Demo: BuildTwin: Towards Real-Time High-Fidelity Digital Twin for Smart Building Management

Although desirable, achieving a high-fidelity digital twin of buildings often requires a substantial influx of real-time data, demanding a dense network of environmental sensors. Regrettably, factors such as high hardware expenses, deployment constraints, and sensor malfunctions can impede the realization of such digital twins. In this demonstration, we introduce a pioneering approach that harnesses data-driven virtual sensing within a real-time, high-fidelity 3D digital twin of a building environment. Our innovative method provides accurate machine learning-based inference of real-time sensor variables across both two-dimensional (varying rooms) and three-dimensional (diverse elevations) domains, with limited reliance on physical sensor inputs. Thus, by extending the sensing coverage through the use of ML-driven virtual sensing, we are able to create a more accurate digital twin. Our initial results indicate a mean absolute percentage error of <1 % for temperature and around 10% for air quality parameters such as CO
2
, when compared against the ground truth physical sensors.