changes that can have an impact on zero carbon goals, such as changes in user behaviour or socioeconomic trends, can be anticipated, and stakeholder engagement enhanced. Supporting multiple stakeholders across the community and increasing their engagement with new systems requires accurate but accessible means of visualising not only the current state of the facilities but also the impact of potential changes.