Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems

Abstract

Intelligent building systems require a data-rich environment. Virtual sensors can provide informative and reliable sensing environments for operational datasets in building systems. In particular, backup virtual sensors that are in situ are beneficial for developing the counterparts of target physical sensors in the field, thus providing additional information about residuals between both types of sensors for use in data-driven modeling, analytics, and diagnostics. Therefore, to obtain virtual sensor potentials continuously during operation, we proposed an in situ calibration method for in situ backup virtual sensors (IBVS) in operational building energy systems, based on virtual in situ calibration (VIC). The proposed method was applied using operational datasets measured by a building automation system built into a target system. In a case study, the in situ virtual sensor showed large errors (the root mean squared error (RMSE) was 0.97 °C) on certain days. After conducting the proposed VIC, the RMSE of virtual sensor errors decreased by 22.7% and 18.7% from the perspective of sensor error types such as bias and random error, respectively, in the validation month. The subsequent virtual measurements could be considerably and effectively improved without retraining the specific in situ backup virtual sensor.