OPFLearnData: Dataset for Learning AC Optimal Power Flow
The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.
Citation Formats
TY - DATA
AB - The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.
AU - Joswig-Jones
A2 - Zamzam
A3 - Baker
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - power system
KW - load profile
KW - machine learning
KW - optimal power flow
KW - nonlinear optimization
LA - English
DA - 2021/10/26
PY - 2021
PB - National Renewable Energy Laboratory
T1 - OPFLearnData: Dataset for Learning AC Optimal Power Flow
UR - https://data.openei.org/submissions/8232
ER -
Joswig-Jones, et al. OPFLearnData: Dataset for Learning AC Optimal Power Flow. National Renewable Energy Laboratory, 26 October, 2021, NREL. https://data.nrel.gov/submissions/177.
Joswig-Jones, Zamzam, & Baker. (2021). OPFLearnData: Dataset for Learning AC Optimal Power Flow. [Data set]. NREL. National Renewable Energy Laboratory. https://data.nrel.gov/submissions/177
Joswig-Jones, Zamzam, and Baker. OPFLearnData: Dataset for Learning AC Optimal Power Flow. National Renewable Energy Laboratory, October, 26, 2021. Distributed by NREL. https://data.nrel.gov/submissions/177
@misc{OEDI_Dataset_8232,
title = {OPFLearnData: Dataset for Learning AC Optimal Power Flow},
author = {Joswig-Jones and Zamzam and Baker},
abstractNote = {The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process.\ The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.},
url = {https://data.nrel.gov/submissions/177},
year = {2021},
howpublished = {NREL, National Renewable Energy Laboratory, https://data.nrel.gov/submissions/177},
note = {Accessed: 2025-05-25}
}
Details
Data from Oct 26, 2021
Last updated Jan 21, 2025
Submitted Oct 26, 2021
Organization
National Renewable Energy Laboratory
Contact
Ahmed Zamzam
Authors
Original Source
https://data.nrel.gov/submissions/177Research Areas
DOE Project Details
Project Name The Science Undergraduate Laboratory Internships Program (SULI), and the Laboratory Directed Research and Development (LDRD) Program at NREL.
Project Number AC36-08GO28308
