The built environment sector is responsible for almost one-third of the world's final energy
consumption. Hence, seeking plausible solutions to minimise building energy demands and …

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 …

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 …

The problem of energy consumption and the importance of improving existing buildings'
energy performance are notorious. This work aims to contribute to this improvement by …

Building operations represent a significant percentage of the total primary energy consumed
in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning …

The utilization of machine learning (ML) techniques is increasingly prevalent in the domain
of building performance evaluation. This trend is primarily driven by ML's capacity to capture …

After decades of evolution and improvements, Artificial Intelligence (AI) is now taking root in
our daily lives, and is starting to profoundly influence the fields of architecture and …

Abstract Heating, Ventilation, and Air Conditioning (HVAC) systems play a vital role in
building energy management by controlling the indoor temperature and ensuring the …

The ever-changing data science landscape is fueling innovation in the built environment
context by providing new and more effective means of converting large raw data sets into …

Residential buildings account for a significant portion of global energy consumption,
emphasizing the pressing need to optimize their energy efficiency. This paper proposes a …