Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control
DOI:10.4121/19107257.v1
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DOI: 10.4121/19107257
DOI: 10.4121/19107257
Datacite citation style:
Neustroev, Greg; de Weerdt, Mathijs; Remco Verzijbergh; Sytze Andringa (2022): Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/19107257.v1
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Software
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Licence
MIT
This is a simulation study to illustrate benefits of reinforcement learning (RL) for active wake control in wind farms. The repository includes a simulator (./code/wind_farm_gym), implementation of RL agents (./code/agent), and configurations for the experiments presented in the paper (./code/configs), as well as the simulation results (./data). For more detailed instructions, see README.md.
History
- 2022-02-04 first online, published, posted
Publisher
4TU.ResearchDataReferences
Organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software TechnologyDATA
Files (1)
- 645,579 bytesMD5:
5f5700578a1cbefb39dc631bee9b2d91
wind-farm-env-0.0.2.zip