This is a link where the U.S. Department of Energy DATA Act reporting can be found.

This data is aligned to eligibility criteria outlined in the United States Department of Energy (DOE) 2023 Communities LEAP (Local Energy Action Program). Please visit the LEAP website (https://www.energy.gov/communitiesLEAP/communities-leap) to learn more about LEAP and gain additional contextual information for how these data may be used. The data provided approximates how the eligibility criteria apply at the census tract level across the United States. This EDX submission provides access to information pertaining to each of the four eligibility criteria outlined (average energy burden, percent low income, communities with a historic economic dependence on fossil fuel industrial facilities, and disadvantaged communities) for all census tracts within the 50 U.S. States, the District of Columbia (D.C.), and Puerto Rico. This information can be access in a detailed excel spreadsheet or through the linked interactive web application (https://arcgis.netl.doe.gov/portal/apps/experiencebuilder/experience/?id=2a77f443d72b4a4d82474b3ffe33b8cd). Please note that while these data are provided at the census tract level, census tracts do not necessarily have the same physical boundaries as a community but were used as they provide the closest proxy based on publicly available information collected using an empirically robust method. U.S. territories are not listed but are eligible to apply to Communities LEAP. As stated in the Opportunity Announcement, applying communities should describe how they meet the eligibility criteria in their application even if these data do not specifically show that they are eligible. These data align to the White House’s The Interagency Working Group on Coal and Power Plant Communities and Economic Revitalization, https://energycommunities.gov/.

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States: Data (846.3MB .csv) - minimalist list of each building (rows) for the following fields (columns) • ID - unique building ID • Centroid - building center location in latitude/longitude (from Footprint2D) • Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) • State_abbr - state name • Area - estimate of total conditioned floor area (ft2) • Area2D - footprint area (ft2) • Height - building height (ft) • NumFloors - number of floors (above-grade) • WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) • CZ - ASHRAE Climate Zone designation • BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards • Standard - building vintage • Sample Models (114GB.zip by county) - OpenStudio and EnergyPlus building energy models named according to ID This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

Data released under the Department of Energy's (DOE) Open Energy Data Initiative (OEDI). The Open Energy Data Initiative aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s programs, offices, and national laboratories. OEDI aims to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation.

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.

To further transparency and openness, DOE established a policy to document and post online all CX determinations involving classes of actions listed in Appendix B to Subpart D of the DOE NEPA regulations (10 CFR Part 1021). This raw data set contains CX determinations required to be posted under the policy, and also some for which documentation and posting are optional, i.e., determinations involving classes of actions listed in Appendix A or made before the policy's effective date of November 2, 2009. The data set includes information by state, CX applied, date range, DOE Program, Field, or Site Office, keyword, and whether the CX determination is for a project related to the American Recovery and Reinvestment Act (Recovery Act or ARRA) of 2009. The web address to the CX determination documents are provided. This data set will be updated approximately monthly. See www.gc.doe.gov/NEPA/categorical_exclusion_determinations.htm for information on DOE CX procedures. For further information on DOE's NEPA compliance program, see www.gc.energy.gov/nepa or email: askNEPA@hq.doe.gov.

DATA HAS BEEN MIGRATED TO https://data.ess-dive.lbl.gov/view/doi:10.15485/2283980

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".

Data, separated by state - minimalist list of each building (rows) for the following fields (columns)

ID - unique building ID

Footprint2D - lat/lon vertices of building footprint

State_Abbrev - Abbreviation for the from which building is located

Area - estimate of total conditioned floor area (ft2)

Area2D - footprint area (ft2)

CZ - ASHRAE Climate Zone designation

Height - building height (ft)

NumFloors - number of floors (above-grade)

WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)

CZ - US climate zone designation

BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards

Standard - building vintage (determined by building age)

This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA).

The following submission includes raw and processed data from the 2024 Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) belt tests conducted using NREL's Large Amplitude Motion Platform (LAMP). A description of the motion profiles run during testing can be found in the run log document. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in the form of TDMS files. Data was then processed using Python and MATLAB and converted to MATLAB workspace, parquet, and csv file formats. During Data processing, a low pass filter was applied to each array and the arrays were then resampled to common 10Hz timestamps. A MATLAB data viewer script is provided to quickly visualize these data sets. The following arrays are contained in each test data file: - Time: Unix seconds timestamp - Test_Time: Time in seconds since beginning of test - POS_OS_1001: Encoder position in degrees (the encoder is located on the secondary shaft of the spring return and is driven by the winch after a 4.5:1 gear reduction) - LC_ST_1001: Anchor load cell data in lbf - PRESS_OS_2002: Air spring pressure in psi This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

Laws and Incentives Data provides a comprehensive collection of information on various federal, state, and local laws, incentives, and policies aimed at promoting clean energy, alternative fuels, and energy efficiency. This includes regulations and incentives related to electric vehicles (EVs), alternative fuel infrastructure, renewable energy adoption, and energy efficiency measures.

This statistic shows the outlays of the U.S. Department of Energy in fiscal years 2000 to 2019, with estimated data until 2025. The U.S. Department of Energy had outlays of about 28.94 billion U.S. dollars in 2019.

Commercial reference buildings provide complete descriptions for whole building energy analysis using EnergyPlus (see "About EnergyPlus" resource link) simulation software. Included here is data pertaining to the reference building type "Large Hotel" for each of the 16 climate zones described on the Wiki page (see "OpenEI Wiki Page for Commercial Reference Buildings" resource link), and each of three construction categories: new (2004) construction, post-1980 construction existing buildings, and pre-1980 construction existing buildings.

The dataset includes four key components: building summary, zone summary, location summary and a picture. Building summary includes details about: form, fabric, and HVAC. Zone summary includes details such as: area, volume, lighting, and occupants for all types of zones in the building. Location summary includes key building information as it pertains to each climate zone, including: fabric and HVAC details, utility costs, energy end use, and peak energy demand.

In total, DOE developed 16 reference building types that represent approximately 70% of commercial buildings in the U.S.; for each type, building models are available for each of the three construction categories. The commercial reference buildings (formerly known as commercial building benchmark models) were developed by the U.S. Department of Energy (DOE), in conjunction with three of its national laboratories.

Additional data is available directly from DOE's Energy Efficiency & Renewable Energy (EERE) website (see "About Commercial Buildings" resource link), including EnergyPlus software input files (.idf) and results of the EnergyPlus simulations (.html).

Note: There have been many changes and improvements since this dataset was released. Several revisions have been made to the models and moved to a different approach to representing typical building energy consumption. For current data on building energy consumption please see the ComStock resource below.

description: The data provided in this Excel spreadsheet is the final data that was collected through the Smart Grid Data Hub and provided by the individual Smart Grid Investment Grant (SGIG) project recipients. The project recipients represent utilities from across the country who received funding from the Smart Gird American Recovery and Reinvestment Act (ARRA) Program and the data provides details regarding the deployment of key assets and expenditures by ARRA supported smart grid projects. This ARRA Program was managed by the U.S. Department of Energy's Office of Electricity Delivery and Energy Reliability.; abstract: The data provided in this Excel spreadsheet is the final data that was collected through the Smart Grid Data Hub and provided by the individual Smart Grid Investment Grant (SGIG) project recipients. The project recipients represent utilities from across the country who received funding from the Smart Gird American Recovery and Reinvestment Act (ARRA) Program and the data provides details regarding the deployment of key assets and expenditures by ARRA supported smart grid projects. This ARRA Program was managed by the U.S. Department of Energy's Office of Electricity Delivery and Energy Reliability.

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.

The following submission includes raw and processed electrical configuration deployment data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files, and MATLAB workspaces. This dataset was collected in April 2024 at the Jennette's pier test site in North Carolina. Raw data as TDMS, CSV, and bag files are provided here alongside processed data in the form of MATLAB workspaces and Parquet files. This dataset includes the Python code used to process the data and MATLAB scripts to visualize the processed data. All data types, calculations, and processing is described in the included "Data Descriptions" document. All files in this dataset are described in detail in the included README. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

This webpage provides links to the current and past U.S. Department of Energy budgets.

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.

Released to the public as part of the Department of Energy's Open Energy Data Initiative, this is the highest resolution publicly available long-term wave hindcast dataset that – when complete – will cover the entire U.S. Exclusive Economic Zone (EEZ).

The Building Energy Data Book (2011) is a compendium of data from a variety of data sets and includes statistics on residential and commercial building energy consumption. Data tables contain statistics related to construction, building technologies, energy consumption, and building characteristics. The Building Technologies Office (BTO) within the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy developed this resource to provide a comprehensive set of buildings- and energy-related data.

The Data Book has not been updated since 2011.

The data sets comprising the Data Book are now publicly available in user-friendly formats and you can use them to find data relevant to your questions. Please find below a list of Energy Information Administration (EIA) data sets that BTO consults:

• Annual Energy Outlook

  Projections in the Annual Energy Outlook 2015 (AEO2015) focus on the factors expected to shape U.S. energy markets through 2040.

• Monthly Energy Review

  A publication of recent and historical energy statistics.

• Residential Energy Consumption Survey (RECS)

  A nationally representative sample of housing units that specially trained interviewers collect energy characteristics, usage patterns, and household demographics.

• Commercial Buildings Energy Consumption Survey (CBECS)

  A national sample survey that collects information on the stock of U.S. commercial buildings, including their energy-related building characteristics and energy usage data.

Questions about the above resources can be directed to the relevant EIA subject matter expert.

In August 1995, the Office of Fossil Energy (Fossil Energy) initiated an analysis of the Department of Energy's (DOE or the Department) Fiscal Year (FY) 1995 oil and gas research, development, and demonstration (RD&D) programs. The purpose of this analysis was to determine the degree to which DOE's programs address industry's priority technology needs as identified in the National Petroleum Council's (the Council or NPC) study entitled, "RD&D Needs of the Oil and Gas Industry" (August 1995). The Council is an advisory body to the Secretary of Energy on matters relating to oil and natural gas or the oil and gas industries. The Council's study provides a listing of 176 industry technology RD&D needs, of which 36 are designated as high priority. An overview of the Council's study, including its methodology and recommendations, is provided in this report. Based on this program analysis, the Department will examine all high priority technology areas to determine whether there is an appropriate Federal RD&D role under current Departmental policy, regulation, and statutory mandates. In an ongoing dialogue with the industry, DOE will redirect current programs, as appropriate to DOE's mission, and identify cooperative RD&D opportunities with industry in the high priority areas.