Abstract Faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings
result in significant energy waste in building operation. With fast-growing sensing data …

Fueled by big data, powerful and affordable computing resources, and advanced algorithms,
machine learning has been explored and applied to buildings research for the past decades …

Buildings have a significant impact on global sustainability. During the past decades, a wide
variety of studies have been conducted throughout the building lifecycle for improving the …

Implementing machine-learning models in real applications is crucial to achieving intelligent
building control and high energy efficiency. Over the past few decades, numerous studies …

Abstract Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller
systems can lead to discomfort for the users, energy wastage, system unreliability and …

Faulty operation of HVAC chillers may result in increased energy usage, reduced thermal
comfort, and increased maintenance costs. Establishing reliable fault detection and …

This article reviews the current research on the development and implementation of
automated fault detection and diagnostics (AFDD) technology for building systems. This …

Early detection and diagnosis of the chiller sensor drift fault are crucial to maintain normal
operation for energy saving. Due to the complex physical structure and operation conditions …

Fault detection and diagnosis (FDD) of centrifugal chillers plays an essential role in reducing
energy consumption and ensuring safe operation of heating, ventilation and air conditioning …

Fault diagnosis is crucial for energy conversation in building energy systems. There are
three different types of fault diagnosis methods: residual-, knowledge-, and data-driven …