The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system’s historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.

A hybrid cooling system which combines natural ventilation with a radiant cooling system for a hot and humid climate was studied. Indirect evaporative cooling was used to produce chilled water at temperatures slightly higher than the dew point. With this hybrid system, the condensation issue on the panel surface of a chilled ceiling was overcome. A computational fluid dynamics (CFD) model was employed to determine the cooling load and the parameters required for thermal comfort analysis for this hybrid system in an office-sized, well-insulated test room. Upon closer investigation, it was found that the thermal comfort by the hybrid system was acceptable only in limited outdoor conditions. Therefore, the hybrid system with a secondary fresh air supply system was suggested. Furthermore, the energy consumptions of conventional all-air, radiant cooling, and hybrid systems including the secondary air supply system were compared under similar thermal comfort conditions. The predicted results indicated that the hybrid system saves up to 77% and 61% of primary energy when compared with all-air and radiant cooling systems, respectively, while maintaining similar thermal comfort.