Toward intelligent multizone thermal control with multiagent deep reinforcement learning
Energy usage and thermal comfort are the pillars of smart buildings. Many research works have been proposed to save energy while maintaining a comfortable thermal condition. However, most of them either make the over-simplified assumption on thermal comfort with unsatisfied comfort performance or de...
Saved in:
Main Authors: | Li, Jie, Zhang, Wei, Gao, Guanyu, Wen, Yonggang, Jin, Guangyu, Christopoulos, Georgios |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/152738 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Deepcomfort : energy-efficient thermal comfort control in buildings via reinforcement learning
by: Gao, Guanyu, et al.
Published: (2021) -
Demystifying thermal comfort in smart buildings : an interpretable machine learning approach
by: Zhang, Wei, et al.
Published: (2021) -
INTELLIGENT DYNAMIC THERMAL CONTROL USING DEEP LEARNING AND REINFORCEMENT LEARNING
by: ZHANG QINGANG
Published: (2023) -
Towards explaining sequences of actions in multi-agent deep reinforcement learning models
by: KHAING, Phyo Wai, et al.
Published: (2023) -
HiSOMA: A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning
by: GENG, Minghong, et al.
Published: (2024)