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The html maps are provided as supplementary information for the publication titled Parcel Scale Assessment of Rooftop Solar Technical Potential (NREL/PR-7A40-80780). The maps contain information on rooftop solar technical potential at the block scale for…

The Solar Futures Study explores pathways for solar energy to drive deep decarbonization of the U.S. electric grid and considers how further electrification could decarbonize the broader energy system. This workbook contains data behind most figures…

This dataset has been superseded by a new set of workbooks that can be found here: https://data.nrel.gov/submissions/183 These workbooks contain modeled estimates of long-run marginal CO2 emission rates (LRMER) for the contiguous United States. The…

Open sourced agent files needed to run version 1.0.0 of the dGen model. Contains all national, ISO, and state level residential and commercial agents.

The U.S. Environmental Protection Agency’s (EPA’s) MOtor Vehicle Emission Simulator (MOVES) is a publicly available tool used by researchers and policymakers to help understand motor vehicle emission sources at a national, county, and project level…

Figure data for report on opportunities for solar technologies to provide industrial process heat in the United States (NREL/TP-6A20-77760).

The Caerbannog Point Clouds provide point-sampled 3D models occluded in clouds of points. We synthesized the 3D point clouds from polygonal models, point-sampling the models and surrounding them in a point cloud such that the shape of the model is…

3 months of Eagle jobs data including Slurm metadata and derived power use characterization.

This data set includes the levelized cost of charging (LCOC) and lifetime fuel cost savings (LFCS) values as reported in "Levelized Cost of Charging of Electric Vehicles in the United States." Values are reported at the state and national levels for…

This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it…