The World Avatar added to WEF’s list of Global Use Cases

The World Avatar project has been selected for the World Economic Forum (WEF)’s list of Global Use Cases as part of its Global Digital Twin Cities initiative.

The initiative offers a potential solution for policymakers worldwide to improve city governance and create a conducive urban ecosystem for industries and people. It is a collaboration between the WEF’s Urban Transformation Platform, The China Academy of Information and Communication Technology (CAICT), and other key stakeholders.

At the beginning of 2021, the WEF and the CAICT launched the three-year initiative to make digital twin technology accessible to the public, promote social equity and inclusiveness, help cities achieve digital and low-carbon transformation, and jointly shape the future of digital twin city development. Ultimately, WEF hopes to render cities more inclusive, sustainable, and liveable through these efforts.

The World Avatar and the WEF

The World Avatar (TWA) project perfectly fits as an example for this WEF initiative. TWA represents the digital world using a dynamic knowledge graph containing concepts and data describing the world. TWA simulates the world’s behaviour with an ecosystem of autonomous computational agents that update the concepts and data so that the digital world remains current in time.

In this manner, it is possible to create digital twins that can describe the behaviour of any complex system and can make data-driven decisions about how to optimise the systems. TWA can improve interoperability across all sectors by providing a solution that enables the integration of distributed heterogeneous models and data. By including a description of the computational agents within the TWA knowledge graph, we can confer the discoverability of the computational capabilities and easily combine heterogeneous pieces of software and data.

Uses of The World Avatar

TWA contains a description of the UK’s electrical power and gas transmission systems. This gives geospatial details of infrastructure and captures relationships between different infrastructure elements and live data feeds describing gas flow into the transmission system. TWA can leverage disparate information from gas transmission systems, gas consumption data, and climate observations to investigate the hypothetical outcome of switching from gas heating to heat pumps.

Using TWA, it was discovered that while a complete switch to heat pumps would reduce the UK’s total carbon dioxide emissions by approximately 10%, it would exacerbate fuel poverty in colder parts of the country. In this way, TWA allows the UK government to examine specific scenarios in enough detail to support local policymaking.

TWA can also contain descriptions of the geometry and features of buildings, including details of individual apartments and the contents of individual rooms. Once the buildings are described in TWA, they can be processed by computational agents acting on the knowledge graph.

For example, TWA has a representation of the CARES lab in Singapore. Data about the building, the laboratory layout, and the locations of sensors in the laboratory are all represented as part of TWA’s knowledge graph. Agents then use real-time data about the ambient humidity and outdoor air temperature to achieve a specified temperature in the lab, and flammable chemicals can be monitored by incorporating sensors into the safety cabinet in which they are stored.

Another fitting application of TWA is the Climate Resilience Demonstrator (CReDo), developed by CARES’ collaborator CMCL Innovations as part of the UK’s National Digital Twin programme. CMCL applied ideas from TWA to create a digital twin that integrated data describing assets from the water, energy, and telecom industries. Data from flood simulations were then combined with asset data to model the potential cascade of failures through the combined asset network in future flood scenarios for cities.

TWA is much more than just these examples. With its dynamic knowledge graph technology and ecosystem of autonomous computational agents, it can create digital twins that can describe the behaviour of any complex system and can make data-driven decisions about how to optimise the systems. WEF’s Global Digital Twin Cities initiative has recognised the value of this approach, and by adding CARES and TWA to its Global Use Cases, it stands as an example of how to reach true interoperability between diverse sectors.