[HTML][HTML] Energetics Systems and artificial intelligence: Applications of industry 4.0

T Ahmad, H Zhu, D Zhang, R Tariq, A Bassam, F Ullah… - Energy Reports, 2022‏ - Elsevier
Industrial development with the growth, strengthening, stability, technical advancement,
reliability, selection, and dynamic response of the power system is essential. Governments …

A review of machine learning in building load prediction

L Zhang, J Wen, Y Li, J Chen, Y Ye, Y Fu, W Livingood - Applied Energy, 2021‏ - Elsevier
The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …

AI-empowered methods for smart energy consumption: A review of load forecasting, anomaly detection and demand response

X Wang, H Wang, B Bhandari, L Cheng - International Journal of Precision …, 2024‏ - Springer
This comprehensive review paper aims to provide an in-depth analysis of the most recent
developments in the applications of artificial intelligence (AI) techniques, with an emphasis …

Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network …

M Alizamir, J Shiri, AF Fard, S Kim, ARD Gorgij… - … Applications of Artificial …, 2023‏ - Elsevier
Accurate daily solar radiation prediction is a crucial task for the management and generation
of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy …

Machine learning for estimation of building energy consumption and performance: a review

S Seyedzadeh, FP Rahimian, I Glesk… - Visualization in …, 2018‏ - Springer
Ever growing population and progressive municipal business demands for constructing new
buildings are known as the foremost contributor to greenhouse gasses. Therefore …

Surrogate modelling for sustainable building design–A review

P Westermann, R Evins - Energy and buildings, 2019‏ - Elsevier
Statistical models can be used as surrogates of detailed simulation models. Their key
advantage is that they are evaluated at low computational cost which can remove …

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

ZM Yaseen, RC Deo, A Hilal, AM Abd, LC Bueno… - … in Engineering Software, 2018‏ - Elsevier
In this research, a machine learning model namely extreme learning machine (ELM) is
proposed to predict the compressive strength of foamed concrete. The potential of the ELM …

[HTML][HTML] Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China

J Fan, L Wu, F Zhang, H Cai, W Zeng, X Wang… - … and Sustainable Energy …, 2019‏ - Elsevier
Accurate estimation of global solar radiation (R s) is essential to the design and assessment
of solar energy utilization systems. Existing empirical and machine learning models for …

Building simulations supporting decision making in early design–A review

T Østergård, RL Jensen, SE Maagaard - Renewable and Sustainable …, 2016‏ - Elsevier
The building design community is challenged by continuously increasing energy demands,
which are often combined with ambitious goals for indoor environment, for environmental …

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 …