Building sector account for significant global energy consumption and Heating Ventilation
and Air Conditioning (HVAC) systems contribute to the highest portion of building energy …

Heating, ventilation and air-conditioning (HVAC) accounts for around 40% of the total
building energy consumption. It has therefore become a major target for reductions, in terms …

Timely and accurate fault diagnosis (FD) in building energy systems (BESs) can promote
energy efficiency and sustainable development. Especially the heating, ventilating, and air …

Convolutional neural networks (CNNs) have been widely utili sed for fault diagnosis (FD) in
building heating, ventilation, and air conditioning (HVAC) systems. Despite achieving high …

The heating, ventilation and air-conditioning fault impacts vary with different seasonal
climatic conditions, but the fault data may not be available under some seasons in real …

Existing data-driven HVAC fault diagnosis methods mainly adopt supervised learning
paradigms, making them less feasible/implementable for individual buildings with limited …

Although deep learning models have been rapidly developed, their practical applications
still lag behind for building energy systems (BESs) fault diagnosis. Owing to the “black-box” …

Data-driven methods have drawn increasing interests in HVAC fault diagnosis tasks due to
their intrinsic advantages in making real-time automated decisions. To ensure the reliability …

The cross-domain fault diagnosis (CDFD) method can provide accurate fault diagnosis
models for HVAC systems in the case of incomplete labeled data. However, the relationship …

The failure of air conditioning systems is random and uncertain, with one or more faults
occurring simultaneously at any given time. Factors such as difficulty in collecting fault data …