A comprehensive review on the application of artificial neural networks in building energy analysis

SR Mohandes, X Zhang, A Mahdiyar - Neurocomputing, 2019 - Elsevier
This paper presents a comprehensive review of the significant studies exploited Artificial
Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of …

The role of machine learning and the internet of things in smart buildings for energy efficiency

SFA Shah, M Iqbal, Z Aziz, TA Rana, A Khalid… - Applied Sciences, 2022 - mdpi.com
Machine learning can be used to automate a wide range of tasks. Smart buildings, which
use the Internet of Things (IoT) to connect building operations, enable activities, such as …

CNN-LSTM architecture for predictive indoor temperature modeling

F Elmaz, R Eyckerman, W Casteels, S Latré… - Building and …, 2021 - Elsevier
Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air
Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive …

Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: A review and new modeling results

S Ghimire, RC Deo, H Wang, MS Al-Musaylh… - Energies, 2022 - mdpi.com
We review the latest modeling techniques and propose new hybrid SAELSTM framework
based on Deep Learning (DL) to construct prediction intervals for daily Global Solar …

Adopting building information modeling (BIM) for the development of smart buildings: a review of enabling applications and challenges

A Yang, M Han, Q Zeng, Y Sun - Advances in Civil Engineering, 2021 - Wiley Online Library
The construction industry is undergoing a digital revolution due to the emergence of new
technologies. A significant trend is that construction projects have been transformed and …

Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model

Z Fang, N Crimier, L Scanu, A Midelet, A Alyafi… - Energy and …, 2021 - Elsevier
Accurate indoor temperature forecasting can facilitate energy savings of the building without
compromising the occupant comfort level, by providing more accurate control of the HVAC …

[HTML][HTML] Smart readiness indicator evaluation and cost estimation of smart retrofitting scenarios-A comparative case-study in European residential buildings

V Apostolopoulos, P Giourka, G Martinopoulos… - Sustainable Cities and …, 2022 - Elsevier
The current research applies the SRI methodology in two typologies of typical residential
buildings, Single-Family Houses and Multi-Family Houses, in five EU Countries, to evaluate …

A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings

S Alawadi, D Mera, M Fernández-Delgado… - Energy Systems, 2020 - Springer
The international community has largely recognized that the Earth's climate is changing.
Mitigating its global effects requires international actions. The European Union (EU) is …

Artificial neural networks for sustainable development of the construction industry

M Ahmed, S AlQadhi, J Mallick, NB Kahla, HA Le… - Sustainability, 2022 - mdpi.com
Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence
(AI) technology due to their proven accuracy and efficiency in control, estimation …

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 …