Strategies for sensor virtual in-situ calibration in building energy system: Sensor evaluation and data-driven based methods

Abstract

Sensor errors greatly affect the optimal control, safe operation, and energy efficiency of the building energy system. The sensor virtual in-situ calibration (VIC) method based on Bayesian inference can calibrate the sensor errors and does not need to remove existing sensors or add redundant sensors. In the previous study, the calibration of all sensors (non-differential calibration) and the physical model-driven based methods are applied in the simulation system. However, in the practical building energy system, these reduce calibration accuracy and efficiency. To solve these two negative factors, two calibration strategies are proposed: (1) sensor evaluation, and (2) data-driven based methods. Four cases are designed to demonstrate calibration accuracy and efficiency improvement by applying the calibration strategies in the variable air volume system. The results show that non-differential calibration causes difficulties in practical applications because of its low accuracy (38.10 %) and long calibration time (5136.35 s). The calibration accuracy of the physical model-driven based methods is 9.06 %, which completely deviates from the true value. The two calibration strategies are applied to improve the sensor calibration accuracy (up to 91.88 %) and reduce the calibration time (28.99 %) although it will increase some replacement costs. Meantime, the calibration accuracy of the temperature sensor is further improved (exceeding 94 %) by dividing the operating conditions according to the compressor power. In summary, the application of the calibration strategies can effectively overcome the negative impacts on calibration accuracy and efficiency, and increase the robustness of the VIC method in the practical building energy system.