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.

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

This webpage contains sources of (1) Data tables of federal agency energy and water consumption; (2) Interactive graphics associated with most data tables; (3) Energy costs by end-use sector and efficiency investment information; (4) Progress toward key goals outlined in the National Energy Conservation Policy Act, as amended (42 U.S.C. 8253-8258); Energy Policy Act of 2005 (42 U.S.C. 15852); (5) Historical data tables of agency energy use and costs by facility and mobility sectors by energy type are also available for fiscal year (FY) 1975 through FY 2020

Facility data includes information on electricity consumption by larger-scale infrastructure, including buildings, solar arrays, and energy storage systems. Parameter definitions can be found in the data dictionary. If a connection between specific vehicle information and facility data exists, it will be available in the vehicle attributes table. Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

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.

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 data illustrates Federal progress in meeting the requirements outlined in Section 432 of the Energy Independence and Security Act of 2007 (EISA 432) (42 U.S.C. 8253(f)). The data is accessible through the FEMP EISA 432 Compliance Tracking System, which offers: (1) Top-tier agency aggregates, representing all reported data subject to the EISA 432 requirements; (2) Facility-level detailed data that excludes information for facilities that have requested exemption from public disclosure for national-security purposes.

A list of the department's datasets assessed for current release to the open data portal in line with the department's Open Data Strategy.

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.

Abstract

Census tract level data of the Low-Income Energy Affordability Data (LEAD) Tool created by Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy. Curated by Carnegie Mellon University Libraries.

Methodology

https://lead.openei.org/docs/LEAD-Tool-Methodology.pdf

Usage

License: Creative Commons Attribution

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).

On October 1, 1977, the Department of Energy became the twelfth cabinet-level department in the Federal Government. The new Department of Energy brought together within one agency two separate programmatic traditions that had long coexisted within the federal establishment. The first tradition consisted of a loosely knit amalgamation of agencies, offices, and commissions scattered throughout the Federal Government dealing with various aspects of non-nuclear federal energy policy and programs. These included energy research, development, regulation, pricing, and conservation. Although the Federal Government had been involved in various energy programs for decades, the many entities responsible for energy research, development, production, or regulation usually had not coordinated their activities or policies. The second tradition consisted of the Federal Government's activities in the field of nuclear energy. Beginning with World War II and the Manhattan Project effort to build the atomic bomb, the Federal Government dominated the development of nuclear energy in the United States. Bureaucratically centralized and security-oriented, federal involvement was almost exclusively of a military nature until the mid-1950s when the Atomic Energy Commission began major efforts to commercialize nuclear power.

Additional information reported in lieu of inclusion in the annual report: consultancies, overseas travel, Queensland Language Services Policy. Read the complete annual reports: https://www.epw.qld.gov.au/news-publications/annual-report

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.

Department of Energy and Climate performance reporting on the Queensland Government On-Time Payment Policy.

This project provides a national unified database of residential building retrofit measures and associated retail prices and end-user might experience. These data are accessible to software programs that evaluate most cost-effective retrofit measures to improve the energy efficiency of residential buildings and are used in the consumer-facing website https://remdb.nrel.gov/

This publicly accessible, centralized database of retrofit measures offers the following benefits:

• Provides information in a standardized format
• Improves the technical consistency and accuracy of the results of software programs
• Enables experts and stakeholders to view the retrofit information and provide comments to improve data quality
• Supports building science R&D
• Enhances transparency

This database provides full price estimates for many different retrofit measures. For each measure, the database provides a range of prices, as the data for a measure can vary widely across regions, houses, and contractors. Climate, construction, home features, local economy, maturity of a market, and geographic location are some of the factors that may affect the actual price of these measures.

This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

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.

1. Data content: Presenting the statistics of gross electricity generation produced by various power generation techniques in Taiwan over the past 21 years, including pumped storage hydro, thermal (coal, oil, gas), nuclear, and renewable energy (conventional hydro, geothermal, solar photovoltaic, wind, biomass, waste) from national, state-owned, privately-owned power plants, and self-generating facilities.2. Collection purpose: To serve as the basis for energy-related policy planning and management in Taiwan.3. Collection methods:(1) Presenting the statistics of gross electricity generation from power generation and self-generating facilities as defined in the Electricity Act.(2) Electricity generation (national): Recording the gross electricity generation of various power generation techniques in Taiwan, including the total electricity generation reported by power generation, self-generating facilities, and electricity sales (third-party renewable energy power generation facilities).(3) Electricity generation (Taiwan Power Company): Recording the gross electricity generation of various power generation techniques by Taiwan Power Company, including the total electricity generation reported by the company.(4) Electricity generation (privately-owned power plants): Recording the gross electricity generation of various power generation techniques by privately-owned power plants (excluding Taiwan Power Company), including the total electricity generation reported by these entities.(5) Electricity generation (self-generating facilities): Recording the gross electricity generation of various power generation techniques by self-generating facilities, including the total electricity generation reported by these facilities and electricity sales (third-party renewable energy power generation facilities).

The vehicle type energy consumption certificate is compiled by this department each month. It includes energy consumption certificate data for various types of new vehicles, including brand, vehicle type, displacement, energy consumption standards, fuel consumption test values, energy efficiency grades, etc. (The fuel consumption test values recorded in the data set are tested by an accredited testing laboratory according to internationally recognized procedures. The results may not necessarily be consistent with the fuel consumption values of vehicles on general roads. Users are reminded to have an understanding of the background of the generated data to avoid misunderstanding).