[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 …
[HTML][HTML] Applications of reinforcement learning in energy systems
ATD Perera, P Kamalaruban - Renewable and Sustainable Energy …, 2021 - Elsevier
Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …
renewable energy technologies and improve efficiencies, leading to the integration of many …
A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
Advances in de novo drug design: from conventional to machine learning methods
De novo drug design is a computational approach that generates novel molecular structures
from atomic building blocks with no a priori relationships. Conventional methods include …
from atomic building blocks with no a priori relationships. Conventional methods include …
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] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review
Implementing an efficient control strategy for heating, ventilation, and air conditioning
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
(HVAC) systems can lead to improvements in both energy efficiency and thermal …
Deep reinforcement learning for power system applications: An overview
Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …
conventional methods often meet bottlenecks when attempting to solve decision and control …
Multi-agent deep reinforcement learning for HVAC control in commercial buildings
In commercial buildings, about 40%-50% of the total electricity consumption is attributed to
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
Application of machine learning in thermal comfort studies: A review of methods, performance and challenges
ZQ Fard, ZS Zomorodian, SS Korsavi - Energy and Buildings, 2022 - Elsevier
This paper provides a systematic review on the application of Machine Learning (ML) in
thermal comfort studies to highlight the latest methods and findings and provide an agenda …
thermal comfort studies to highlight the latest methods and findings and provide an agenda …
Machine learning driven smart electric power systems: Current trends and new perspectives
MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …