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 …

Building energy use is highly sensitive to its occupants' energy-related behavior, including
their presence and interaction with different building systems. The modeling and simulation …

Controlling buildings energy consumption is a great practical significance. During early
design stage, accurate and rapid prediction of energy consumption could provide a …

With the development of building information and energy consumption data, machine
learning methods are increasingly being used for predicting and analyzing building energy …

Accurate prediction of the building load is crucial to ensure the energy saving and improve
the operational efficiency of the heating, ventilation, and air conditioning (HVAC) system. In …

Inefficiencies in the management of earthmoving equipment greatly contribute to the
productivity gap of infrastructure projects. This paper develops and tests a Deep Neural …

Ventilation has proved to be an effective solution for reducing building cooling load in high-
rise buildings, ie ventilative cooling (VC), especially in cold climates. Mechanical ventilation …

Optimizing the building performance at the early design stage is justified as a promising
approach to achieve many sustainable design goals in buildings; in particular, it opens a …

A major challenge in the common approach of hot water generation in residential houses
lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water …

This paper proposes an energy and carbon footprint modelling using artificial intelligence
technique to assess the impact of occupant density for various types of office building. We …