Recent progress in computer science and stringent requirements of the design of “greener”
buildings put forwards the research and applications of simulation-based optimization …

Buildings all around the world consume a significant amount of energy, which is more or
less one-third of the total primary energy resources. This has raised concerns over energy …

How to predict building energy performance with low computational times and good
reliability? The study answers this question by employing artificial neural networks (ANNs) to …

In most of the countries, buildings are often one of the major energy consumers, leading to
the necessity of achieving sustainable building designs, and to the mandatory use of …

The pressing global environmental issues are fostering a rapid change in the energy and
sustainability policies for the built environment. New paradigms are emerging, such as …

An attempt has been made to develop linear regression models and Artificial Neural
Networks (ANN) to predict the heating and cooling energy demands, energy consumptions …

Tree-based ensemble learning has received significant interest as one of the most reliable
and broadly applicable classes of machine learning techniques. However, thus far, it has …

Buildings account for a significant amount of energy consumption leading to the issues of
global emissions and climate change. Thus, energy management in a building is …

Electrochromic windows are one of the most promising technologies in smart windows. They
offer flexible control of indoor thermal environments, lighting environments and energy. The …

Recently, there have been many attempts to predict residential energy consumption using
artificial neural networks. The optimization of these neural networks depends on the trial and …