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. 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.

  5. 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.

  6. 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.

  7. 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.

  8. g

    PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic...

    • gimi9.com
    Updated Dec 7, 2024
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    (2024). PoroTomo Natural Laboratory Horizontal and Vertical Distributed Acoustic Sensing Data | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_porotomo-natural-laboratory-horizontal-and-vertical-distributed-acoustic-sensing-data
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    Dataset updated
    Dec 7, 2024
    Description

    This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also included are links to the documentation for the dataset, Jupyter Notebook tutorials for working with the data as it is stored in AWS S3, and links to data viewers in OEDI for the horizontal (DASH) and vertical (DASV) DAS datasets. Horizontal DAS (DASH) data collection began 3/8/16, paused, and then started again on 3/11/2016 and ended 3/26/2016 using zigzag trenched fiber optic cabels. Vertical DAS (DASV) data collection began 3/17/2016 and ended 3/28/16 using a fiber optic cable through the first 363 m of a vertical well. These are raw data files from the DAS deployment at (DASH) and below (DASV) the surface during testing at the PoroTomo Natural Laboratory at Brady Hot Spring in Nevada. SEG-Y and hdf5 files are stored in 30 second files organized into directories by day. The hdf5 files available via HSDS are stored in daily files. Metadata includes information on the timing of recording gaps and a file count is included that lists the number of files from each day of recording. These data are available for download without login credentials through the free and publicly accessible Open Energy Data Initiative (OEDI) data viewer which allows users to browse and download individual or groups of files.

  9. PNNL Distribution System State Estimator Docker Image

    • data.openei.org
    • catalog.data.gov
    archive, code +2
    Updated Jul 10, 2023
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    Bilal Bhatti; Gary Black; Andrew Reiman; Bilal Bhatti; Gary Black; Andrew Reiman (2023). PNNL Distribution System State Estimator Docker Image [Dataset]. http://doi.org/10.25984/1992365
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    text_document, archive, code, websiteAvailable download formats
    Dataset updated
    Jul 10, 2023
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Open Energy Data Initiative (OEDI)
    Pacific Northwest National Laboratory
    Authors
    Bilal Bhatti; Gary Black; Andrew Reiman; Bilal Bhatti; Gary Black; Andrew Reiman
    License

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

    Description

    This is the docker image for Pacific Northwest National Laboratory's (PNNL) distribution system state estimator (DSSE) 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.

  10. 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.

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

    • data.openei.org
    • osti.gov
    • +1more
    data, website
    Updated Sep 30, 2020
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    Joachim Seel; Andrew Mills; Dev Millstein; Will Gorman; Seongeun Jeong; Joachim Seel; Andrew Mills; Dev Millstein; Will Gorman; Seongeun Jeong (2020). Solar-to-Grid Public Data File for Utility-scale (UPV) and Distributed Photovoltaics (DPV) Generation, Capacity Credit, and Value [Dataset]. http://doi.org/10.25984/1787566
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    data, websiteAvailable download formats
    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
    Open Energy Data Initiative (OEDI)
    Lawrence Berkeley National Lab
    Authors
    Joachim Seel; Andrew Mills; Dev Millstein; Will Gorman; Seongeun Jeong; Joachim Seel; Andrew Mills; Dev Millstein; Will Gorman; Seongeun Jeong
    License

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

    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".

  12. O

    Data from: SMART-DS Synthetic Electrical Network Data OpenDSS Models for...

    • data.openei.org
    • catalog.data.gov
    code, data, website
    Updated Dec 18, 2020
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    Bryan Palmintier; Carlos Mateo Domingo; Fernando Emilio Postigo Marcos; Tomas Gomez San Roman; Fernando de Cuadra; Nicolas Gensollen; Tarek Elgindy; Pablo Duenas; Bryan Palmintier; Carlos Mateo Domingo; Fernando Emilio Postigo Marcos; Tomas Gomez San Roman; Fernando de Cuadra; Nicolas Gensollen; Tarek Elgindy; Pablo Duenas (2020). SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS [Dataset]. https://data.openei.org/submissions/2981
    Explore at:
    website, code, dataAvailable download formats
    Dataset updated
    Dec 18, 2020
    Dataset provided by
    USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)
    National Renewable Energy Laboratory (NREL)
    Open Energy Data Initiative (OEDI)
    Authors
    Bryan Palmintier; Carlos Mateo Domingo; Fernando Emilio Postigo Marcos; Tomas Gomez San Roman; Fernando de Cuadra; Nicolas Gensollen; Tarek Elgindy; Pablo Duenas; Bryan Palmintier; Carlos Mateo Domingo; Fernando Emilio Postigo Marcos; Tomas Gomez San Roman; Fernando de Cuadra; Nicolas Gensollen; Tarek Elgindy; Pablo Duenas
    License

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

    Description

    The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the datasets.

    This dataset contains synthetic detailed electrical distribution network models, and connected timeseries loads for the greater San Francisco (SFO), Greensboro, and Austin areas. It is intended to provide researchers with very realistic and complete models that can be used for extensive powerflow simulations under a variety of scenarios. The data is synthetic, but has been validated against thousands of utility feeders to ensure statistical and operational similarity to electrical distribution networks in the US.

    The OpenDSS data is partitioned into several regions (each zipped separately). After unzipping these files, each region has a folder for each substation, and subsequent folders for each feeder within the substation. This allows users to simulate smaller sections of the full dataset. Each of these folders (region, substation and feeder) has a folder titled "analysis" which contains CSV files listing voltages and overloads throughout the network for the peak loading time in the year. It also contains .png files showing the loading of residential and commercial loads on the network for every day of the year, and daily breakdowns of loads for commercial building categories. Time series data is provided in the "profiles" folder including real and reactive power at 15 minute resolution along with parquet files in the "endues" folder with breakdowns of building end-uses.

  13. D

    Waterinfiltratieproject OEDI, deeltrajecten I en II

    • archaeology.datastations.nl
    pdf, xml
    Updated Dec 31, 1999
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    M.G. Marinelli; M.G. Marinelli (1999). Waterinfiltratieproject OEDI, deeltrajecten I en II [Dataset]. http://doi.org/10.17026/DANS-ZPQ-KUSJ
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    pdf(3335309), pdf(5606055), xml(4394), xml(1660), pdf(2486492), xml(4610), xml(3315)Available download formats
    Dataset updated
    Dec 31, 1999
    Dataset provided by
    DANS Data Station Archaeology
    Authors
    M.G. Marinelli; M.G. Marinelli
    License

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

    Description

    onderzoeksrapport

  14. 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
    Explore at:
    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.

  15. 2022 Annual Technology Baseline (ATB) Cost and Performance Data for...

    • osti.gov
    • data.openei.org
    • +1more
    Updated Jun 1, 2022
    + more versions
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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
    Explore at:
    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.

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

    • osti.gov
    • data.openei.org
    • +2more
    Updated Jun 30, 2022
    + more versions
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    DOE Open Energy Data Initiative (OEDI) (2022). U.S. Wind Siting Regulation and Zoning Ordinances [Dataset]. http://doi.org/10.25984/1873866
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    Dataset updated
    Jun 30, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Energy Efficiency and Renewable Energyhttp://energy.gov/eere
    National Renewable Energy Lab. (NREL), Golden, CO (United States)
    DOE Open Energy Data Initiative (OEDI)
    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 spreadsheet. 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"

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

    • osti.gov
    • data.openei.org
    • +1more
    Updated Dec 30, 2022
    + more versions
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    DOE Open Energy Data Initiative (OEDI) (2022). BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset [Dataset]. http://doi.org/10.25984/2329316
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    Dataset updated
    Dec 30, 2022
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    Office of Sciencehttp://www.er.doe.gov/
    National Renewable Energy Laboratory (NREL), Golden, CO (United States)
    DOE Open Energy Data Initiative (OEDI)
    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.

  18. Renewable Electricity Procurement Options Data

    • data.openei.org
    • gimi9.com
    • +2more
    data, website
    Updated Nov 12, 2019
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    Megan Day; Megan Day (2019). Renewable Electricity Procurement Options Data [Dataset]. http://doi.org/10.25984/1788085
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    website, dataAvailable download formats
    Dataset updated
    Nov 12, 2019
    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
    Megan Day; Megan Day
    License

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

    Description

    The Procurement Analysis Tool (PAT) was developed at NREL to help organizations explore renewable energy options that align with their goals. Users input facility data and answer goal-oriented questions. PAT analyzes this information to identify potential wind, solar, or storage resources and suitable procurement options (PPA, Green Tariffs) that align with their budget, location, and sustainability goals. For more information see the "Procurement Analysis Tool" resource below.

    The Renewable Electricity Procurement Options Data (RE-POD) was an aggregated dataset meant to help local jurisdictions and utility customers within those jurisdictions understand the options that may be available to them to procure renewable electricity or renewable energy credits to meet energy goals. RE-POD has been discontinued and replaced with the PAT.

    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 builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

  19. 2021 Annual Technology Baseline (ATB) Cost and Performance Data for...

    • data.openei.org
    • datasets.ai
    • +3more
    archive, code, data +1
    Updated Jul 12, 2021
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    Laura Vimmerstedt; Sertac Akar; Philipp Beiter; Wesley Cole; David Feldman; Parthiv Kurup; Craig Turchi; Gbadebo Oladosu; Greg Rhodes; Chad Augustine; Caitlin Murphy; Anna Schleifer; Stuart Cohen; Jeffrey Hoffmann; Paul Schwabe; Mark Bolinger; Brian Mirletz; Mike Bannister; Dana Stright; Jeremy Jenkins; Laura Vimmerstedt; Sertac Akar; Philipp Beiter; Wesley Cole; David Feldman; Parthiv Kurup; Craig Turchi; Gbadebo Oladosu; Greg Rhodes; Chad Augustine; Caitlin Murphy; Anna Schleifer; Stuart Cohen; Jeffrey Hoffmann; Paul Schwabe; Mark Bolinger; Brian Mirletz; Mike Bannister; Dana Stright; Jeremy Jenkins (2021). 2021 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies [Dataset]. http://doi.org/10.25984/1807473
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    website, data, archive, codeAvailable download formats
    Dataset updated
    Jul 12, 2021
    Dataset provided by
    United States Department of Energyhttp://energy.gov/
    National Renewable Energy Laboratory (NREL)
    Open Energy Data Initiative (OEDI)
    Authors
    Laura Vimmerstedt; Sertac Akar; Philipp Beiter; Wesley Cole; David Feldman; Parthiv Kurup; Craig Turchi; Gbadebo Oladosu; Greg Rhodes; Chad Augustine; Caitlin Murphy; Anna Schleifer; Stuart Cohen; Jeffrey Hoffmann; Paul Schwabe; Mark Bolinger; Brian Mirletz; Mike Bannister; Dana Stright; Jeremy Jenkins; Laura Vimmerstedt; Sertac Akar; Philipp Beiter; Wesley Cole; David Feldman; Parthiv Kurup; Craig Turchi; Gbadebo Oladosu; Greg Rhodes; Chad Augustine; Caitlin Murphy; Anna Schleifer; Stuart Cohen; Jeffrey Hoffmann; Paul Schwabe; Mark Bolinger; Brian Mirletz; Mike Bannister; Dana Stright; Jeremy Jenkins
    License

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

    Description

    Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This version of the workbook provides the final updates to data for the 2021 ATB. In 2019 and 2020, NREL has also provided selected data in Tableau workbooks and structured summary csv files. The data for 2015 - 2020 is located on https://data.nrel.gov. In 2021 and going forward, the data is cloud optimized and provided in the OEDI data lake. A website documents this and future data at https://atb.nrel.gov.

  20. 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.

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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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