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In building Heating, Ventilation and Air Conditioning (HVAC) systems, sensor healthy operation is the foundation of the adopted control strategies to improve building energy efficiency and indoor thermal comfort. For large and complex HVAC systems where a large number of sensors are often installed, associated sensor fault detection is highly challenging due to the high dimensionality of the sensor data and complex multiple-fault scenarios. To address this challenging issue, this study proposes a novel method in which the thermodynamic laws (i.e., mass balance and energy conservation) are integrated with deep learning. By making use of the intelligence, flexibility, and efficiency of deep learning, the proposed method can easily handle high-dimensional sensor measurements. More importantly, the integration enables the thermodynamic laws (which govern the mass and heat transfer processes in HVAC systems) to be explicitly learned and thus can effectively reduce/eliminate unreasonable res

Air conditioningData drivenFault detectionPhysics informed deep learningEnsor fault