The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls

J Ngarambe, GY Yun, M Santamouris - Energy and Buildings, 2020 - Elsevier
Buildings consume about 40% of globally-produced energy. A notable amount of this energy
is used to provide sufficient comfort levels to the building occupants. Moreover, given recent …

A review of thermal comfort models and indicators for indoor environments

D Enescu - Renewable and Sustainable Energy Reviews, 2017 - Elsevier
This paper reviews the most used thermal comfort models and indicators with their variants,
discussing their usage in control problems referring to energy management in indoor …

[HTML][HTML] Artificial intelligence enabled energy-efficient heating, ventilation and air conditioning system: Design, analysis and necessary hardware upgrades

D Lee, ST Lee - Applied Thermal Engineering, 2023 - Elsevier
Literature search across different databases showed that the application of artificial
intelligence (AI) in heating, ventilation and air conditioning (HVAC) equipment has been …

Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach

F Ascione, N Bianco, C De Stasio, GM Mauro… - Energy, 2017 - Elsevier
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 …

Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review

ND Roman, F Bre, VD Fachinotti, R Lamberts - Energy and Buildings, 2020 - Elsevier
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 …

Estimating building energy consumption using extreme learning machine method

S Naji, A Keivani, S Shamshirband, UJ Alengaram… - Energy, 2016 - Elsevier
The current energy requirements of buildings comprise a large percentage of the total
energy consumed around the world. The demand of energy, as well as the construction …

Using machine learning algorithms to predict occupants' thermal comfort in naturally ventilated residential buildings

Q Chai, H Wang, Y Zhai, L Yang - Energy and Buildings, 2020 - Elsevier
Thermal comfort evaluations in the built environment are essential to occupant's satisfaction
and also building energy consumption. Traditionally, thermal comfort has been assessed by …

A prediction model based on neural networks for the energy consumption of a bioclimatic building

R Mena, F Rodríguez, M Castilla, MR Arahal - Energy and Buildings, 2014 - Elsevier
Energy in buildings is a topic that is being widely studied due to its high impact on global
energy demand. This problem involves the performance of an adequate management of the …

Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database

X Zhou, L Xu, J Zhang, B Niu, M Luo, G Zhou… - Energy and …, 2020 - Elsevier
Many models can predict building occupants' thermal comfort, but their accuracies were not
always perfect due to the limited self-learning and self-correction capability when varying the …

LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings

F Mtibaa, KK Nguyen, M Azam, A Papachristou… - Neural Computing and …, 2020 - Springer
Accurate indoor air temperature (IAT) predictions for heating, ventilation, and air
conditioning (HVAC) systems are challenging, especially for multi-zone building and for …