Smart Scheduling of EVs Through Intelligent Home Energy Management Using Deep Reinforcement Learning
Author:
Affiliation:
1. University of the Punjab,Pak
2. Tsinghua University,China
3. North Dakota State University,USA
4. Tallinn University of Technology,Tallinn,Estonia
5. North China Electric Power University,China
6. Military College of Signals, NUST,Pakistan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10004630/10004620/10004664.pdf?arnumber=10004664
Reference22 articles.
1. Efficient Reactive Power Control Using Reinforcement Learning under Inaccurate Power Network Model
2. An Advanced Satisfaction-Based Home Energy Management System Using Deep Reinforcement Learning
3. Residential Demand Response for Renewable Energy Resources in Smart Grid Systems
4. Coordinated Charge and Discharge Scheduling of Electric Vehicles for Load Curve Shaping
5. A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor
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1. A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks;Applied Energy;2024-02
2. Real-Time Energy Management in Smart Homes Through Deep Reinforcement Learning;IEEE Access;2024
3. Distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17
4. Multi-objective optimal scheduling of an integrated energy system under the multi-time scale ladder-type carbon trading mechanism;Journal of Cleaner Production;2023-09
5. Renewable Energy Maximization for Pelagic Islands Network of Microgrids Through Battery Swapping Using Deep Reinforcement Learning;IEEE Access;2023
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