In-situ observation virtual sensor in building systems toward virtual sensing-enabled digital twins

Sensing in building operations is essential to realize intelligent buildings and digital twin-enabled oper-ations. However, buildings have a limited sensing environment with sensor absences, faulty sensors, low redundancy, and less dense deployment because of the inherent building characteristics (e.g., massive, heterogeneous, and long-term). It impedes the advanced building operations. To tackle these issues, this study proposes a novel sensing method for in-situ observation virtual sensors (OVS) in building opera-tions. The proposed OVS is intended to predict the unmeasured variables in real time, without the target sensors. To do so, the OVS is modeled in-situ and then calibrated indirectly without the target observa-tion (Y) so that the virtual sensing can be effectively applied in the building sector, where it is very dif-ficult to establish a laboratory environment having Y for the OVS modeling. The OVS can be developed and operated in the building digital twins, thus extending the physical sensing coverages for digital twin-enabled intelligent operations. For real application, the proposed OVS was developed to observe the return water temperature of a real district heating system. The OVS was experimentally validated with the real measurement to discuss the in-situ OVS model and calibration performance. The OVS could be successfully modeled without using the target observation (Y), and indirect calibration improved the initial OVS performance by 32 %. The OVS demonstrates a significant performance with a root mean square error of 0.55 degrees C.(c) 2023 Elsevier B.V. All rights reserved.