Renewable energy (RE) is the fastest growing sector of our country’s electricity portfolio, and has become a mainstream power source nationally and internationally, by utilities, businesses and individual homes. Unlike conventional fuel sources, RE data sources are more limited to development where and when the resource occurs; the characteristics of energy demand, energy distribution, and energy cost also change regionally and temporally. The significant spatiotemporal characterization of all of these factors makes the application of geospatial data science essential to understanding RE opportunities and development issues, incorporating physical, operational and social parameters that can impact development. These applications include high spatiotemporal resolution modeling of RE resource data, modeling how RE integrates into the existing electricity infrastructure and evolving grid systems at multiple scales, understanding factors that drive energy choices at individual homeowner to national scales, and understanding the impacts of changes in energy demand and availability due to changes in climate.
The National Renewable Energy Laboratory's (NREL) Geospatial Data Science Team is hosting a series of sessions focused on the role of geospatial data science, modeling, and visualization in advancing renewable energy futures. This session is the third in the series and will explore interactive visualizations and applications by NREL and other external collaborators in the renewable energy geospatial data science and modeling space.
|Presenter||Haiku Sky*, NREL, Renewable Energy Geospatial Data Visualization Applications||15||11:10 AM|
|Presenter||Nicholas Gilroy*, NREL, Visualizations commonly used in the renewable energy space||15||11:25 AM|
|Presenter||Aaron Vimont*, NREL, The Low-Income Energy Affordability Data (LEAD) Tool||15||11:40 AM|
|Presenter||Jeff Friesen*, Radiant Labs, Adam Stenftenagel, Radiant Labs, Visualizing Carbon Free Opportunities at Decision Maker Resolutions||15||11:55 AM|
|Presenter||Kristi Potter*, , Uncertainty Visualization: From Metrics to Imagery||15||12:10 PM|
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