[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 …
[HTML][HTML] A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
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] Data-driven predictive control for unlocking building energy flexibility: A review
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 …
the increasing penetration of variable renewable energy sources. As significant end-use …
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 …
[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 …
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning
In this chapter, a novel data-driven method, which is called the deep deterministic policy
gradient (DDPG), is applied for optimally controlling the multi-zone residential heating …
gradient (DDPG), is applied for optimally controlling the multi-zone residential heating …
Occupancy-based HVAC control systems in buildings: A state-of-the-art review
M Esrafilian-Najafabadi, F Haghighat - Building and Environment, 2021 - Elsevier
Intelligent buildings have drawn considerable attention due to rapid progress in
communication and information technologies. These buildings can utilize current and …
communication and information technologies. These buildings can utilize current and …
[HTML][HTML] Reinforcement learning for whole-building HVAC control and demand response
This paper proposes a novel reinforcement learning (RL) architecture for the efficient
scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a …
scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a …