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 …
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 …
Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions
A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …
A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis
D Mariano-Hernández, L Hernández-Callejo… - Journal of Building …, 2021 - Elsevier
Building energy use is expected to grow by more than 40% in the next 20 years. Electricity
remains the largest energy source consumed by buildings, and that demand is growing. To …
remains the largest energy source consumed by buildings, and that demand is growing. To …
State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field
Y Yao, DK Shekhar - Building and Environment, 2021 - Elsevier
Building systems are subject of dynamic system that have a general feature of non-linearity
and in turn, present us with different challenges for its optimized control of energy-saving …
and in turn, present us with different challenges for its optimized control of energy-saving …
[HTML][HTML] An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are
challenged by unpredicted changes in operational environments due to climate change and …
challenged by unpredicted changes in operational environments due to climate change and …
Reinforcement learning for building controls: The opportunities and challenges
Building controls are becoming more important and complicated due to the dynamic and
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
stochastic energy demand, on-site intermittent energy supply, as well as energy storage …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
[HTML][HTML] Deep learning in mammography images segmentation and classification: Automated CNN approach
In this work, a new framework for breast cancer image segmentation and classification is
proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and …
proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and …
Recycling waste classification using optimized convolutional neural network
An automatic classification robot based on effective image recognition could help reduce
huge labors of recycling tasks. Convolutional neural network (CNN) model, such as …
huge labors of recycling tasks. Convolutional neural network (CNN) model, such as …