[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 …

Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques

GH Merabet, M Essaaidi, MB Haddou… - … and Sustainable Energy …, 2021 - Elsevier
Building operations represent a significant percentage of the total primary energy consumed
in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning …

A review of deep reinforcement learning for smart building energy management

L Yu, S Qin, M Zhang, C Shen, T Jiang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
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 …

[HTML][HTML] Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review

G Calzolari, W Liu - Building and Environment, 2021 - Elsevier
Fast and accurate airflow simulations in the built environment are critical to provide
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …

Reinforcement learning for selective key applications in power systems: Recent advances and future challenges

X Chen, G Qu, Y Tang, S Low… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

[HTML][HTML] Data-driven predictive control for unlocking building energy flexibility: A review

A Kathirgamanathan, M De Rosa, E Mangina… - … and Sustainable Energy …, 2021 - Elsevier
Managing supply and demand in the electricity grid is becoming more challenging due to
the increasing penetration of variable renewable energy sources. As significant end-use …

[HTML][HTML] Energy modelling and control of building heating and cooling systems with data-driven and hybrid models—A review

Y Balali, A Chong, A Busch, S O'Keefe - Renewable and Sustainable …, 2023 - Elsevier
Implementing an efficient control strategy for heating, ventilation, and air conditioning
(HVAC) systems can lead to improvements in both energy efficiency and thermal …

Multi-agent deep reinforcement learning for HVAC control in commercial buildings

L Yu, Y Sun, Z Xu, C Shen, D Yue… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …

CNN-LSTM architecture for predictive indoor temperature modeling

F Elmaz, R Eyckerman, W Casteels, S Latré… - Building and …, 2021 - Elsevier
Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air
Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive …

A survey on data center cooling systems: Technology, power consumption modeling and control strategy optimization

Q Zhang, Z Meng, X Hong, Y Zhan, J Liu, J Dong… - Journal of Systems …, 2021 - Elsevier
Data center is a fundamental infrastructure of computers and networking equipment to
collect, store, process, and distribute huge amounts of data for a variety of applications such …