Buildings Sector Scenarios (BSS)
The Buildings Sector Scenarios (BSS) dataset establishes and simulates a plausible range of scenarios for U.S. buildings sector development between now and 2050 with a high degree of geographic and temporal resolution. The BSS framework integrates the capabilities of existing modeling tools to pair detailed snapshots of the buildings sector today with representation of the key drivers of change in building and technology stocks over time. The high granularity and extensive scope of BSS data position this modeling resource as a starting point for diverse stakeholder analyses, ranging from the use of regional or national estimates of annual demand to evaluate program impacts to the use of county-level hourly electricity data to inform grid planning efforts and supply-side scenario modeling exercises.
The data lake contains:
- bss_ref_slides: Reference slides with information to support interpretation of the dataset.
- df_potential: Demand flexibility technical potential estimates and costs for the projection years 2030, 2040, and 2050 by scenario (from DR-Path)
- dmd_cal_ann_state_county_hourly: Hourly-county disaggregation multipliers and projections (every 2 years, 2026-2050) of annual, state-level and hourly, county-level demand, post-calibration of electricity data to EIA 861M.
- dmd_uncal_ann_state: Uncalibrated annual state-level stock, energy, and energy cost projections (every 2 years, 2024-2050) by scenario (from Scout).
- meas_scn_inputs: Measure definitions, scenario summary, and adoption driver input files.
For more information on the data structure and contents please see the "README" resource below and the Reference Slides included in the data lake.
Versioning:
- v1.1.0 Created 04/16/2026
- Key changes from v1.0.0:
Assign "miscellaneous" load shape to commercial other electricity usage (previously assigned commercial "gap" shape).
Bug fixes for residential panel upgrade cost assignment and estimation of energy use reductions from code/BPS efficiency provisions.
Note that DF potential estimates (in "Demand Flexibility Technical Potential.zip") are still based on demand projections from v1.
Citation Formats
TY - DATA
AB - The Buildings Sector Scenarios (BSS) dataset establishes and simulates a plausible range of scenarios for U.S. buildings sector development between now and 2050 with a high degree of geographic and temporal resolution. The BSS framework integrates the capabilities of existing modeling tools to pair detailed snapshots of the buildings sector today with representation of the key drivers of change in building and technology stocks over time. The high granularity and extensive scope of BSS data position this modeling resource as a starting point for diverse stakeholder analyses, ranging from the use of regional or national estimates of annual demand to evaluate program impacts to the use of county-level hourly electricity data to inform grid planning efforts and supply-side scenario modeling exercises.
The data lake contains:
- bss_ref_slides: Reference slides with information to support interpretation of the dataset.
- df_potential: Demand flexibility technical potential estimates and costs for the projection years 2030, 2040, and 2050 by scenario (from DR-Path)
- dmd_cal_ann_state_county_hourly: Hourly-county disaggregation multipliers and projections (every 2 years, 2026-2050) of annual, state-level and hourly, county-level demand, post-calibration of electricity data to EIA 861M.
- dmd_uncal_ann_state: Uncalibrated annual state-level stock, energy, and energy cost projections (every 2 years, 2024-2050) by scenario (from Scout).
- meas_scn_inputs: Measure definitions, scenario summary, and adoption driver input files.
For more information on the data structure and contents please see the "README" resource below and the Reference Slides included in the data lake.
Versioning:
- v1.1.0 Created 04/16/2026
- Key changes from v1.0.0:
Assign "miscellaneous" load shape to commercial other electricity usage (previously assigned commercial "gap" shape).
Bug fixes for residential panel upgrade cost assignment and estimation of energy use reductions from code/BPS efficiency provisions.
Note that DF potential estimates (in "Demand Flexibility Technical Potential.zip") are still based on demand projections from v1.
AU - Langevin, Jared
A2 - Pigman, Margaret
A3 - Parker, Andrew
A4 - Wilson, Eric
A5 - Chandra Putra, Handi
A6 - Murthy, Sam
A7 - Sun, Kaiyu
A8 - Zhang, Wanni
A9 - Zhuang, Xinwei
A10 - Satchwell, Andrew
A11 - Ringold, Eric
A12 - Adhikari, Rajendra
A13 - Lou, Yingli
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Laboratory of the Rockies
DO -
KW - energy
KW - buildings
KW - power demand
KW - demand-side solutions
KW - hourly electric loads
KW - annual energy demand
KW - scenario modeling
KW - BSS
KW - building
KW - sector
KW - scenario
KW - data
KW - dataset
KW - raw data
KW - processed data
KW - United States
KW - development
KW - 2050
KW - model
KW - key driver
KW - building stock
KW - technology stock
LA - English
DA - 2025/10/31
PY - 2025
PB - Lawrence Berkeley National Laboratory (LBNL)
T1 - Buildings Sector Scenarios (BSS)
UR - https://data.openei.org/submissions/8558
ER -
Langevin, Jared, et al. Buildings Sector Scenarios (BSS). Lawrence Berkeley National Laboratory (LBNL), 31 October, 2025, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8558.
Langevin, J., Pigman, M., Parker, A., Wilson, E., Chandra Putra, H., Murthy, S., Sun, K., Zhang, W., Zhuang, X., Satchwell, A., Ringold, E., Adhikari, R., & Lou, Y. (2025). Buildings Sector Scenarios (BSS). [Data set]. Open Energy Data Initiative (OEDI). Lawrence Berkeley National Laboratory (LBNL). https://data.openei.org/submissions/8558
Langevin, Jared, Margaret Pigman, Andrew Parker, Eric Wilson, Handi Chandra Putra, Sam Murthy, Kaiyu Sun, Wanni Zhang, Xinwei Zhuang, Andrew Satchwell, Eric Ringold, Rajendra Adhikari, and Yingli Lou. Buildings Sector Scenarios (BSS). Lawrence Berkeley National Laboratory (LBNL), October, 31, 2025. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8558
@misc{OEDI_Dataset_8558,
title = {Buildings Sector Scenarios (BSS)},
author = {Langevin, Jared and Pigman, Margaret and Parker, Andrew and Wilson, Eric and Chandra Putra, Handi and Murthy, Sam and Sun, Kaiyu and Zhang, Wanni and Zhuang, Xinwei and Satchwell, Andrew and Ringold, Eric and Adhikari, Rajendra and Lou, Yingli},
abstractNote = {The Buildings Sector Scenarios (BSS) dataset establishes and simulates a plausible range of scenarios for U.S. buildings sector development between now and 2050 with a high degree of geographic and temporal resolution. The BSS framework integrates the capabilities of existing modeling tools to pair detailed snapshots of the buildings sector today with representation of the key drivers of change in building and technology stocks over time. The high granularity and extensive scope of BSS data position this modeling resource as a starting point for diverse stakeholder analyses, ranging from the use of regional or national estimates of annual demand to evaluate program impacts to the use of county-level hourly electricity data to inform grid planning efforts and supply-side scenario modeling exercises.
The data lake contains:
- bss_ref_slides: Reference slides with information to support interpretation of the dataset.
- df_potential: Demand flexibility technical potential estimates and costs for the projection years 2030, 2040, and 2050 by scenario (from DR-Path)
- dmd_cal_ann_state_county_hourly: Hourly-county disaggregation multipliers and projections (every 2 years, 2026-2050) of annual, state-level and hourly, county-level demand, post-calibration of electricity data to EIA 861M.
- dmd_uncal_ann_state: Uncalibrated annual state-level stock, energy, and energy cost projections (every 2 years, 2024-2050) by scenario (from Scout).
- meas_scn_inputs: Measure definitions, scenario summary, and adoption driver input files.
For more information on the data structure and contents please see the "README" resource below and the Reference Slides included in the data lake.
Versioning:
- v1.1.0 Created 04/16/2026
- Key changes from v1.0.0:
Assign "miscellaneous" load shape to commercial other electricity usage (previously assigned commercial "gap" shape).
Bug fixes for residential panel upgrade cost assignment and estimation of energy use reductions from code/BPS efficiency provisions.
Note that DF potential estimates (in "Demand Flexibility Technical Potential.zip") are still based on demand projections from v1.
},
url = {https://data.openei.org/submissions/8558},
year = {2025},
howpublished = {Open Energy Data Initiative (OEDI), Lawrence Berkeley National Laboratory (LBNL), https://data.openei.org/submissions/8558},
note = {Accessed: 2026-07-28}
}
Details
Data from Oct 31, 2025
Last updated Apr 29, 2026
Submitted Nov 6, 2025
Organization
Lawrence Berkeley National Laboratory (LBNL)
Contact
Jared Langevin
Authors
Research Areas
Keywords
energy, buildings, power demand, demand-side solutions, hourly electric loads, annual energy demand, scenario modeling, BSS, building, sector, scenario, data, dataset, raw data, processed data, United States, development, 2050, model, key driver, building stock, technology stockDOE Project Details
Project Name Buildings Standard Scenarios
Project Number FY25 AOP 3.5.5.71
