Abnormal operation of HVAC systems can result in an increase in energy usage as well as
poor indoor air quality, thermal discomfort, and low productivity. Building automated systems …

Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of
building energy systems. This paper aims at making a comprehensive literature review of …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale datasets. On the other hand, data mining applications with high …

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 …

With the advent of the era of big data, buildings have become not only energy-intensive but
also data-intensive. Data mining technologies have been widely utilized to release the …

The feature selection is an essential data preprocessing stage in data mining. The core
principle of feature selection seems to be to pick a subset of possible features by excluding …

This review study examines the latest research and developments in the fault detection and
diagnostics of Heating Ventilation and Air Conditioning (HVAC) systems. This review …

Data-driven fault detection and diagnosis (FDD) in building energy systems is typically
limited by the quantity and quality of training data. These methods can be only used for …

Because of high detection accuracy, deep learning algorithms have recently become the
focus of increased attention for fault detection diagnostic (FDD) analysis of heat, ventilation …