Watch a how-to demonstration video: End-Use Load Profiles Dataset Access Demonstration
End Use Savings Shapes (2022.1)
End-Use Load Profiles for the U.S. Building Stock (2021.1)
The residential (ResStock) dataset represents dwelling units in the contiguous United States, including single-family, multi-family (including high rise multi-family), and mobile homes. It does not include dormitories, prisons, assisted care facilities, and other congregate housing situations.
The commercial (ComStock) dataset represents 14 of the most common commercial building types – small office, medium office, large office, retail, strip mall, warehouse, primary school, secondary school, full-service restaurant, quick-service restaurant, small hotel, large hotel, hospital, and outpatient – which comprise about 65% of the commercial sector floor area in the United States according to CBECS.
The building stock represents, as closely as possible, the U.S. building stock as it was in 2018. The building stock representation is the same regardless of the weather data used.
It depends on your application. Many applications just need an aggregate load shape. If analyzing scenarios that require realistic spikiness of individual dwelling unit or building loads, such as behind-the-meter solar plus storage, rate design involving real-time pricing or demand charges, or distribution system impacts, then we recommend using individual building profiles. These recommendations will be discussed in more detail in a forthcoming Applications and Opportunities report.
Descriptions of each of the building characteristics filters and the end-use categories can be found in the data_dictionary.tsv file (example). Descriptions of the values used in those filters can be found in the enumeration_dictionary.tsv (example). Both files can be opened with Excel or a text editor. Each dataset has its own data_dictionary.tsv and enumeration_dictionary.tsv file, linked to from the Datasets page.
The list of included end-use categories can be found in each dataset’s data_dictionary.tsv file (example).
In each of the published datasets there is a spatial lookup table: spatial_tract_lookup_table.csv (example). County and PUMA codes can be looked up using the "nhgis_county_gisjoin" and "nhgis_puma_gisjoin" columns, respectively. If you want to find the pre-aggregated timeseries file for a county or PUMA, you can use this lookup to find the code for the county of interest. To find the 5-digit PUMA code based on a city or place name, use this file from the U.S. Census Bureau: 2010_PUMA_Names.pdf.
Aggregating by California climate zone is available for residential building profiles but not commercial building profiles.
To achieve this aggregation for the residential load profiles, use the resstock.nrel.gov website and select the dataset and region of interest (Example: the ResStock End Use Savings Shapes by State 2018 dataset and the state of California.) At the bottom click the “Explore Timeseries” button. At the left side, halfway down, click the button “+ Add Filters”. In the “Filters” column, find and select the “Cec climate zone” filter. The options available are for CEC Climate Zones 1-16. Do not use the “None” option, as the option is for locations outside the state of California.
There are two ways to access the building characteristics data associated with an aggregate load profile:
The filename of the individual building (or dwelling unit) load profile's parquet files contains the building ID. Each of these building IDs corresponds to a row in the dataset's metadata, which is available in either .parquet or .tsv format (tab-separated value format that can be opened in Excel) (ResStock example) (ComStock example).
All downloaded energy data is in kWh, including all electricity, natural gas, propane, and fuel oil end uses, as documented in the data_dictionary.tsv files (example).
Timeseries energy consumption data viewed on the website are in metric units. The metric prefix is on the y-axis label (T for tera, G for giga, M for mega, etc.) and the rest of the unit information is in the y-axis label.
The timestamps of all load profiles have been converted to Eastern Standard Time, to prevent issues when aggregating across time zones.
The underlying modeling was conducted using local standard time for each location, with occupant schedules adjusted for daylight savings as applicable. All EnergyPlus timeseries outputs were converted from local standard time to Eastern Standard Time for publication in the web data viewer, data viewer exports, timeseries aggregates, and individual timeseries parquet files. In converting from local Standard Time to Eastern Standard Time, if necessary the last few hours of each dataset were moved to the beginning of the timeseries. For example, the first two hours of data from Colorado in Eastern Standard Time (Jan 1, midnight to 2 AM) were originally modeled as the last two hours of the year in Mountain Standard Time (Dec 31, 10 PM to midnight) using the corresponding weather.
Yes. The aggregates represent the total relevant building stock (e.g. all small office buildings in the state of Colorado), not just the sum of the model results. In other words, the aggregates represent the total “floor_area_represented” for commercial or “units_represented” for residential.
Parquet files can be read using programming languages such as Python, using the pyarrow package. For other options, see https://arrow.apache.org/docs/index.html. There are a few third-party graphical tools for viewing parquet files, but we have not tested them and the third-party support is limited.
There are no plans for an API. However, we are currently developing documentation that will explain how to link one’s own Amazon Web Services account to this data, so the data can be queried by analytic tools like Athena. We will also be providing example SQL queries to help facilitate analyses.
No, we do not currently model EV charging in the dataset. For modeling aggregate EV load profiles for a city or state, we suggest using EVI-Pro Lite. Measured charging profile data for individual homes can be found in the NEEA HEMS data and Pecan Street Dataport. Email us at load.profiles@nrel.gov if you have suggestions for other EV charging data sources.
Users should estimate standard error for metrics of interest using the standard deviation divided by the square root of the number of samples (i.e., profiles or models). As discussed in the methodology report (section 5.1.3), for residential units, a good rule of thumb is to use at least 1000 samples to maintain 15% or less sampling discrepancy for common quantities of interest. Queries in sparsely populated areas or with filters applied may have relatively few samples available. In these cases, samples from similar locations can be grouped to increase the sample size.
As an example, if one is interested in the mean change in annual electricity costs in a certain county under a potential new rate structure and 500 samples are available in that county, the costs should be calculated for all 500 samples and the standard deviation of those costs can be used to calculate the standard error of the mean change in annual electricity costs.
Weather data used for the modeling have been provided in .csv format for regression modeling, forecasting, or other analyses. The TMY3 weather files in EnergyPlus input format (EPW) can be downloaded from the NREL Data Catalog, with filenames that correspond to county IDs in the ResStock/ComStock metadata.
EPW format weather files for 2018 or other actual meteorological years have not been publicly released. These files can be purchased from private sector vendors. See https://energyplus.net/weather/simulation for a list of providers.
We query data in real time to produce the time series graphs you see on the webpage, and this can involve scanning terabytes (TB) of data. Running a baseline-only query for California, Texas, New York, or Illinois takes around a minute, while running a query for a state like Colorado or Massachusetts takes about 10-20 seconds. However - if the graphs have previously been generated we have the data cached and can typically load the data in a few seconds. That's why the load time varies.
The “Explore Timeseries” option is available once a specific geography (e.g. state or PUMA region) is selected.
Clicking on the end uses in the legend will toggle their inclusion in the visualization.
Choose “Export csv” and “15 minute resolution”. The resulting csv file will have 15 minute end use load profiles that are not aggregated over time.
The timestamp indicates the end of each 15-minute interval. So "12:15" represents the energy use between 12:00 and 12:15.
The 'sum' aggregation is the total energy consumption for all buildings that meet the filter criteria across all the occurrences of the given time step within the selected month(s). For example, in a day timeseries range for a specific state for the month of July, the 7-7:15 AM hour time step shows the sum of all energy consumption statewide between 7-7:15 AM in July, from buildings that meet the filter criteria. The value in that timestamp would be approximately 1/96th of the total statewide energy consumption in that month in that sector. The ‘sum’ view has fewer uses than the ‘average’ view.
The 'average' aggregation is the total energy consumption for all buildings that meet the filter criteria, averaged across all the occurrences of the given time step within the selected month(s). For example, in a day timeseries range for a specific state for the month of July, the 7-7:15 AM hour time step shows the average statewide energy consumption between 7-7:15 AM in July, from buildings that meet the filter criteria. The ‘average’ aggregation provides a view of the average day of total energy consumption in the state. This is the more logical view for most use cases.
Note that while each time step within a day or a year has the same number of occurrences within each dataset, each time step for a week does not - some days of the week occur more times than others in each year or month range (except for February).
The viewer allows aggregations of up to six locations (states or PUMAs, depending on the dataset). When viewing a single location, choose the “+ More Locations” option, add up to five additional locations, and choose “Update Search”.
Sums of more than six locations can be created manually by downloading sums of up to six locations and summing further on your local computer.
TMY3 weather is not aligned between locations. This does not affect our recommendations for working with annual data. However, if your application requires timeseries data and therefore would benefit from aligned weather, we recommend either using an AMY dataset, or filtering by weather station and summing only within a single weather station’s PUMAs.
Downloading a csv of the relevant data is the best approach. The data visualizations in the web viewer that include PV have some UI limitations. We are also aware that the plot axes cut off negative values.
The peak day is the day with the highest single-hour (peak) energy consumption. The min peak day is the day with the lowest single-hour energy. The peak_day or min_peak_day aggregation type is only available when month constraints are not used.
This is not currently available in the web interface, but you can use the interface to download the full year of 15-min data and see which day is the peak day.
Pre-aggregated files For commercial buildings, the pre-aggregated timeseries files include a floor area column, so it is straightforward to divide energy use by the floor area to get intensity. Floor area is not currently included in the residential aggregates, but the floor area can be calculated from the metadata.tsv file (example), by adding up the values in the "in.sqft" column after filtering down to the building type and geographic region corresponding to the pre-aggregated file.
Data viewer In the data viewer, the bar graphs can show energy use intensity by selecting "energy_consumption_intensity" from the Output drop down menu. Timeseries data for energy use intensity are not directly available, but you can use the Building Characteristics viewer to download floor area values for a filtered subset of buildings and use that to convert timeseries energy use to timeseries energy use intensity.
While the pre-aggregated files (example) contain a column with the "floor_area_represented" for commercial or "units_represented" for residential, aggregations generated by the web viewer don’t include the "floor_area_represented" or "units_represented" information currently. Instead, you can find this information in one of two ways:
This is the total energy consumption by that end use within the selected months.
Yes! Please visit the Datasets page for direct links to each dataset's Data Repository.
Key documentation: dataset release slides; technical documentation
The baseline dataset in the 2022.1 data release is similar to but not identical to the dataset in the 2021.1 release due to improvements to the modeling approach made between the two efforts. These improvements are described in both the dataset release slides linked above and the technical documentation linked on the Datasets page. The 2022.1 release includes savings shapes (also known as measure impact profiles) and emissions impacts for ten measure packages. We recommend using the 2022.1 release for any new work. For work already underway using the 2021.1 release that would not benefit from the measure package or emissions results, updating to the 2022.1 is optional and will have minimal impact on most results.
It is important to note that the dwelling unit models are not the same between the two releases. ID 1 in the 2021.1 release is not related to ID 1 in the 2022.1 release.
The data viewer includes end-use energy consumption information for all end uses and fuels modeled, at a range of different aggregation levels. Carbon emissions outputs and PV outputs (including the PV end use and the net totals) are not available in the data viewer and should be accessed using other avenues (see Datasets page). Additionally, the data viewer is not set up to show individual model results, only results aggregated across models.
We make the HPXML model input files available in the datasets which can be translated into OpenStudio (.osm) and EnergyPlus (.idf) models via the OpenStudio-HPXML workflow. (Use the git tag euss.2022.1 for the version used in EUSS.) The input HPXML and schedule csv files are available in the OEDI Data Lake in the folder “building_energy_models” within each dataset. TMY3 weather files are available on the NREL Data Catalog. 2012 and 2018 AMY files are available for purchase from commercial vendors.
The carbon emissions results represent 1 year of emissions, approximately the average year within the specified lifetime, but it’s a weighted average towards sooner years. More information is available in the technical documentation (see link at the head of this section).
The logic used to assign a water heater location for each modeled dwelling unit is this. For colder climates (IECC 2004 3A, 4A, 4B, 4C, 5A, 5B, 6A, 6B, 7) the water heater is located in the basement in any home that has a basement (whether conditioned or unconditioned) and in living space otherwise. For warmer climates (IECC 2004 1A, 1B, 1C, 2A, 2B, 2C, 3B, 3C) the water heater is located in the garage in any home that has an attached garage and in living space otherwise.
Key documentation: technical report, End-Use Load Profiles for the U.S. Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification.
Water system energy consumption includes all building energy related to to residential water heating and commercial service water heating and pumping.
While the validation effort was largely focused on electricity, we did make some comparisons to annual and monthly EIA survey data for natural gas. These comparisons, which we used to inform the model improvements made during calibration, are published in the technical Methodology and Results report linked at the top of this FAQ. We did not do any timeseries comparisons for propane, fuel oil, or other fuels, although these fuels are included in the models.
All comparisons we completed as part of the calibration and validation effort are published in the technical Methodology and Results report linked at the top of this FAQ. In general, the comparisons are against anonymous hourly utility meter data, EIA monthly/annual data, and various end-use metered datasets.
Yes, there are solar PV profiles in the ResStock data but not the ComStock data.
Not directly. We made OpenStudio model input files (.osm) available in the dataset (ResStock example, ComStock example), which generate the EnergyPlus model input files. The residential models require external schedule .csv files (example).
Update your browser to view this website correctly. Update my browser now