An improved sensor fault in-situ calibration strategy for building HVAC systems with forgetting-adaptive mechanism based on data incremental learning

Sensor faults, which are primarily caused by environmental changes, calibration deficiencies, and component aging, critically compromise energy efficiency and operational reliability for building heating, ventilation and air-conditioning (HVAC) systems. Although conventional data-driven sensor fault calibration methods showed theoretical precision with low variable dependency, their practical implementation still faces challenges: difficulties in maintaining high accuracy and stability during model updates and HVAC system operation varies, insufficient data quantity and quality for effective modeling. To address these challenges, this study proposed a forgetting-adaptive (FA) mechanism based on data incremental learning (DIL), and develops a data selection method by autoencoder (AE) reconstruction to enhance Bayesian inference (BI) calibration models. FA selectively forgets and discards low-contribution data samples via AE reconstruction distance analysis while adaptively integrating high-contribution newly incremental data. Validations were conducted on two case studies: an EnergyPlus-Python simulated Chiller-AHU system and a practical water-cooled chiller system. It was revealed that FA reduced sensor calibration mean absolute error by 20.21% on average compared to the traditional MLR-BI. The impacts of modeling data volume on calibration performance were also explored, FA can maintain calibration accuracy with relatively limited data volumes. Also, this study tried to interpret the FA mechanism in BI model improvement by assessing the modeling data quality using the AE based reconstruction distances and adaptively selecting the high-contribution data via the AEThreshold.