Buchumschlag

Deep Reinforcement Learning-Based Decision-Making Strategy Considering User Satisfaction Feedback in Demand Response Program

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Li, Xin, Ding, Li, Lin, Qiao, Yu, Zhen-Wei
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Systems and Control
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
von: Li, Yang, et al.
Veröffentlicht: (2023)

Sizing of Battery Considering Renewable Energy Bidding Strategy with Reinforcement Learning
von: Mantani, Taiyo, et al.
Veröffentlicht: (2026)

Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
von: Li, Yang, et al.
Veröffentlicht: (2024)

Reinforcement Learning-Based Energy Management for Industrial Park with Heterogeneous Batteries under Demand Response
von: Yuan, Meng, et al.
Veröffentlicht: (2026)

Deep-Reinforcement-Learning-Based Adaptive State-Feedback Control for Inter-Area Oscillation Damping with Continuous Eigenvalue Configurations
von: Liang, Siyuan, et al.
Veröffentlicht: (2023)

Demand Response Under Stochastic, Price-Dependent User Behavior
von: Cavraro, Guido, et al.
Veröffentlicht: (2026)

End-to-End Differentiable Predictive Control with Guaranteed Constraint Satisfaction and feasibility for Building Demand Response
von: Xu, Kaipeng, et al.
Veröffentlicht: (2026)

Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control
von: Bi, Congbo, et al.
Veröffentlicht: (2024)

A Robust Optimization Approach for Demand Response Participation of Fixed-Frequency Air Conditioners
von: He, Jinhua, et al.
Veröffentlicht: (2025)

Optimal Operation of Renewable Energy Communities under Demand Response Programs
von: Bianchini, Gianni, et al.
Veröffentlicht: (2025)

Decision-focused Conservation Voltage Reduction to Consider the Cascading Impact of Forecast Errors
von: Du, Qintao, et al.
Veröffentlicht: (2026)

Research on a Two-Layer Demand Response Framework for Electric Vehicle Users and Aggregators Based on LLMs
von: Zhang, Zhaoyi, et al.
Veröffentlicht: (2025)

Multi-Agent Deep Reinforcement Learning for Multiple Anesthetics Collaborative Control
von: Li, Huijie, et al.
Veröffentlicht: (2025)

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
von: Ding, Yimian, et al.
Veröffentlicht: (2025)

A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration
von: Li, Yuanzheng, et al.
Veröffentlicht: (2023)

Multiobjective Model Predictive Control for Residential Demand Response Management Under Uncertainty
von: Lin, Guan-Ting, et al.
Veröffentlicht: (2026)

Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
von: Li, Ran, et al.
Veröffentlicht: (2024)

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
von: Lin, Ding, et al.
Veröffentlicht: (2025)

Selling Demand Response Using Options
von: Muthirayan, Deepan, et al.
Veröffentlicht: (2019)

Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions
von: Al-Sharman, Mohammad, et al.
Veröffentlicht: (2024)

Deep Reinforcement Learning-Based Control Strategy with Direct Gate Control for Buck Converters
von: Katayama, Noboru
Veröffentlicht: (2025)

Efficient and Safe Planner for Automated Driving on Ramps Considering Unsatisfication
von: Li, Qinghao, et al.
Veröffentlicht: (2025)

Online Reinforcement Learning in Markov Decision Process Using Linear Programming
von: Leon, Vincent, et al.
Veröffentlicht: (2023)

Adaptive Model-Based Reinforcement Learning for Orbit Feedback Control in NSLS-II Storage Ring
von: Dong, Zeyu, et al.
Veröffentlicht: (2026)

Solving Decision-Dependent Games by Learning from Feedback
von: Wood, Killian, et al.
Veröffentlicht: (2023)

Enhance Low-Carbon Power System Operation via Carbon-Aware Demand Response
von: Chen, Xin
Veröffentlicht: (2024)

Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
von: Hoppe, Heiko, et al.
Veröffentlicht: (2023)

A Geometric Analysis-Based Safety Assessment Framework for MASS Route Decision-Making in Restricted Waters
von: Xu, Zilong, et al.
Veröffentlicht: (2025)

Hierarchical Decision-Making in Population Games
von: Chen, Yu-Wen, et al.
Veröffentlicht: (2025)

Reinforcement Learning Based Symbolic Regression for Load Modeling
von: Lin, Ding, et al.
Veröffentlicht: (2025)

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid
von: Chiu, Wei-Yu, et al.
Veröffentlicht: (2019)

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
von: Jüni, Colin, et al.
Veröffentlicht: (2026)

Deterministic Multistage Constellation Reconfiguration Using Integer Programming and Sequential Decision-Making Methods
von: Lee, Hang Woon, et al.
Veröffentlicht: (2024)

A Minimal Incentive-based Demand Response Program With Self Reported Baseline Mechanism
von: Muthirayan, Deepan, et al.
Veröffentlicht: (2019)

Anatomical Landmark-Guided Deep Reinforcement Learning for Autonomous Gastric Navigation
von: Wu, Haoxuan, et al.
Veröffentlicht: (2026)

Fast State Stabilization using Deep Reinforcement Learning for Measurement-based Quantum Feedback Control
von: Song, Chunxiang, et al.
Veröffentlicht: (2024)

Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction
von: Mosalli, Hesam, et al.
Veröffentlicht: (2025)

Economic MPC with an Online Reference Trajectory for Battery Scheduling Considering Demand Charge Management
von: Cortes-Aguirre, Cristian, et al.
Veröffentlicht: (2024)

Deep-Learning-Directed Preventive Dynamic Security Control via Coordinated Demand Response
von: Masoumi, Amin, et al.
Veröffentlicht: (2025)

Performance Guarantees for Data-Driven Sequential Decision-Making
von: Li, Bowen, et al.
Veröffentlicht: (2026)