The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities.

The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel.

A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.

Data is associated with Report "ResStock Communities LEAP Pilot Residential Housing Analysis - Detailed Methodology" by Lixi Liu, Jes Brossman, Yingli Lou technical report number NREL/TP-5500-88058. Results outline the potential impacts of the energy efficiency and electrification retrofit packages on communities’ housing stock.

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

These data tables are cross-tabulations that provide the distribution of households by household income, converted to Federal Poverty Level (FPL) or Area Median Income (AMI) bins, and tenure status (owner or renter), specified by location and various household characteristics including vintage, size (floor area), cooling system type, and heating fuel type. These household characteristics were identified as strongly correlated with household income. All of the tables are tab-separated value (.tsv) formatted and gzip compressed. Four versions of the tables are provided: 1) using AMI bins and including only single-family detached homes (files beginning with AMI_SFD)2) AMI bins with all home types (files beginning with AMI_MF)3) FPL bins with only single-family detached homes (files beginning with FPL_SFD)4) FPL bins with all home types (files beginning with AMI_MF). Additional detail on the contents of these files can be found in the README.

The U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described here. This dataset includes load profiles for both the baseline building stock and the building stock with various energy efficiency, electrification, and demand flexibility upgrades applied.

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 ResStock and ComStock, which are building stock energy models of the US residential and commercial sector, respectively, and are 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 the technical report linked below. 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 (see "dsgrid TEMPO" link below). 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 power sector 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.

A set of TMY3 EPW weather files for each county of the U.S.

A set of TMY3 EPW weather files for each county of the U.S. NOTE: A file was uploaded on 2022/10/05 with additions to address known missing data for Nolan County, TX (FIPS: 48353), Fisher County, TX (FIPS: 48151), and Stonewall County, TX (FIPS: 48433).

These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including Scout, ResStock, and the Commercial Building Prototype Models, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020. Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.

The four ZIP files that make up this data record are interpreted as follows:

Measure_Data.zip: Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available here.

Results_Data.zip: Includes the main and side case results data. Baseline-case outcomes, which are consistent with the EIA 2019 Annual Energy Outlook, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in each of these first three folders. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) regions (p.6) of focus are stored in "Sector_Level_8760s".

Source_Code.zip: Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout Quick Start Guide; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described here. See Scout's Local Execution Tutorials for more details on how to develop Scout inputs and outputs.

Supporting_Data.zip: Includes supplemental data files provided by EIA that describe key inputs and outputs to the Electricity Market Module in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json".

A system-generated layer that combines the Management Coupes and Restock Area layers to give a view of how and when the forest structure will change over time. This layer is used by the Production Forecast tool and to create Future Forest sub-compartments.

A 2022 survey revealed that most digital buyers from the United States and the United Kingdom (UK) preferred to be notified via email when an apparel item they are interested in is back in stock, with ** percent of respondents reporting this. Additionally, ** percent of respondents preferred to be notified via text message. Only ** percent of respondents opted for the option to have the item auto-added to their shopping carts.

This is weather data required for use in building energy simulations, including ResStock and OpenStudio-HPXML workflows.

A complete list of live websites using the Hengam ReStock technology, compiled through global website indexing conducted by WebTechSurvey.

Datasets for residential appliances energy usage/efficiency. The Federal Trade Commission (FTC) collects manufacturer data about the energy use and efficiency of appliances. All of the data was submitted by manufacturers under the FTC's Appliance Labeling Rule (see "FTC Appliance Label Ruling" resource below). For more information about what is included in the individual datasets see the resource descriptions. For more up to date residential energy information, please visit the ResStock resource linked below.

Comprehensive statistics and key metrics for RPC, Inc. (RES) including valuation ratios, profitability metrics, and financial data.

OCHRE™ uses a variety of input data sources to run time-series simulations. Building models can be taken from the ResStock™ database or generated using the Building Energy Optimization Tool (BEopt™) or other OpenStudio-HPXML workflows. EV charging profiles can be taken from datasets used in NREL's 2030 National Charging Network project. Weather data can be taken from the National Solar Radiation Database or EnergyPlus® weather files. There are no public datasets with OCHRE outputs at this time. However, a recent project dataset on water heater and EV demand flexibility can be requested. OCHRE is a Python-based energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, EVs, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

RPC reported $117.7M in Stock for its fiscal quarter ending in June of 2025. Data for RPC | RES - Stock including historical, tables and charts were last updated by Trading Economics this last August in 2025.

RPC stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. This dataset provides future weather data under two emissions scenarios - RCP4.5 and RCP8.5 - across two 10-year periods (2045-2054 and 2085-2094). It also includes simulated historical weather data for 1995-2004 to serve as the baseline for climate impact assessments. We strongly recommend using this built-in baseline rather than external sources (e.g., TMY3) for two key reasons: (1) it shares the same model grid as the future projections, thereby minimizing geographic-averaging bias, and (2) both historical and future datasets were generated by the same RCM, so their differences yield anomalies largely free of residual model bias. This dataset offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The authors observed an anomalous warming signal over the Great Plains in the end-of-century (2085 - 2094) RCP4.5 time slice. This anomaly is absent in the mid-century slice (2045 - 2054) under RCP4.5 and in both the mid- (2045 - 2054) and end-of-century (2085 - 2094) slices under RCP8.5. Consequently, we recommend that users exercise particular caution when using the RCP4.5 2085-2094 data, especially for analyses involving the Great Plains region.

This is a backup of raw data sets used by PyPSA-USA Sector that are not archived in other places. The only reason to use these datasets directly is if the underlying dataset goes offline. Included in this deposit is;

• ResStock End Use Load Profiles (Licensed under CC-BY-4.0)
• ComStock End Use Load Profiles (Licensed under CC-BY-4.0)