Data-Driven Smart Sensing and Real-Time High-Fidelity Digital Twin for Building Management

Achieving a high-fidelity digital twin of buildings is desirable but often requires a substantial influx of real-time data, necessitating a dense network of environmental sensors. Additionally, high hardware expenses, deployment costs, constraints, and sensor malfunctions can impede the realization of such digital twins. In this paper, we introduce TwinSense, a pioneering system that harnesses data-driven virtual sensing within a real-time high-fidelity 3D digital twin of the building environment. Our innovative method utilizes machine learning-based inference to accurately estimate real-time sensor variables, such as temperature and CO2 levels, across both two dimensional (varying rooms) and three-dimensional (diverse elevations) domains, with limited reliance on physical sensors. By extending sensing coverage through ML driven virtual smart sensing, we create a more accurate digital twin for advanced building management. Our case study results across different seasons indicate an average mean absolute percentage error of 2% for temperature and approximately 5% for air quality parameters, such as CO2, when compared against ground truth physical sensors deployed in the multi-room segmented building. Furthermore, we propose a modified 3D-Inverse Distance Weighting (IDW) thermal interpolation method. Leveraging the comprehensive multi-camera-angle visualization capabilities facilitated by the Unreal Engine, our analysis revealed a potential anomaly within the air conditioning system. The building management team validated this observation during the real-world trial, affirming the initial efficacy of our solution.