Development of a National Scale Digital Twin for Domestic Building Stock

The operation of buildings accounted for 40% of global energy consumption and 27% of greenhouse gas emissions (GHG) in 2022. Access to integrated information sources about a building stock is key to supporting policy and decision makers as they pursue green house gas reductions. However, over time, information has evolved into functional silos which accordingly limits the ability of experts in functional areas to exchange data and implement broader decision support systems. This paper describes the creation of a national scale digital twin for a national domestic building stock and is achieved through the use of semantic technologies to create a homogeneous knowledge graph from multiple heterogeneous data sources. The utility of the digital twin is demonstrated by the development of a virtual surveyor. This tool is used to predict building features such as window u-values for buildings that have not been surveyed as part of the national EPC scheme. In turn, these values are used to enrich the digital twin.