2022
DOI: 10.1007/s10489-022-04320-7
|View full text |Cite
|
Sign up to set email alerts
|

Application of deep reinforcement learning to intelligent distributed humidity control system

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 35 publications
0
0
0
Order By: Relevance
“…The naive RL approaches in HVAC were explored by 21,22 . Releasing the expressive capability of deep neural networks, fitted Q iteration and deep Q learning (DQN) were implemented in the HVAC systems with discrete action space 23,24 and indoor humidity control system 25 . Deep deterministic policy gradient (DDPG) was applied to optimize HVAC systems in green data center 26 , energy distribution system 27 and multi-zone residential HVAC systems 28,29 .…”
Section: Introductionmentioning
confidence: 99%
“…The naive RL approaches in HVAC were explored by 21,22 . Releasing the expressive capability of deep neural networks, fitted Q iteration and deep Q learning (DQN) were implemented in the HVAC systems with discrete action space 23,24 and indoor humidity control system 25 . Deep deterministic policy gradient (DDPG) was applied to optimize HVAC systems in green data center 26 , energy distribution system 27 and multi-zone residential HVAC systems 28,29 .…”
Section: Introductionmentioning
confidence: 99%