Building energy simulation (BES) plays a significant role in buildings with applications such
as architectural design, retrofit analysis, and optimizing building operation and controls …

Buildings consume more than one-third of the world's primary energy. Reducing energy use
and greenhouse-gas emissions in the buildings sector through energy conservation and …

The study investigates the impact of data normalization on the prediction of electricity
consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms …

This study compares building electric energy prediction approaches that use a traditional
statistical method (linear regression) and artificial neural network (ANN) algorithms. We …

The ability to control and optimise energy consumption at end-user level is of increasing
interest as a means to achieve a balance between supply and demand, particularly when …

The development of advanced data-driven approaches for building energy management is
becoming increasingly essential in the era of big data. Machine learning techniques have …

Buildings' expected (projected, simulated) energy use frequently does not match actual
observations. This is commonly referred to as the energy performance gap. As such, many …

Accurate occupancy prediction can improve facility control and energy efficiency of
buildings. In recent years, buildings' exiting WiFi infrastructures have been widely studied in …

With the rapid advances in sensing and digital technologies, cyber-physical systems are
regarded as the most viable platforms for improving building design and management …

In China, despite only 2% of the population residing on university campuses, these
campuses account for 8% of the total energy consumption. Understanding the space-level …