AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

Y Himeur, M Elnour, F Fadli, N Meskin, I Petri… - Artificial Intelligence …, 2023‏ - Springer
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …

[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review

Z Chen, F **ao, F Guo, J Yan - Advances in Applied Energy, 2023‏ - Elsevier
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …

T Ahmad, R Madonski, D Zhang, C Huang… - … and Sustainable Energy …, 2022‏ - Elsevier
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …

A review of data-driven fault detection and diagnostics for building HVAC systems

Z Chen, Z O'Neill, J Wen, O Pradhan, T Yang, X Lu… - Applied Energy, 2023‏ - Elsevier
With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …

Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review

M Khalil, AS McGough, Z Pourmirza… - … Applications of Artificial …, 2022‏ - Elsevier
The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …

Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction

J Li, N Wu, J Zhang, HH Wu, K Pan, Y Wang, G Liu… - Nano-Micro Letters, 2023‏ - Springer
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

Review and prospect of data-driven techniques for load forecasting in integrated energy systems

J Zhu, H Dong, W Zheng, S Li, Y Huang, L ** - Applied Energy, 2022‏ - Elsevier
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …

Load forecasting techniques and their applications in smart grids

H Habbak, M Mahmoud, K Metwally, MM Fouda… - Energies, 2023‏ - mdpi.com
The growing success of smart grids (SGs) is driving increased interest in load forecasting
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

G Pinto, Z Wang, A Roy, T Hong, A Capozzoli - Advances in Applied Energy, 2022‏ - Elsevier
Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit
about one-third of greenhouse gases. In the last few years, machine learning has achieved …

Building energy prediction using artificial neural networks: A literature survey

C Lu, S Li, Z Lu - Energy and Buildings, 2022‏ - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …