Visualization Encoding Experiments for Power Systems Analysis
Publicly accessible
License
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
Computational Science. (2022). Visualization Encoding Experiments for Power Systems Analysis [data set]. Retrieved from https://data.nrel.gov/submissions/200.
Gruchalla, , Molnar, , and Johnson, . Visualization Encoding Experiments for Power Systems Analysis. United States: N.p., 27 Oct, 2022. Web. https://data.nrel.gov/submissions/200.
Gruchalla, , Molnar, , & Johnson, . Visualization Encoding Experiments for Power Systems Analysis. United States. https://data.nrel.gov/submissions/200
Gruchalla, , Molnar, , and Johnson, . 2022. "Visualization Encoding Experiments for Power Systems Analysis". United States. https://data.nrel.gov/submissions/200.
@div{oedi_8251, title = {Visualization Encoding Experiments for Power Systems Analysis}, author = {Gruchalla, , 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. }, doi = {}, url = {https://data.nrel.gov/submissions/200}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {10}}
Details
Data from Oct 27, 2022
Last updated Dec 18, 2024
Submitted Oct 27, 2022
Organization
Computational Science
Contact
Kenny Gruchalla
