Effects of various partitions on the accuracy of virtual in-situ calibration in building energy systems

Abstract Sensor errors significantly affect the operation and control of building energy systems. Thus, using correct and reliable sensors can effectively reduce the energy consumption of building energy systems. Virtual in-situ calibration (VIC), which is based on Bayesian inference and the Markov Chain Monte Carlo method, can help avoid the installation of new sensors, effectively reduce the systematic and random errors of sensors, and improve the reliability of collected data. In the current study, six working conditions were designed to check the robustness and accuracy of the proposed VIC technology. It's shown that for the systematic and random error of various sensors, the improved component calibration method is the most accurate. Contrarily, the whole calibration method is the least accurate, whereas the performance of the three local calibrations is intermediate. The maximum errors of the whole, local, and component calibration methods were 973%, 112.7%, and 30%, respectively. This verifies that component calibration effectively reduces the possibility of abnormal data and also enhances the reliability of sensor measurements. For the whole and local calibration methods, the constraints between various parameters are significantly reduced because of the random and uncertain coefficients; consequently, a majority of the correction results deviate from their true values. It is not necessary to determine the sensitivity and normalizing coefficients through a complicated optimization algorithm or historical experience by using the proposed component calibration. Therefore, the component calibration method features advantages in terms of computational time and correction accuracy.