Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)
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.
Citation Formats
National Renewable Energy Laboratory. (2018). Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS) [data set]. Retrieved from https://dx.doi.org/10.25984/1823248.
Hale, Elaine, Horsey, Henry, Johnson, Brandon, Muratori, Matteo, Wilson, Eric, Borlaug, Brennan, Christensen, Craig, Farthing, Amanda, Hettinger, Dylan, Parker, Andrew, Robertson, Joseph, Rossol, Michael, Stephen, Gord, Wood, Eric, and Vairamohan, Baskar. Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS). United States: N.p., 08 Jul, 2018. Web. doi: 10.25984/1823248.
Hale, Elaine, Horsey, Henry, Johnson, Brandon, Muratori, Matteo, Wilson, Eric, Borlaug, Brennan, Christensen, Craig, Farthing, Amanda, Hettinger, Dylan, Parker, Andrew, Robertson, Joseph, Rossol, Michael, Stephen, Gord, Wood, Eric, & Vairamohan, Baskar. Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS). United States. https://dx.doi.org/10.25984/1823248
Hale, Elaine, Horsey, Henry, Johnson, Brandon, Muratori, Matteo, Wilson, Eric, Borlaug, Brennan, Christensen, Craig, Farthing, Amanda, Hettinger, Dylan, Parker, Andrew, Robertson, Joseph, Rossol, Michael, Stephen, Gord, Wood, Eric, and Vairamohan, Baskar. 2018. "Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)". United States. https://dx.doi.org/10.25984/1823248. https://data.openei.org/submissions/4130.
@div{oedi_4130, title = {Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)}, author = {Hale, Elaine, Horsey, Henry, Johnson, Brandon, Muratori, Matteo, Wilson, Eric, Borlaug, Brennan, Christensen, Craig, Farthing, Amanda, Hettinger, Dylan, Parker, Andrew, Robertson, Joseph, Rossol, Michael, Stephen, Gord, Wood, Eric, and Vairamohan, Baskar.}, abstractNote = {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.}, doi = {10.25984/1823248}, url = {https://data.openei.org/submissions/4130}, journal = {}, number = , volume = , place = {United States}, year = {2018}, month = {07}}
https://dx.doi.org/10.25984/1823248
Details
Data from Jul 8, 2018
Last updated Jan 2, 2024
Submitted Jul 21, 2021
Organization
National Renewable Energy Laboratory
Contact
Elaine T. Hale
303.384.7812
Authors
Research Areas
Keywords
energy, power, demand, Electrification Futures Study, dsgrid, historial year, modeled data, high-resolution, electricity demand, demand-side, demand side, model, analysis, processed data, electrification, demand flexibility, validation, grid, electrical, PyPl, python, contiguous United States, load, data, electricityDOE Project Details
Project Name Integrated Nuclear Renewable Energy Systems Analysis
Project Number FY17 AOP 2.4.0.3