In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …

The goal of managing smart cities and societies is to maximize the efficient use of finite
resources while enhancing the quality of life. To establish a sustainable urban existence …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Advanced controls have attracted increasing interests due to the high requirement on smart
and energy-efficient (SEE) buildings and decarbonization in the building industry with …

Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …

Deep learning has emerged as a powerful tool in various domains, revolutionising machine
learning research. However, one persistent challenge is the scarcity of labelled training …

With the advent of sustainable and clean energy transitions, lithium-ion batteries have
become one of the most important energy storage sources for many applications. Battery …

The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

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

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL)
proved to be effective in optimizing the management of integrated energy systems in …