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AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …
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
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 …
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 …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
A review of data-driven fault detection and diagnostics for building HVAC systems
With the wide adoption of building automation system, and the advancement of data,
sensing, and machine learning techniques, data-driven fault detection and diagnostics …
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
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 …
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …
Nevertheless, the conventional" trial and error" method for producing advanced …
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …
recognized lately as an effective approach to accommodate large-scale renewables and …
Load forecasting techniques and their applications in smart grids
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 …
(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
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 …
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
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 …
improving energy efficiency in building energy management systems. Essentially, building …