Design-Friendly Wind Farm Control
Yaw control setpoint estimation for wind farms using layout-agnostic Graph Neural Networks (GNNs).
Installation
pip install torch torch-geometric
pip install -e .
Usage
from design_friendly.utils.easy import easy_yaw_gnn
# Predict yaw angles
yaw = easy_yaw_gnn(x, y, wd, ws, TI)
See easy.ipynb for examples.
Citation
If you use this code or the models, please cite:
Dirik, D. G., Réthoré, P.-E., Riva, R., & Quick, J. (2026). Design-friendly wind farm control setpoint estimation via layout-agnostic graph neural networks. Journal of Physics: Conference Series.
@article{dirik_design-friendly_2026,
title = {Design-friendly wind farm control setpoint estimation via layout-agnostic graph neural networks},
journal = {Journal of Physics: Conference Series},
author = {Dirik, Deniz Gokhan and Réthoré, Pierre-Elouan and Riva, Riccardo and Quick, Julian},
year = {2026},
}
Metadata
Release files for design-friendly 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| design_friendly-0.1.1.tar.gz | 13.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| design_friendly-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.8 MB
Release files / design_friendly-0.1.1.tar.gz
| Download URL | design_friendly-0.1.1.tar.gz |
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| Size | 13.0 MB |
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| Size | 10.8 MB |
| Tags | Python 3 |
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