Hidden factors and handling strategies on virtual in-situ sensor calibration in building energy systems: Prior information and cancellation effect
Abstract Sensor errors greatly affect the performance of control, diagnosis, and optimization systems within building energy systems, negatively impacting energy efficiency. Virtual in-situ sensor calibration (VIC), a Bayesian theory based method, can improve building energy performance by calibrating erroneous sensors in working building energy systems on a large scale. Working sensors do not need to be removed nor will reference sensors need to be added, as is done in a conventional calibration. To improve the calibration accuracy, hidden factors and their negative effects on the accuracy of a VIC must be addressed properly. In this study, we define (1) prior information and (2) cancellation effects as the negative effects. The suggested VIC method is applied to a single energy system component and to a LiBr-H 2 O absorption refrigeration system, respectively, to discuss the two primary effects (mentioned above). In addition to adding data sets, two strategies—inclusion of local calibration and conducting repetitive prior updates—are proposed to solve the hidden factors’ issue. The case study (1) shows that the proposed local calibration with the prior updates can solve the two negative effects, thus suggesting the high calibration accuracy and (2) demonstrates that the calibrated measurements improve the accuracy of energy performance analysis for a building energy system (up to 17.82%). Highlights VIC can be applied in building automation systems to calibrate erroneous measurements. We investigate the hidden factors that could reduce the accuracy of a VIC. Prior information and cancellation effect are explained and illustrated with examples. Inclusion of local calibration and applying a prior update are proposed to solve the challenges. Case studies show a system performance analysis can be improved with the calibrated data.
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