This data set contains the full-resolution and state-level data described in the linked technical report (https://www.nrel.gov/docs/fy18osti/71492.pdf). It can be accessed with the NREL-dsgrid-legacy-efs-api, available on GitHub at https://github.com/dsgrid/dsgrid-legacy-efs-api and through PyPI (pip install NREL-dsgrid-legacy-efs-api). The data format is HDF5. The API is written in Python. This initial dsgrid data set, whose description was originally published in 2018, covers electricity demand in the contiguous United States (CONUS) for the historical year of 2012. It is a proof-of-concept demonstrating the feasibility of reconciling bottom-up demand modeling results with top-down information about electricity demand to create a more detailed description than is possible with either type of data source on its own. The result is demand data that is more highly resolved along geographic, temporal, sectoral, and end-use dimensions as may be helpful for conducting electricity sector-wide "what-if" analysis of, e.g., energy efficiency, electrification, and/or demand flexibility. Although we conducted bottom-up versus top-down validation, the final residuals were significant, especially at higher geographic and temporal resolution. Please see the Executive Summary and/or Section 3 of the report to obtain an understanding of the data set limitations before deciding whether these data are suitable for any particular use case. New dsgrid datasets are under development. Please visit https://www.nrel.gov/analysis/dsgrid.html for the latest information which is also linked in the data resources.

This page contains links to all available GIS elevation datasets, services, and related applications.

This page contains available Vermont land cover data in GIS format.

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial dataset "sup3rcc_conus_mriesm20_ssp585_r1i1p1f1" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical average climate, not the actual historical weather that we experienced. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the *possible* future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.