cff-version: 1.2.0
abstract: "<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>"
authors:
  - family-names: Lin
    given-names: Nan
    orcid: "https://orcid.org/0000-0002-6745-8281"
  - family-names: Orfanoudakis
    given-names: Stavros
    orcid: "https://orcid.org/0000-0002-0767-9488"
title: "Data underlying the publication: PowerFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/b27152e4-4237-40f9-a72c-e6a1ca916960.v1
license: CC BY 4.0
date-released: 2024-02-05