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.

This dataset, compiled by NREL using data from ABB, the Velocity Suite (http://energymarketintel.com/) and the U.S. Energy Information Administration dataset 861 (http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the OpenEI U.S. Utility Rate Database (https://openei.org/apps/USURDB/).

description: Open Energy Information (OpenEI) is a knowledge-sharing online community dedicated to connecting people with the latest information and data on energy resources from around the world. Created in partnership with the United States Department of Energy and federal laboratories across the nation, OpenEI offers access to real-time data and unique visualizations that will help you find the answers you need to make better, more informed decisions with structured linked open data and information in widely-used formats such as API, CSV, XML, and XLS. OpenEI is making a profound impact on the world s energy transformation by providing data access, generative data use, key knowledge derivation tools, and synthetic datasets that will help inform policy, purchase, build, and business decisions. This community-based platform is a core competency for the U.S. Department of Energy and its laboratories, providing a high-degree of value for building knowledge and datasets, connecting and structuring data via linked open data standards, and serving as the place for the world to contribute and utilize energy data, APIs and web-services. OpenEI is the backbone to the DOE Data Catalog and federates all DOE-sponsored data upwards to Data.gov in order to enable data transparency and access.; abstract: Open Energy Information (OpenEI) is a knowledge-sharing online community dedicated to connecting people with the latest information and data on energy resources from around the world. Created in partnership with the United States Department of Energy and federal laboratories across the nation, OpenEI offers access to real-time data and unique visualizations that will help you find the answers you need to make better, more informed decisions with structured linked open data and information in widely-used formats such as API, CSV, XML, and XLS. OpenEI is making a profound impact on the world s energy transformation by providing data access, generative data use, key knowledge derivation tools, and synthetic datasets that will help inform policy, purchase, build, and business decisions. This community-based platform is a core competency for the U.S. Department of Energy and its laboratories, providing a high-degree of value for building knowledge and datasets, connecting and structuring data via linked open data standards, and serving as the place for the world to contribute and utilize energy data, APIs and web-services. OpenEI is the backbone to the DOE Data Catalog and federates all DOE-sponsored data upwards to Data.gov in order to enable data transparency and access.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022).

These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data.

Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period.

Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented.

Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region.

One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold).

The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock.

Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

This file contains a list of relevant references on value of information (VOI) in RIS format. VOI provides a quantitative analysis to evaluate the outcome of the combined technologies (seismology, hydrology, geodesy) used to monitor Brady's Geothermal Field.

The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States.

The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page.

For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below.

No filters have been applied to the raw WRF output.

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only.

The following variables were extracted from the WRF model data: - Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Pressure at 0, 100, 200 m - Surface Precipitation Rate - Surface Relative Humidity - Inverse Monin Obukhov Length

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This submission supersedes submission MHKDR-483

This submission file contains the files that are needed to simulate NREL's HERO WEC (hydraulic and electric reverse osmosis wave energy converter). This requires the user to have already installed WEC-Sim. In addition to the standard toolboxes that are required to run WEC-Sim the user will also need the Simscape Fluids and Simscape Driveline packages.

The zip file (HERO_V1_WECSim_2024.zip) contains the following: - HERO_HPTO_2024.slx: Simulink-based WEC Sim model of the first gen (V1.0) Hydraulic PTO (power take-off) that was designed for the HERO WEC. This model has been updated since submission #483 based on in-laboratory experimental results. - wecSimInputFile.m: Input file needed to run the model - userDefinedFunctionsMCR.m: MCR (multi condition run) script that is needed if a use wants to simulate multiple wave conditions. - geometry (folder): Includes the geometry file that is needed for visualization - hydroData (folder): Includes the required WAMIT data to run WEC-Sim -HydVisualization.mlx: Visualization script to plot simulation results (not needed to run)

This submission contains links to Geothermal Areas on OpenEI that were completed as part of an effort to gather clean, unbiased information on which to build geothermal drilling prospects. The specific areas that were part of this focused effort, or case studies, are linked individually, and also available for download as a table in CSV format.

The Geothermal Areas exist live on OpenEI and are constantly evolving with updated information. Snapshots of both the specific case study areas and of all geothermal areas on OpenEI, from the time of submission, have been included in CSV format. For the most up-to-date information, please use the provided All Geothermal Areas link to view these on OpenEI.

This dataset also includes a link to the Geothermal Exploration Overview page on OpenEI, which provides exhaustive detail on the activities cataloged for these case studies and their references, as well as the technologies employed in each geothermal area.

The compressed (.zip) file contains Datawell MK-III Directional Waverider binary and unpacked data files as well as a description of the data and manuals for the instrumentation. The data files are contained in the two directories within the zip file, "Apr_July_2012" and "Jun_Sept_2013". Time series and summary data were recorded in the buoy to binary files with extensions '.RDT' and '.SDT', respectively. These are located in the subdirectories 'Data_Raw' in each of the top-level deployment directories. '.RDT' files contain 3 days of time series (at 1.28 Hz) in 30 minute "bursts". Each '.SDT' file contains summary statistics for the month indicated computed at half-hour intervals for each burst. Each deployment directory also contains a description (in 'File.list') of the Datawell binary data files, and a figure ('Hs_vs_yearday') showing the significant wave height associated with each .RDT file (decoded from the filename). The corresponding unpacked Matlab .mat files are contained in the subdirectories 'Data_Mat'. These files have the extension '.mat' but use the root filename of the source .RDT and .SDT files.

The objective of this project is to detect and locate microearthquakes to aid in the characterization of reservoir fracture networks. Accurate identification and mapping of the large numbers of microearthquakes induced in EGS is one technique that provides diagnostic information when determining the location, orientation and length of underground crack systems for use in reservoir development and management applications. Conventional earthquake location techniques often are employed to locate microearthquakes. However, these techniques require labor-intensive picking of individual seismic phase onsets across a network of sensors. For this project we adapt the Matched Field Processing (MFP) technique to the elastic propagation problem in geothermal reservoirs to identify more and smaller events than traditional methods alone.

Overview This is the data collected during the validation period. The buoy is scheduled to be deployed near Martha's Vinyard in mid-January 2020. Data collected from buoy instruments are contained in two files labeled “primary” and secondary.” Header information for these files can be found in the respective tabs in the Excel spreadsheet under Attachments. Data Details Add information about buoy data files here.

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios.

Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this.

The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Note: v0.2.2 was released on May 7th 2025 for preliminary use while under peer review. Files and folders with a preliminary tag are subject to change in response to review feedback. The preliminary tag and this note will be removed upon final release.

These are the data collected during the validation period. The buoy is scheduled to be deployed near Martha's Vinyard in mid-January 2020. The Leosphere Windcube 866 lidar systems output 5 different compressed data files. Compression is explained below.

In 2009, National Grid started a DER pilot program that offered technical support and financial incentives to qualified Massachusetts homeowners who planned and successfully completed a retrofit that incorporated the performance requirements and goals of the National Grid DER measures package. This DER measures package, developed through collaboration with Building Science Corporation (BSC), includes specific thermal and airtightness goals for the enclosure components as well as health, safety, durability, and indoor air quality requirements. By providing measures that can be included with common renovation activities such as roof replacement, window replacement, re-siding, basement remediation, and remodeling, this DER measures package is expected to have widespread application for existing homes in the New England area. The post-retrofit performance and cost ranges provided by this research project can provide concrete input for homeowners who are considering a DER. Field test data available for air tightness measured using blower door test. House 1 - Address Belchertown, MA 01007, Notes: Energy Savings: 75%, Company: Clark House 2-1 and 2 - Address (1) Brownsberger, MA 02478 and (2) Belmont, MA 02478, Notes Energy Savings: 73%, Company: Brownsberger House 3 - Address Millbury, MA 01527, Notes Energy Savings: 31%, Company: Tweedly House 4 - Address Milton, MA 02186 Notes Energy Savings: 42%, Company: Koh House 5 - Address Quincy, MA 02169, Notes Energy Savings: 57%, Company: Hall House 6-1 and 2 - Address Arlington, MA 02476, Notes Energy Savings: 55%, Company: Venable-Hwang House 7 - Address Newton, MA 02459, Notes Energy Savings: 42%, Company: Lavine House 8-1, 2, and 3 - Address Jamaica Plain, MA 02130, Notes Energy Savings: 43%, Company: Buhs House 9 - Address Northampton, MA 01060, Notes Energy Savings: 49%, Company: Wick House 10 - Address Lancaster, MA 01523, Notes Energy Savings: 40%, Company: Habitat for Humanity of North Central Massachusetts House 11 - Address Brookline, MA 02445, Notes Energy Savings: 27%, Company: Aquiline House 12 - Address Westford, MA 01886, Notes Energy Savings: 30%, Company: Atkins House 13 - Address Gloucester, MA 01930, Notes Energy Savings: 35%, Company: Cunningham

This submission includes data collected from experiments on the performance of rare earth adsorption by immobilized bacteria that accompany the FY18 Q2 and Q3 quarter reports. Relevant information from these reports is included in a resource below. The spreadsheet below includes data from the following three experiments: REE Bioadsorption from buffer solution by Caulobacter biofilms. REE Bioadsorption from mock geothermal fluids by Caulobacter biofilms. REE biosorption capacity and its temperature dependence with Mutag Biochips.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics.

Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document).

For more information, and to access the interactive LEAD Tool platform, please visit the "4. LEAD Tool Platform" resource link below.

For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "5. CELICA Website" resource below.

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs.

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.