Abstract
Systematic and random errors for working sensors in building systems can seriously affect the operation performance and have a significant negative impact on energy efficiency and indoor comfort. However, the traditional method for sensor calibration is very time-consuming and labor-intensive. Therefore, a virtual in-situ calibration (VIC) method was proposed based on Bayesian inference that can simultaneously correct systematic errors for multiple working and greatly reduce random errors. Using an actual system with reheating of the primary return air, we introduced the principle of the VIC method and its application. The robustness and applicability of the method were verified under six normal and four extreme operating conditions. The effects of sensor system errors on the energy consumption of the building system and on indoor thermal comfort were also studied. The results showed that under various working conditions, systematic and random errors for all the working sensors in the system were accurately identified. On average, the systematic and random error after calibration were reduced by approximately 95% and 60%, respectively. Compared to operating conditions with systematic errors, the average energy consumption after VIC was reduced by more than 50%, and predicted mean vote values after calibration were within the comfort zone.
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