In-situ observation and calibration in building digitalization: Comparison of intrusive and nonintrusive approaches

Abstract

Under digitalization-driven carbon neutrality in the building energy sector, in-situ virtual sensors could play a role in informative sensing environments and information modeling in building operations. This study proposes methodologies for the in-situ observation of virtual sensors and their in-situ calibration in building operations and digitalization. In the context of the building life cycle, in-situ observation virtual sensors are developed based on building related physics principles and design information, without physical observations, and then they are calibrated using two calibration approaches: (1) nonintrusive-indirect and (2) intrusive-direct calibrations. A comparative study is conducted to investigate the effectiveness of physics-based in-situ virtual sensors in real operation, compare the two calibration performances, and suggest recommendations for each calibration approach for real applications. According to a field study in a target district substation serving residential buildings, the in-situ observation virtual sensor for the demand-side return water temperature showed a root mean squared error (RMSE) of 0.81 °C before calibration in the heating season (112 days). The RMSEs of 0.61 and 0.55 °C were found for the nonintrusive-indirect and intrusive-direct calibrations in the representative case, respectively. These results showed the effectiveness of nonintrusive-indirect calibration, even though the target observations were unknown in the calibration. This study recommends obtaining informative datasets for improved intrusive calibration. In the case study, calibration with the informative dataset for 1 day (RMSE of 0.59 °C) was superior to that of the average RMSE for nonintrusive calibration. The current energy patterns could be a basis for deciding whether the next few days are suitable for obtaining informative intrusive datasets, resulting in better calibration accuracy or shortening of the required datasets.