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Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021

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This dataset contains simulated hourly end use load profiles of the residential and commercial building sector in the contiguous United States for every other year from 2010 to 2050. Data were produced in 2021 using ResStockTM and ComStockTM, which are building stock energy models of the US residential and commercial sector, respectively, and published in dsgrid Toolkit format.

The dataset consists of base year 2018 ResStock and ComStock (collectively known as BuildStock) timeseries data differentiated by county, building type, fuel type, and end use, along with backward-and forward-looking projections created by applying regional-, sectoral-, and end use-specific growth rates derived from EIA?s 2021 Annual Energy Outlook (AEO)?s Reference Scenario. The base year datasets represent the US building stock as of 2018 and were simulated in 2021 using AMY 2012 weather to align with NREL?s wind and solar resource datasets. They were produced using the BuildStock tools during the End Use Load Profiles (EULP) calibration project. The projection methodology is described in NREL/TP-5500-84471. Reflecting EIA?s reference scenario assumptions to provide a baseline for exploring long-term trends, the projection does not reflect large-scale electrification of building space heating, water heating, clothes drying, cooking, or other end uses. The dataset also does not include electric vehicle charging that might occur on-site at buildings. Electric vehicle charging is described in the dsgrid TEMPO Light-Duty Vehicle Charging Profiles v2022 (https://data.openei.org/submissions/5958).

This dataset describes a reference projection of building energy consumption at a resolution sufficient for bulk power system and other forms of regional energy system planning. It improves on traditional load forecasting practices in the power sector by providing annual hourly data resolved geographically, temporally, and sectorally using state-of-the-art sector-specific energy modeling tools and dimensionally aligned (i.e., regionally, sectorally, and end-use specific) growth rates. Compared to previous practice of regional load forecasts using a single load shape and all-electricity growth rates, the product is a more resolved dataset that is easier to align with the geographic resolution of production cost and capacity expansion models and more capable of representing load shape changes induced by uneven growth across sectors or technology types. The parameterization of the growth rates could also enable creation of alternative scenarios with different amounts of electrification and energy efficiency.

The full dataset as well as various aggregations are available for access. Large datasets are in parquet format, with some partitioned by a few key dimensions. Smaller datasets are available as csv.

Citation Formats

TY - DATA AB - This dataset contains simulated hourly end use load profiles of the residential and commercial building sector in the contiguous United States for every other year from 2010 to 2050. Data were produced in 2021 using ResStockTM and ComStockTM, which are building stock energy models of the US residential and commercial sector, respectively, and published in dsgrid Toolkit format. The dataset consists of base year 2018 ResStock and ComStock (collectively known as BuildStock) timeseries data differentiated by county, building type, fuel type, and end use, along with backward-and forward-looking projections created by applying regional-, sectoral-, and end use-specific growth rates derived from EIA?s 2021 Annual Energy Outlook (AEO)?s Reference Scenario. The base year datasets represent the US building stock as of 2018 and were simulated in 2021 using AMY 2012 weather to align with NREL?s wind and solar resource datasets. They were produced using the BuildStock tools during the End Use Load Profiles (EULP) calibration project. The projection methodology is described in NREL/TP-5500-84471. Reflecting EIA?s reference scenario assumptions to provide a baseline for exploring long-term trends, the projection does not reflect large-scale electrification of building space heating, water heating, clothes drying, cooking, or other end uses. The dataset also does not include electric vehicle charging that might occur on-site at buildings. Electric vehicle charging is described in the dsgrid TEMPO Light-Duty Vehicle Charging Profiles v2022 (https://data.openei.org/submissions/5958). This dataset describes a reference projection of building energy consumption at a resolution sufficient for bulk power system and other forms of regional energy system planning. It improves on traditional load forecasting practices in the power sector by providing annual hourly data resolved geographically, temporally, and sectorally using state-of-the-art sector-specific energy modeling tools and dimensionally aligned (i.e., regionally, sectorally, and end-use specific) growth rates. Compared to previous practice of regional load forecasts using a single load shape and all-electricity growth rates, the product is a more resolved dataset that is easier to align with the geographic resolution of production cost and capacity expansion models and more capable of representing load shape changes induced by uneven growth across sectors or technology types. The parameterization of the growth rates could also enable creation of alternative scenarios with different amounts of electrification and energy efficiency. The full dataset as well as various aggregations are available for access. Large datasets are in parquet format, with some partitioned by a few key dimensions. Smaller datasets are available as csv. AU - Hale, Elaine A2 - Liu, Lixi A3 - Bianchi, Carlo A4 - Parker, Andrew A5 - Fontanini, Anthony A6 - Horsey, Ry A7 - Sandoval, Noah A8 - Reyna, Janet A9 - Thom, Dan A10 - Mooney, Meghan A11 - Jensen, Zack A12 - Praprost, Marlena A13 - Van Sant, Amy DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - KW - energy KW - power KW - electricity KW - energy forecast KW - load projection KW - residential KW - commercial KW - buildings KW - building stock energy modeling KW - high-performance computing KW - United States US KW - ResStock KW - ComStock KW - dsgrid KW - Energy Information Agency EIA LA - English DA - 2025/07/09 PY - 2025 PB - National Renewable Energy Lab (NREL) T1 - Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021 UR - https://data.openei.org/submissions/8446 ER -
Export Citation to RIS
Hale, Elaine, et al. Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021. National Renewable Energy Lab (NREL), 9 July, 2025, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8446.
Hale, E., Liu, L., Bianchi, C., Parker, A., Fontanini, A., Horsey, R., Sandoval, N., Reyna, J., Thom, D., Mooney, M., Jensen, Z., Praprost, M., & Van Sant, A. (2025). Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Lab (NREL). https://data.openei.org/submissions/8446
Hale, Elaine, Lixi Liu, Carlo Bianchi, Andrew Parker, Anthony Fontanini, Ry Horsey, Noah Sandoval, Janet Reyna, Dan Thom, Meghan Mooney, Zack Jensen, Marlena Praprost, and Amy Van Sant. Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021. National Renewable Energy Lab (NREL), July, 9, 2025. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8446
@misc{OEDI_Dataset_8446, title = {Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021}, author = {Hale, Elaine and Liu, Lixi and Bianchi, Carlo and Parker, Andrew and Fontanini, Anthony and Horsey, Ry and Sandoval, Noah and Reyna, Janet and Thom, Dan and Mooney, Meghan and Jensen, Zack and Praprost, Marlena and Van Sant, Amy}, abstractNote = {This dataset contains simulated hourly end use load profiles of the residential and commercial building sector in the contiguous United States for every other year from 2010 to 2050. Data were produced in 2021 using ResStockTM and ComStockTM, which are building stock energy models of the US residential and commercial sector, respectively, and published in dsgrid Toolkit format.

The dataset consists of base year 2018 ResStock and ComStock (collectively known as BuildStock) timeseries data differentiated by county, building type, fuel type, and end use, along with backward-and forward-looking projections created by applying regional-, sectoral-, and end use-specific growth rates derived from EIA?s 2021 Annual Energy Outlook (AEO)?s Reference Scenario. The base year datasets represent the US building stock as of 2018 and were simulated in 2021 using AMY 2012 weather to align with NREL?s wind and solar resource datasets. They were produced using the BuildStock tools during the End Use Load Profiles (EULP) calibration project. The projection methodology is described in NREL/TP-5500-84471. Reflecting EIA?s reference scenario assumptions to provide a baseline for exploring long-term trends, the projection does not reflect large-scale electrification of building space heating, water heating, clothes drying, cooking, or other end uses. The dataset also does not include electric vehicle charging that might occur on-site at buildings. Electric vehicle charging is described in the dsgrid TEMPO Light-Duty Vehicle Charging Profiles v2022 (https://data.openei.org/submissions/5958).

This dataset describes a reference projection of building energy consumption at a resolution sufficient for bulk power system and other forms of regional energy system planning. It improves on traditional load forecasting practices in the power sector by providing annual hourly data resolved geographically, temporally, and sectorally using state-of-the-art sector-specific energy modeling tools and dimensionally aligned (i.e., regionally, sectorally, and end-use specific) growth rates. Compared to previous practice of regional load forecasts using a single load shape and all-electricity growth rates, the product is a more resolved dataset that is easier to align with the geographic resolution of production cost and capacity expansion models and more capable of representing load shape changes induced by uneven growth across sectors or technology types. The parameterization of the growth rates could also enable creation of alternative scenarios with different amounts of electrification and energy efficiency.

The full dataset as well as various aggregations are available for access. Large datasets are in parquet format, with some partitioned by a few key dimensions. Smaller datasets are available as csv.}, url = {https://data.openei.org/submissions/8446}, year = {2025}, howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Lab (NREL), https://data.openei.org/submissions/8446}, note = {Accessed: 2025-07-09} }

Details

Data from Jul 9, 2025

Last updated Jul 10, 2025

Submission in progress

Organization

National Renewable Energy Lab (NREL)

Contact

Elaine Hale

303.384.7812

Authors

Elaine Hale

NREL

Lixi Liu

NREL

Carlo Bianchi

NREL

Andrew Parker

National Renewable Energy Laboratory NREL

Anthony Fontanini

National Renewable Energy Laboratory NREL

Ry Horsey

National Renewable Energy Laboratory NREL

Noah Sandoval

National Renewable Energy Laboratory NREL

Janet Reyna

National Renewable Energy Laboratory NREL

Dan Thom

National Renewable Energy Laboratory NREL

Meghan Mooney

National Renewable Energy Laboratory NREL

Zack Jensen

NA

Marlena Praprost

National Renewable Energy Laboratory NREL

Amy Van Sant

National Renewable Energy Laboratory NREL

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