Recent studies show that artificial intelligence (AI), such as machine learning and deep
learning, models can be adopted and have advantages in fault detection and diagnosis for …

With the rapid development of the artificial intelligence (AI) technology, its application in
optimizing heating, ventilation and air-conditioning (HVAC) systems operation is becoming …

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” …

The equipment of chiller systems is characterized by a complex mechanical structure and
operating environments that vary widely, resulting in high failure rates, energy waste, and …

The proliferation of Internet of Things (IoT) sensors in smart buildings has generated vast
amounts of time-series data, offering valuable insights when properly leveraged. We …

Variable-speed packaged rooftop units have been increasingly adopted in the commercial
sector in the last decade. Several studies, mainly focused on constant-speed rooftop units …

Incomplete data is the most common but tricky problem for data-driven energy and building
solutions. Due to sensor errors or communication failures, raw building data with missing …

Accurate fault diagnosis of chillers is essential for extending equipment lifespan and
reducing energy consumption. Currently, data-driven diagnostic models for chillers exhibit …

Rapid and accurate detection of anomalies is critical to safety and profitability in the
industrial process. In recent years, most data-driven unsupervised anomaly detection …

Timely fault detection in HVAC systems is crucial for preventing energy waste and
maintaining thermal comfort in commercial buildings. This paper introduces the adaptive …