TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters
Through this TEAMER project, Michigan Technological University (MTU) collaborated with Oregon State University (OSU) to test the performance of a Deep Reinforcement Learning (DRL) control in the wave tank. Unlike model-based controls, DRL control is model-free and can directly maximize the performance of the Wave Energy Converter (WEC) in terms of power production, regardless of system complexity. While DRL control has demonstrated promising performance in previous studies, this project aimed to (1) evaluate the practical performance of DRL control and (2) identify the challenges and limitations associated with its practical implementation.
The dataset consists of six main parts:
(1) the Post Access Report,
(2) the test log,
(3) the tank testing results,
(4) the model used for retraining the DRL control and its results,
(5) the model used for pretraining the DRL control and its results, and
(6) the scripts used for processing the data.
This testing was funded by TEAMER RTFS 10
Citation Formats
TY - DATA
AB - Through this TEAMER project, Michigan Technological University (MTU) collaborated with Oregon State University (OSU) to test the performance of a Deep Reinforcement Learning (DRL) control in the wave tank. Unlike model-based controls, DRL control is model-free and can directly maximize the performance of the Wave Energy Converter (WEC) in terms of power production, regardless of system complexity. While DRL control has demonstrated promising performance in previous studies, this project aimed to (1) evaluate the practical performance of DRL control and (2) identify the challenges and limitations associated with its practical implementation.
The dataset consists of six main parts:
(1) the Post Access Report,
(2) the test log,
(3) the tank testing results,
(4) the model used for retraining the DRL control and its results,
(5) the model used for pretraining the DRL control and its results, and
(6) the scripts used for processing the data.
This testing was funded by TEAMER RTFS 10
AU - Zou, Shangyan
A2 - Subramanian, Abishek
A3 - Bosma, Bret
A4 - Lou, Junhui
A5 - Beringer, Courtney
A6 - Robertson, Bryson
A7 - Lomonaco, Pedro
DB - Marine and Hydrokinetic Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - MHK
KW - Marine
KW - Wave Energy
KW - Deep Reinforcement Learning
KW - PTO control
LA - English
DA - 2025/02/17
PY - 2025
PB - Michigan Technological University
T1 - TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters
UR - https://mhkdr.openei.org/submissions/628
ER -
Zou, Shangyan, et al. TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters. Michigan Technological University, 17 February, 2025, Marine and Hydrokinetic Data Repository. https://mhkdr.openei.org/submissions/628.
Zou, S., Subramanian, A., Bosma, B., Lou, J., Beringer, C., Robertson, B., & Lomonaco, P. (2025). TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters. [Data set]. Marine and Hydrokinetic Data Repository. Michigan Technological University. https://mhkdr.openei.org/submissions/628
Zou, Shangyan, Abishek Subramanian, Bret Bosma, Junhui Lou, Courtney Beringer, Bryson Robertson, and Pedro Lomonaco. TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters. Michigan Technological University, February, 17, 2025. Distributed by Marine and Hydrokinetic Data Repository. https://mhkdr.openei.org/submissions/628
@misc{MHKDR_Dataset_628,
title = {TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters},
author = {Zou, Shangyan and Subramanian, Abishek and Bosma, Bret and Lou, Junhui and Beringer, Courtney and Robertson, Bryson and Lomonaco, Pedro},
abstractNote = {Through this TEAMER project, Michigan Technological University (MTU) collaborated with Oregon State University (OSU) to test the performance of a Deep Reinforcement Learning (DRL) control in the wave tank. Unlike model-based controls, DRL control is model-free and can directly maximize the performance of the Wave Energy Converter (WEC) in terms of power production, regardless of system complexity. While DRL control has demonstrated promising performance in previous studies, this project aimed to (1) evaluate the practical performance of DRL control and (2) identify the challenges and limitations associated with its practical implementation.
The dataset consists of six main parts:
(1) the Post Access Report,
(2) the test log,
(3) the tank testing results,
(4) the model used for retraining the DRL control and its results,
(5) the model used for pretraining the DRL control and its results, and
(6) the scripts used for processing the data.
This testing was funded by TEAMER RTFS 10},
url = {https://mhkdr.openei.org/submissions/628},
year = {2025},
howpublished = {Marine and Hydrokinetic Data Repository, Michigan Technological University, https://mhkdr.openei.org/submissions/628},
note = {Accessed: 2025-05-21}
}
Details
Data from Feb 17, 2025
Last updated May 20, 2025
Submission in progress
Organization
Michigan Technological University
Contact
Shangyan Zou
Authors
Keywords
MHK, Marine, Wave Energy, Deep Reinforcement Learning, PTO controlDOE Project Details
Project Name TEAMER: Experimental Validation and Analysis of Deep Reinforcement Learning Control for Wave Energy Converters
Project Lead Lauren Ruedy
Project Number EE0008895
