Geospatial Data Science Modeling

NREL uses geospatial data science modeling to develop innovative models and tools for energy professionals, project developers, and consumers.

Photo of researchers inspecting maps on a large display.

Geospatial modeling at NREL often produces the foundational information for other modeling efforts, including grid-planning modeling (ReEDS), grid operations modeling (PLEXOS), distributed generation market demand modeling (dGen), and modeling of specific systems.

Featured Model

NREL's open-source Renewable Energy Potential (reV) model is an example of geospatial modeling. The reV model enables detailed techno-economic assessment of renewable energy resources under a variety of regulatory, sociopolitical, and environmental factors. reV also serves as a pipeline for coupling energy models (e.g., ReEDS and PLEXOS) to ensure model scenarios are seeded with synchronous data. The reV model was developed for cross-platform use and can be scaled to run a small geographic extent on a laptop to all of North America on a high performance computer (HPC) or on the cloud.