TY - DATA
T1 - Data underlying the publication: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation
PY - 2024/02/05
AU - Nan Lin
AU - Stavros Orfanoudakis
UR - 
DO - 10.4121/b27152e4-4237-40f9-a72c-e6a1ca916960.v1
KW - power system
KW - smart grid
KW - power flow
N2 - <p>Synthetic power flow dataset consist of three cases: 14-bus, 118-bus and 6470-bus. The line parameters, generator/load injections, voltage setpoints are randomly sampled based on the standard scenario. The 14-bus case consists of 100000 scenarios, 118-bus 50000 scenarios, and 6470-bus 30000 scenarios.</p><p><br></p><p>If you use parts of this dataset, please cite as:</p><p><br></p><p>@misc{lin2023powerflownet,</p><p>&nbsp;&nbsp;&nbsp;title={PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation},&nbsp;</p><p>&nbsp;&nbsp;&nbsp;author={Nan Lin and Stavros Orfanoudakis and Nathan Ordonez Cardenas and Juan S. Giraldo and Pedro P. Vergara},</p><p>&nbsp;&nbsp;&nbsp;year={2023},</p><p>&nbsp;&nbsp;&nbsp;eprint={2311.03415},</p><p>&nbsp;&nbsp;&nbsp;archivePrefix={arXiv},</p><p>&nbsp;&nbsp;&nbsp;primaryClass={cs.LG}</p><p>}</p>
ER -