Advancing Renewable Energy 2: Geospatial Data Science & Modeling

Type: Paper
Sponsor Groups: Energy and Environment Specialty Group, Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizers: Meghan Mooney, Haiku Sky, Donna Heimiller
Chairs: Meghan Mooney


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 paper session is the second in the series and will explore research by NREL and other external collaborators in the renewable energy geospatial data science and modeling space.


Type Details Minutes
Presenter Dylan Harrison-Atlas*, National Renewable Energy Laboratory, Galen Maclaurin , National Renewable Energy Laboratory, Eric Lantz, National Renewable Energy Laboratory, Informing broad-scale wind energy planning using machine learning to model spatially variable power density for the United States 15

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