Dynamic Energy Management for IoT-enabled Smart Microgrid using Deep Reinforcement Learning
Author:
Affiliation:
1. Central South University,School of Electronic Information,ChangSha,China
2. Central South University,School of Computer Science and Engineering,ChangSha,China
3. Central South University,School of Automation,ChangSha,China
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10579968/10579982/10580342.pdf?arnumber=10580342
Reference19 articles.
1. A survey on smart grid technologies and applications
2. Integration of smart grid with renewable energy sources: Opportunities and challenges – A comprehensive review
3. The future energy internet for utility energy service and demand-side management in smart grid: Current practices, challenges and future directions
4. A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids
5. Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty
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