100+ datasets found
  1. o

    Department of Energy's Open Energy Data Initiative (OEDI)

    • registry.opendata.aws
    Updated Sep 24, 2020
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    National Renewable Energy Laboratory (2020). Department of Energy's Open Energy Data Initiative (OEDI) [Dataset]. https://registry.opendata.aws/oedi-data-lake/
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    Dataset updated
    Sep 24, 2020
    Dataset provided by
    <a href="https://www.nrel.gov/">National Renewable Energy Laboratory</a>
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    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.

  2. End-Use Load Profiles for the U.S. Building Stock

    • data.openei.org
    • gimi9.com
    • +2more
    data, image_document +1
    Updated Oct 14, 2021
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    Eric Wilson; Andrew Parker; Anthony Fontanini; Elaina Present; Janet Reyna; Rajendra Adhikari; Carlo Bianchi; Christopher CaraDonna; Matthew Dahlhausen; Janghyun Kim; Amy LeBar; Lixi Liu; Marlena Praprost; Philip White; Liang Zhang; Peter DeWitt; Noel Merket; Andrew Speake; Tianzhen Hong; Han Li; Natalie Mims Frick; Zhe Wang; Aileen Blair; Henry Horsey; David Roberts; Kim Trenbath; Oluwatobi Adekanye; Eric Bonnema; Rawad El Kontar; Jonathan Gonzalez; Scott Horowitz; Dalton Jones; Ralph Muehleisen; Siby Platthotam; Matthew Reynolds; Joseph Robertson; Kevin Sayers; Qu Li; Eric Wilson; Andrew Parker; Anthony Fontanini; Elaina Present; Janet Reyna; Rajendra Adhikari; Carlo Bianchi; Christopher CaraDonna; Matthew Dahlhausen; Janghyun Kim; Amy LeBar; Lixi Liu; Marlena Praprost; Philip White; Liang Zhang; Peter DeWitt; Noel Merket; Andrew Speake; Tianzhen Hong; Han Li; Natalie Mims Frick; Zhe Wang; Aileen Blair; Henry Horsey; David Roberts; Kim Trenbath; Oluwatobi Adekanye; Eric Bonnema; Rawad El Kontar; Jonathan Gonzalez; Scott Horowitz; Dalton Jones; Ralph Muehleisen; Siby Platthotam; Matthew Reynolds; Joseph Robertson; Kevin Sayers; Qu Li (2021). End-Use Load Profiles for the U.S. Building Stock [Dataset]. http://doi.org/10.25984/1876417
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    data, website, image_documentAvailable download formats
    Dataset updated
    Oct 14, 2021
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    National Renewable Energy Laboratory (NREL)
    Open Energy Data Initiative (OEDI)
    Authors
    Eric Wilson; Andrew Parker; Anthony Fontanini; Elaina Present; Janet Reyna; Rajendra Adhikari; Carlo Bianchi; Christopher CaraDonna; Matthew Dahlhausen; Janghyun Kim; Amy LeBar; Lixi Liu; Marlena Praprost; Philip White; Liang Zhang; Peter DeWitt; Noel Merket; Andrew Speake; Tianzhen Hong; Han Li; Natalie Mims Frick; Zhe Wang; Aileen Blair; Henry Horsey; David Roberts; Kim Trenbath; Oluwatobi Adekanye; Eric Bonnema; Rawad El Kontar; Jonathan Gonzalez; Scott Horowitz; Dalton Jones; Ralph Muehleisen; Siby Platthotam; Matthew Reynolds; Joseph Robertson; Kevin Sayers; Qu Li; Eric Wilson; Andrew Parker; Anthony Fontanini; Elaina Present; Janet Reyna; Rajendra Adhikari; Carlo Bianchi; Christopher CaraDonna; Matthew Dahlhausen; Janghyun Kim; Amy LeBar; Lixi Liu; Marlena Praprost; Philip White; Liang Zhang; Peter DeWitt; Noel Merket; Andrew Speake; Tianzhen Hong; Han Li; Natalie Mims Frick; Zhe Wang; Aileen Blair; Henry Horsey; David Roberts; Kim Trenbath; Oluwatobi Adekanye; Eric Bonnema; Rawad El Kontar; Jonathan Gonzalez; Scott Horowitz; Dalton Jones; Ralph Muehleisen; Siby Platthotam; Matthew Reynolds; Joseph Robertson; Kevin Sayers; Qu Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    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.

  3. d

    Data from: Sample IEEE123 Bus system for OEDI SI

    • catalog.data.gov
    • data.openei.org
    • +1more
    Updated Jun 15, 2024
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    National Renewable Energy Laboratory (2024). Sample IEEE123 Bus system for OEDI SI [Dataset]. https://catalog.data.gov/dataset/sample-ieee123-bus-system-for-oedi-si
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    Dataset updated
    Jun 15, 2024
    Dataset provided by
    National Renewable Energy Laboratory
    Description

    Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder), but some modifications were made to it to add some solar power modules and measurements on the system. The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation. The readme file included in the S3 bucket provides information about the directory structure and how to use the algorithms. The sensors.json file is used to define the measurement locations.

  4. U.S. Solar Siting Regulation and Zoning Ordinances

    • data.openei.org
    • catalog.data.gov
    data_map, website
    Updated Jun 30, 2022
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    Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan; Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan (2022). U.S. Solar Siting Regulation and Zoning Ordinances [Dataset]. http://doi.org/10.25984/1873867
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    data_map, websiteAvailable download formats
    Dataset updated
    Jun 30, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    National Renewable Energy Laboratory
    Authors
    Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan; Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A machine readable collection of documented solar siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled based on a locality-by-locality review zoning ordinances after completing an initial review of scholarly legal articles. The citations for each ordinance are included in the Solar Ordinances spreadsheet resource below.

  5. d

    Data from: Linearized Distribution Optimal Power Flow for OEDI SI

    • catalog.data.gov
    • data.openei.org
    • +1more
    Updated Oct 19, 2023
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    Pacific Northwest National Laboratory (2023). Linearized Distribution Optimal Power Flow for OEDI SI [Dataset]. https://catalog.data.gov/dataset/linearized-distribution-optimal-power-flow-for-oedi-si
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    Dataset updated
    Oct 19, 2023
    Dataset provided by
    Pacific Northwest National Laboratory
    Description

    This research is to meant to demonstrate the OEDI SI use case for distributed optimal power flow (DOPF). The goal was to formulate the optimal power flow problem in the distribution system for active and reactive power setpoints of PV systems using topology information and voltage measurements. The co-simulation runs every 15 minutes as outlined within the scenario file for the given feeder configuration. The linked GitHub repository includes five federates to achieve DOPF for the small, medium, large, and IEEE 123 feeder scenarios. We are using the OEDI SI framework, as well as the example feeder, sensor, recorder, and estimator federates provided in the example repository for OEDI SI. We also provide a runner script for switching between scenarios.

  6. AlphaBuilding - Synthetic Buildings Operation Dataset

    • data.openei.org
    • catalog.data.gov
    code, data, website
    Updated Dec 21, 2020
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    Han Li; Zhe Wang; Tianzhen Hong; Han Li; Zhe Wang; Tianzhen Hong (2020). AlphaBuilding - Synthetic Buildings Operation Dataset [Dataset]. http://doi.org/10.25984/1784722
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    website, code, dataAvailable download formats
    Dataset updated
    Dec 21, 2020
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    Lawrence Berkeley National Laboratory
    Authors
    Han Li; Zhe Wang; Tianzhen Hong; Han Li; Zhe Wang; Tianzhen Hong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

  7. U.S. Wind Siting Regulation and Zoning Ordinances

    • data.openei.org
    • datasets.ai
    • +2more
    data_map, website
    Updated Jun 30, 2022
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    Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan; Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan (2022). U.S. Wind Siting Regulation and Zoning Ordinances [Dataset]. http://doi.org/10.25984/1873866
    Explore at:
    website, data_mapAvailable download formats
    Dataset updated
    Jun 30, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    National Renewable Energy Laboratory
    Authors
    Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan; Anthony Lopez; Aaron Levine; Jesse Carey; Cailee Mangan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A machine readable collection of documented wind siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled from several sources including, DOE's Wind Exchange Ordinance Database (Linked in the submission), National Conference of State and Legislatures Wind Energy Siting (also linked in the submission), and scholarly legal articles. The citations for each ordinance are included in the Wind Ordinances spreadsheet resource below.

    This data is an updated to a previously developed database of wind ordinances found in OEDI Submission 1932: "U.S. Wind Siting Regulation and Zoning Ordinances"

  8. Data from: PNNL DSSE Docker Image

    • osti.gov
    Updated Jul 10, 2023
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    Bhatti, Bilal; Black, Gary; Reiman, Andrew (2023). PNNL DSSE Docker Image [Dataset]. https://www.osti.gov/dataexplorer/biblio/dataset/1992365-pnnl-dsse-docker-image
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    Dataset updated
    Jul 10, 2023
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    49.0625,-116.1461|45.783082893554,-116.1461|45.783082893554,-125.6272|49.0625,-125.6272|49.0625,-116.1461
    DOE Open Energy Data Initiative (OEDI); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
    Authors
    Bhatti, Bilal; Black, Gary; Reiman, Andrew
    Description

    This is the docker image for Pacific Northwest National Laboratory's DSSE (distribution system state estimator) used for the demo of OEDI-SI platform. To support the operation of modern distribution systems, operators require real-time visibility into system states. Due to a lack of measurements and unbalanced operation, the state estimation in distribution systems is challenging. This submission is related to an OEDI-SI use case which demonstrates the application of an extended Kalman filter-based state estimator on an IEEE-123 bus system. The state estimator uses the measurements to generate voltage estimates for the system.

  9. National Residential Efficiency Measures Database (REMDB)

    • data.openei.org
    • s.cnmilf.com
    • +2more
    data, website
    Updated Sep 29, 2023
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    Nathan Moore; Noel Merket; Scott Horowitz; Micah Webb; Dave Roberts; Brennan Less; Nathan Moore; Noel Merket; Scott Horowitz; Micah Webb; Dave Roberts; Brennan Less (2023). National Residential Efficiency Measures Database (REMDB) [Dataset]. https://data.openei.org/submissions/8336
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    data, websiteAvailable download formats
    Dataset updated
    Sep 29, 2023
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    National Renewable Energy Lab - NREL
    Authors
    Nathan Moore; Noel Merket; Scott Horowitz; Micah Webb; Dave Roberts; Brennan Less; Nathan Moore; Noel Merket; Scott Horowitz; Micah Webb; Dave Roberts; Brennan Less
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  10. United States Utility-Scale PV Supply Curves 2023

    • data.openei.org
    • s.cnmilf.com
    • +2more
    data, image +2
    Updated Jun 30, 2023
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    NREL GDS; NREL GDS (2023). United States Utility-Scale PV Supply Curves 2023 [Dataset]. http://doi.org/10.25984/2428989
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    data, image, image_map, image_documentAvailable download formats
    Dataset updated
    Jun 30, 2023
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    Open Energy Data Initiative (OEDI)
    National Renewable Energy Laboratory
    Authors
    NREL GDS; NREL GDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    This data packet contains supply curves, hourly generation profiles, and a composite siting exclusion TIFF for utility-scale PV across the contiguous United States. The supply curves offer comprehensive metrics such as capacity (MW), generation (MWh), levelized cost of energy (LCOE), levelized cost of transmission (LCOT), and more for each reV site (~60,000 sites). Hourly generation profiles are available for each reV site and can be matched to the available capacity in the supply curve (refer to the Jupyter Notebook). The composite exclusion TIFF is a single file that delineates areas where PV installations are permissible based on various siting assumptions. This data packet contains information for the Reference and Limited siting scenarios.

    For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

  11. Data from: 2024 Annual Technology Baseline (ATB) Cost and Performance Data...

    • osti.gov
    • data.openei.org
    • +1more
    Updated Jun 24, 2024
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    Abou Jaoude, Abdalla; Akindipe, Dayo; Avery, Greg; Cohen, Stuart; Cole, Wesley; Duffy, Patrick; Eberle, Annika; Feldman, David; Fuchs, Becca; Guaita, Nahuel; Hedalen, Ty; Hoffmann, Jeffrey; Joseck, Fred; Kurup, Parthiv; Larson, Levi; Lohse, Chris; Mirletz, Brian; Mulas Hernando, Daniel; Oladosu, Gbadebo; Rakov, Ben; Ramasamy, Vignesh; Roberts, Owen; Rosenlieb, Evan; Schleifer, Anna; Sekar, Ashok; Stehly, Tyler; Stright, Dana; Trivedi, Ishita; Vimmerstedt, Laura; Witter, Eric; Zolan, Alex; Zuboy, Jarett; Zuckerman, Gabriel (2024). 2024 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies [Dataset]. https://www.osti.gov/dataexplorer/biblio/dataset/2377191
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    Dataset updated
    Jun 24, 2024
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    83.0,180.0|-83.0,180.0|-83.0,-180.0|83.0,-180.0|83.0,180.0
    DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory (NREL)
    Authors
    Abou Jaoude, Abdalla; Akindipe, Dayo; Avery, Greg; Cohen, Stuart; Cole, Wesley; Duffy, Patrick; Eberle, Annika; Feldman, David; Fuchs, Becca; Guaita, Nahuel; Hedalen, Ty; Hoffmann, Jeffrey; Joseck, Fred; Kurup, Parthiv; Larson, Levi; Lohse, Chris; Mirletz, Brian; Mulas Hernando, Daniel; Oladosu, Gbadebo; Rakov, Ben; Ramasamy, Vignesh; Roberts, Owen; Rosenlieb, Evan; Schleifer, Anna; Sekar, Ashok; Stehly, Tyler; Stright, Dana; Trivedi, Ishita; Vimmerstedt, Laura; Witter, Eric; Zolan, Alex; Zuboy, Jarett; Zuckerman, Gabriel
    Description

    These data provide the 2024 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

  12. Data from: Solar-to-Grid Public Data File for Utility-scale (UPV) and...

    • osti.gov
    • data.openei.org
    • +2more
    Updated Oct 1, 2021
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    Gorman, Will; Jeong, Seongeun; Mills, Andrew; Millstein, Dev; Seel, Joachim (2021). Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed Photovoltaics (DPV) Generation, Capacity Credit, and Value for 2012-2020 [Dataset]. https://www.osti.gov/dataexplorer/biblio/1825661-solar-grid-public-data-file-utility-scale-upv-distributed-photovoltaics-dpv-generation-capacity-credit-value
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    Dataset updated
    Oct 1, 2021
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    49.2637,-66.5318|24.5873,-66.5318|24.5873,-125.4514|49.2637,-125.4514|49.2637,-66.5318
    DOE Open Energy Data Initiative (OEDI); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
    Authors
    Gorman, Will; Jeong, Seongeun; Mills, Andrew; Millstein, Dev; Seel, Joachim
    Description

    Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

  13. Better Buildings Neighborhood Program Single-Family Home Upgrade Project...

    • data.openei.org
    • osti.gov
    • +1more
    data, website
    Updated May 1, 2015
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    Dale Hoffmeyer; Dale Hoffmeyer (2015). Better Buildings Neighborhood Program Single-Family Home Upgrade Project Dataset [Dataset]. http://doi.org/10.25984/1973648
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    data, websiteAvailable download formats
    Dataset updated
    May 1, 2015
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    Authors
    Dale Hoffmeyer; Dale Hoffmeyer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Building project data for 75,110 single-family homes upgraded between July 1, 2010, and September 30, 2013 from the Better Building Neighborhood Program. This dataset includes a documentation file and three data tables. Reported data for some elements have been transformed and data for some upgraded homes have been omitted to protect privacy. See documentation file for details.

  14. ARPA-E PERFORM datasets

    • osti.gov
    • data.openei.org
    • +2more
    Updated Aug 18, 2022
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    Bryce, Richard; Buster, Grant; Doubleday, Kate; Feng, Cong; Hodge, Bri-Mathias; Ring-Jarvi, Ross; Rose, Megan; Rossol, Michael; Sergi, Brian; Zhang, Flora (2022). ARPA-E PERFORM datasets [Dataset]. https://www.osti.gov/dataexplorer/biblio/dataset/1891136
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    Dataset updated
    Aug 18, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory (NREL)
    48.802720698198,-71.453675|34.940174158744,-71.453675|34.940174158744,-103.3029625|48.802720698198,-103.3029625|48.802720698198,-71.453675
    Authors
    Bryce, Richard; Buster, Grant; Doubleday, Kate; Feng, Cong; Hodge, Bri-Mathias; Ring-Jarvi, Ross; Rose, Megan; Rossol, Michael; Sergi, Brian; Zhang, Flora
    Description

    Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.

  15. 2024 Standard Scenarios: A U.S. Electricity Sector Outlook

    • data.openei.org
    • osti.gov
    • +1more
    data, image_document +1
    Updated Dec 30, 2024
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    Pieter Gagnon; An Pham; Wesley Cole; Anne Hamilton; Pieter Gagnon; An Pham; Wesley Cole; Anne Hamilton (2024). 2024 Standard Scenarios: A U.S. Electricity Sector Outlook [Dataset]. http://doi.org/10.25984/2504171
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    data, website, image_documentAvailable download formats
    Dataset updated
    Dec 30, 2024
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    National Renewable Energy Laboratory (NREL)
    Open Energy Data Initiative (OEDI)
    Authors
    Pieter Gagnon; An Pham; Wesley Cole; Anne Hamilton; Pieter Gagnon; An Pham; Wesley Cole; Anne Hamilton
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    This data corresponds to the 2024 Standard Scenarios report, which contains a suite of forward-looking scenarios of the possible evolution of the U.S. electricity sector through 2050.

    These files contain modeled projections of the future. Although we strive to capture relevant phenomena as comprehensively as possible, the models used to create this data are unavoidably imperfect, and the future is highly uncertain. Consequentially, this data should not be the sole basis for making decisions. In addition to drawing from multiple scenarios within this set, we encourage analysts to also draw on projections from other sources, to benefit from diverse analytical frameworks and perspectives when forming their conclusions about the future of the power sector. For further discussions about the limitations of the models underlying this data, see section 1.4 of the "ReEDS Documentation" linked below.

    For scenario descriptions, input assumptions, and metric definitions for the data in these files, see the "2024 Standard Scenarios Report" linked below.

  16. 2022 Annual Technology Baseline (ATB) Cost and Performance Data for...

    • osti.gov
    • data.openei.org
    • +1more
    Updated Jun 1, 2022
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    DOE Open Energy Data Initiative (OEDI) (2022). 2022 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies [Dataset]. http://doi.org/10.25984/1871952
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    Dataset updated
    Jun 1, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    National Renewable Energy Laboratory (NREL)
    DOE Open Energy Data Initiative (OEDI)
    Description

    These data provide the 2022 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

  17. Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed...

    • osti.gov
    • data.openei.org
    • +1more
    Updated Sep 30, 2020
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    Gorman, Will; Jeong, Seongeun; Mills, Andrew; Millstein, Dev; Seel, Joachim (2020). Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed Photovoltaics (DPV) Generation, Capacity Credit, and Value [Dataset]. https://www.osti.gov/dataexplorer/biblio/dataset/1787566
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    Dataset updated
    Sep 30, 2020
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    49.2637,-66.5318|24.5873,-66.5318|24.5873,-125.4514|49.2637,-125.4514|49.2637,-66.5318
    DOE Open Energy Data Initiative (OEDI); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
    Authors
    Gorman, Will; Jeong, Seongeun; Mills, Andrew; Millstein, Dev; Seel, Joachim
    Description

    Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission. A newer version of the data exists and can be found linked in the resources of this submission under "Solar-to-Grid Public Data File Updated 2021".

  18. BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning...

    • data.openei.org
    • osti.gov
    • +1more
    archive, code, data +1
    Updated Dec 30, 2022
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    Charles Tripp; Jordan Perr-Sauer; Erik Bensen; Jamil Gafur; Ambarish Nag; Avi Purkayastha; Charles Tripp; Jordan Perr-Sauer; Erik Bensen; Jamil Gafur; Ambarish Nag; Avi Purkayastha (2022). BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset [Dataset]. http://doi.org/10.25984/2329316
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    code, data, archive, websiteAvailable download formats
    Dataset updated
    Dec 30, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    National Renewable Energy Laboratory
    Authors
    Charles Tripp; Jordan Perr-Sauer; Erik Bensen; Jamil Gafur; Ambarish Nag; Avi Purkayastha; Charles Tripp; Jordan Perr-Sauer; Erik Bensen; Jamil Gafur; Ambarish Nag; Avi Purkayastha
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects.

    BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

  19. National Transmission Planning Study - Long-Term Modeling Results

    • osti.gov
    • data.openei.org
    • +1more
    Updated Sep 10, 2023
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    DOE Open Energy Data Initiative (OEDI) (2023). National Transmission Planning Study - Long-Term Modeling Results [Dataset]. http://doi.org/10.25984/2460458
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    Dataset updated
    Sep 10, 2023
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    National Renewable Energy Laboratory (NREL)
    DOE Open Energy Data Initiative (OEDI)
    Description

    The National Transmission Planning Study (NTP Study) analyzed the transformation needed to ensure the U.S. transmission system continues to reliably meet electricity demand as the power sector evolves. The study linked several long- and short-term power systems models to test numerous interregional and regional transmission buildout scenarios. The results included in this dataset are from the long-term capacity expansion modeling, completed with NREL's ReEDS model. This includes nearly 100 future transmission scenarios with a wide range of economic, reliability, and resilience conditions. The dataset includes all the results visualized in the Tableau Public site, as linked below.

  20. NREL Eagle supercomputer jobs

    • data.openei.org
    • catalog.data.gov
    code, text_document
    Updated Feb 28, 2023
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    Dmitry Duplyakin; Kevin Menear; Dmitry Duplyakin; Kevin Menear (2023). NREL Eagle supercomputer jobs [Dataset]. https://data.openei.org/submissions/5860
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    text_document, codeAvailable download formats
    Dataset updated
    Feb 28, 2023
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    Open Energy Data Initiative (OEDI)
    National Renewable Energy Laboratory
    Authors
    Dmitry Duplyakin; Kevin Menear; Dmitry Duplyakin; Kevin Menear
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    High performance computing dataset with 11M+ jobs from NREL's Eagle supercomputer. These jobs were submitted to run on Eagle between Nov 2018 and Feb 2023. The data are sufficiently anonymized and do not include sensitive user or project data. HPC research community does not have many public, large, and complete job traces like this one, and releasing this dataset should help address this gap.

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National Renewable Energy Laboratory (2020). Department of Energy's Open Energy Data Initiative (OEDI) [Dataset]. https://registry.opendata.aws/oedi-data-lake/

Department of Energy's Open Energy Data Initiative (OEDI)

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5 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 24, 2020
Dataset provided by
<a href="https://www.nrel.gov/">National Renewable Energy Laboratory</a>
License

Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically

Description

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.

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