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Visualization Encoding Experiments for Power Systems Analysis

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Through a human factors study, we evaluated the use of contour and glyph visualizations for two modern power systems models: an urban distribution model and a large-scale transmission model. This dataset provides model data and scripts for recreating the power flow data and visualizations used in the study, in addition to the study results and statistical analysis.

Citation Formats

TY - DATA AB - Through a human factors study, we evaluated the use of contour and glyph visualizations for two modern power systems models: an urban distribution model and a large-scale transmission model. This dataset provides model data and scripts for recreating the power flow data and visualizations used in the study, in addition to the study results and statistical analysis. AU - Gruchalla A2 - Molnar A3 - Johnson DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - KW - visualization KW - distribution systems KW - transmission systems LA - English DA - 2022/10/27 PY - 2022 PB - National Renewable Energy Laboratory T1 - Visualization Encoding Experiments for Power Systems Analysis UR - https://data.openei.org/submissions/8251 ER -
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Gruchalla, et al. Visualization Encoding Experiments for Power Systems Analysis. National Renewable Energy Laboratory, 27 October, 2022, NREL. https://data.nrel.gov/submissions/200.
Gruchalla, Molnar, & Johnson. (2022). Visualization Encoding Experiments for Power Systems Analysis. [Data set]. NREL. National Renewable Energy Laboratory. https://data.nrel.gov/submissions/200
Gruchalla, Molnar, and Johnson. Visualization Encoding Experiments for Power Systems Analysis. National Renewable Energy Laboratory, October, 27, 2022. Distributed by NREL. https://data.nrel.gov/submissions/200
@misc{OEDI_Dataset_8251, title = {Visualization Encoding Experiments for Power Systems Analysis}, author = {Gruchalla and Molnar and Johnson}, abstractNote = {Through a human factors study, we evaluated the use of contour and glyph visualizations for two modern power systems models: an urban distribution model and a large-scale transmission model. This dataset provides model data and scripts for recreating the power flow data and visualizations used in the study, in addition to the study results and statistical analysis.}, url = {https://data.nrel.gov/submissions/200}, year = {2022}, howpublished = {NREL, National Renewable Energy Laboratory, https://data.nrel.gov/submissions/200}, note = {Accessed: 2025-05-10} }

Details

Data from Oct 27, 2022

Last updated Jan 21, 2025

Submitted Oct 27, 2022

Organization

National Renewable Energy Laboratory

Contact

Kenny Gruchalla

Authors

Gruchalla

National Renewable Energy Laboratory

Molnar

National Renewable Energy Laboratory

Johnson

National Renewable Energy Laboratory

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