Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings-Reference-Cited by-同舟云学术

Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings

Published:2024-08 Issue:8 Volume:5 Page:4280-4292
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Chen Zhiqiang¹,Yu Liang¹^ORCID,Zhang Shuang¹,Hu Shushan²^ORCID,Shen Chao³^ORCID

Affiliation:

1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

2. School of Artificial Intelligence, Hubei University, Wuhan, China

3. School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Shaanxi Province Key Industry Innovation Program

Fundamental Research Funds for the Central Universities

Qinlan Project of Jiangsu Province

Jiangsu Government Scholarship for Overseas Studies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/10635096/10439266.pdf?arnumber=10439266

Reference45 articles.

1. Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review

2. Grid-interactive efficient buildings technical report series: Overview of research challenges and gaps,” Nat. Renewable Energy Lab. (NREL);Neukomm,2019

3. A National Roadmap for Grid-Interactive Efficient Buildings

4. Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

5. Price-responsive model predictive control of floor heating systems for demand response using building thermal mass