Sup3rLoad Dataset
Sup3rLoad is a dataset of hourly electricity load for the contiguous United States that varies jointly with electrification and weather. It spans 2 electrification scenarios, 5 CMIP6 Earth system models (Sup3rCC, SSP2-4.5), 6 scenario years between 2025 and 2050, 60 weather years (2000 through 2059), 14 end-use subsectors, and 48 states plus the District of Columbia, stored in a NetCDF4 file. Hourly profiles for weather-sensitive subsectors (heating, cooling, light-duty electric vehicle charging, and data center cooling) were produced by training neural network regressions on building-energy simulations, applying them to downscaled weather projections, and integrating the results through a bottom-up stock-turnover energy-demand model calibrated to state-level electricity sales and hourly system load patterns.
Resources
6 resources available
-
Sales.csv
CSV -
Driver.csv
CSV -
Energy.csv
CSV -
Stock.csv
CSV -
Final Energy Reconciliation Factors.csv
CSV -
Electricity Load Data.nc
NC
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "019:20" ] |
| contactPoint |
{ "fn": "Ryan Jones", "@type": "vcard:Contact", "hasEmail": "mailto:ryan.jones@evolved.energy" } |
| dataQuality |
true
|
| description | Sup3rLoad is a dataset of hourly electricity load for the contiguous United States that varies jointly with electrification and weather. It spans 2 electrification scenarios, 5 CMIP6 Earth system models (Sup3rCC, SSP2-4.5), 6 scenario years between 2025 and 2050, 60 weather years (2000 through 2059), 14 end-use subsectors, and 48 states plus the District of Columbia, stored in a NetCDF4 file. Hourly profiles for weather-sensitive subsectors (heating, cooling, light-duty electric vehicle charging, and data center cooling) were produced by training neural network regressions on building-energy simulations, applying them to downscaled weather projections, and integrating the results through a bottom-up stock-turnover energy-demand model calibrated to state-level electricity sales and hourly system load patterns. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Sales.csv", "format": "csv", "mediaType": "text/csv", "description": "Contains annual technology sales (new installations and replacements) by subsector, technology, state, and year. Sales represent the flow of new technology into the stock each year and govern the pace of stock turnover.", "downloadURL": "https://data.openei.org/files/8750/d_sales.csv" }, { "@type": "dcat:Distribution", "title": "Driver.csv", "format": "csv", "mediaType": "text/csv", "description": "Contains exogenous demand drivers by state and year, including population, economic activity indices, and other factors that scale service demand. Baseline values are drawn from AEO 2023.", "downloadURL": "https://data.openei.org/files/8750/d_driver.csv" }, { "@type": "dcat:Distribution", "title": "Energy.csv", "format": "csv", "mediaType": "text/csv", "description": "Contains final energy demand by subsector, technology, state, year, and fuel type, in mmBtu. It covers both scenarios and all six scenario years. This is the annual energy total that, when multiplied by the normalized hourly shape, produces the hourly load values in the NetCDF file.", "downloadURL": "https://data.openei.org/files/8750/d_energy.csv" }, { "@type": "dcat:Distribution", "title": "Stock.csv", "format": "csv", "mediaType": "text/csv", "description": "Contains the installed technology stock by subsector, technology, state, and year. Units vary by subsector (for example, number of households with a given HVAC system, number of vehicles, or square footage served).", "downloadURL": "https://data.openei.org/files/8750/d_stock.csv" }, { "@type": "dcat:Distribution", "title": "Final Energy Reconciliation Factors.csv", "format": "csv", "mediaType": "text/csv", "description": "Contains the multiplicative adjustment factors used to calibrate bottom-up energy to EIA SEDS gross electricity in the benchmark year (2020), by sector and state. The file is included for transparency and reproducibility.", "downloadURL": "https://data.openei.org/files/8750/d_final_energy_reconciliation_factors.csv" }, { "@type": "dcat:Distribution", "title": "Electricity Load Data.nc", "format": "nc", "mediaType": "application/octet-stream", "description": "File contains a single data variable: load (units: megawatts, dtype: int32) The load variable has 7 dimensions, described below. 1. scenario (2 values) - central - reference 2. climate_model (5 values) - ecearth3cc - ecearth3veg - gfdlcm4 - mpiesm12hr - taiesm1 3. scenario_year (6 values) - 2025, 2030, 2035, 2040, 2045, 2050 4. weather_year (60 values) - 2000 through 2059 5. subsector (14 values) - MHDV charging - commercial air conditioning - commercial space heating - commercial water heating - data center - industry - light-duty charging (home) - light-duty charging (workplace) - other commercial - other residential - other transportation - residential air conditioning - residential space heating - residential water heating 6. state (49 values) - 48 states plus the District of Columbia 7. time (8784 values) - Hourly time steps indexed 0 through 8783 (8784 hours = 8760 + 24 for leap day).", "downloadURL": "https://data.openei.org/files/8750/electricity_load_data.nc" } ] |
| identifier | https://data.openei.org/submissions/8750 |
| issued | 2025-12-06T07:00:00Z |
| keyword |
[ "CMIP6", "CONUS", "NetCDF4", "SSP2-4.5", "Sup3r", "Sup3rCC", "United States", "cooling", "data", "data center cooling", "earth system model", "electric vehicle charging", "electricity", "electrification", "energy", "heating", "hourly profile", "load", "neural network regression", "power", "processed data", "scenario", "weather", "weather projection" ] |
| landingPage | https://data.openei.org/submissions/8750 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2026-08-19T20:59:28Z |
| programCode |
[ "019:000" ] |
| projectNumber |
"12765"
|
| projectTitle | Energy System Planning for Resilience during Severe Weather (ESPRS) |
| publisher |
{ "name": "Evolved Energy Research", "@type": "org:Organization" } |
| spatial |
"{"type":"Polygon","coordinates":[[[-125.4514,24.5873],[-66.5318,24.5873],[-66.5318,49.2637],[-125.4514,49.2637],[-125.4514,24.5873]]]}"
|
| title | Sup3rLoad Dataset |
