With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

Building energy use is expected to grow by more than 40% in the next 20 years. Electricity
remains the largest energy source consumed by buildings, and that demand is growing. To …

Deep anomaly detection (DAD) is essential in optimizing building energy management.
Nonetheless, most existing works concerning this field consider unsupervised learning and …

Buildings are one of the main consumers of energy in cities, which is why a lot of research
has been generated around this problem. Especially, the buildings energy management …

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 …

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 …

Abstract In this work, Deep Reinforcement Learning (DRL) is implemented to control the
supply water temperature setpoint to terminal units of a heating system. The experiment was …

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 …