Deep Reinforcement Learning for Optimal Hydropower Reservoir Operation-Reference-Cited by-同舟云学术

Deep Reinforcement Learning for Optimal Hydropower Reservoir Operation

Published:2021-08 Issue:8 Volume:147 Page:04021045
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Xu Wei¹,Meng Fanlin²,Guo Weisi³,Li Xia¹,Fu Guangtao⁴^ORCID

Affiliation:

1. Associate Professor, College of River and Ocean Engineering, Chongqing Jiaotong Univ., No.66 Xuefu Rd., Ran’an District, Chongqing 400074, China.

2. Research Fellow, Center for Water Systems, Univ. of Exeter, Exeter EX4 4QF, UK.

3. Professor, School of Aerospace, Transport and Manufacturing, Cranfield Univ., College Road, Bedford, Bedfordshire MK43 0AL, UK.

4. Professor, Center for Water Systems, Univ. of Exeter, Exeter EX4 4QF, UK; Turing Fellow, Alan Turing Institute, 96 Euston Rd., London NW1 2DB, UK (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001409

Reference39 articles.

1. Water Reservoir Control with Data Mining

2. Tree-based reinforcement learning for optimal water reservoir operation

3. A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run

4. An MDP Model-Based Reinforcement Learning Approach for Production Station Ramp-Up Optimization: Q-Learning Analysis

5. Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts

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