Deep Reinforcement Learning Based Data-Driven Mapping Mechanism of Digital Twin for Internet of Energy-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Based Data-Driven Mapping Mechanism of Digital Twin for Internet of Energy

Published:2024-07 Issue:4 Volume:11 Page:3876-3890
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Xu Siyu¹^ORCID,Guan Xin¹^ORCID,Peng Yu²^ORCID,Liu Yang¹^ORCID,Cui Chen¹^ORCID,Chen Hongyang³^ORCID,Ohtsuki Tomoaki⁴^ORCID,Han Zhu⁵^ORCID

Affiliation:

1. School of Data Science and Technology, Heilongjiang University, Harbin, China

2. State Grid Heilongjiang Electric Power Company Limited, Heilongjiang University, Harbin, China

3. Zhejiang Laboratory, Hangzhou, China

4. Department of Information and Computer Science, Keio University, Yokohama, Japan

5. Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA

Funder

Management and Consulting Projects of State Grid Corporation of China

NSF

U.S. Department of Transportation

Toyota Motor Corporation

Amazon and Japan Science and Technology Agency

Adopting Sustainable Partnerships for Innovative Research Ecosystem

Special Funds of Heilongjiang University of the Fundamental Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6488902/10554364/10505015-aam.pdf

Reference31 articles.

1. A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies

2. Digital Twin Networks: A Survey

3. Building blocks for a digital twin of additive manufacturing

4. A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments

5. Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

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