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The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017

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The Foundational Industry Energy Dataset (FIED) addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization by facility. Each facility is identified by a unique registryID, based on the U.S. Environmental Protection Agency (EPA) Facility Registry Service, and includes its coordinates and other geographic identifiers. Energy-using units are characterized by design capacity, as well as their estimated energy use, greenhouse gas emissions, and physical throughput using 2017 data from the EPA's National Emissions Inventory and Greenhouse Gas Reporting Program.

An overview of the derivation methods is provided in a separate technical report which will be linked after publication. The Python code used to compile the dataset is available in a GitHub repository. An updated 2020 version is under development.

Citation Formats

National Renewable Energy Laboratory (NREL). (2024). The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017 [data set]. Retrieved from https://dx.doi.org/10.25984/2437657.
Export Citation to RIS
McMillan, Colin, Schoeneberger, Carrie, Supekar, Sarang, and Thierry, David. The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017. United States: N.p., 01 Jul, 2024. Web. doi: 10.25984/2437657.
McMillan, Colin, Schoeneberger, Carrie, Supekar, Sarang, & Thierry, David. The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017. United States. https://dx.doi.org/10.25984/2437657
McMillan, Colin, Schoeneberger, Carrie, Supekar, Sarang, and Thierry, David. 2024. "The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017". United States. https://dx.doi.org/10.25984/2437657. https://data.openei.org/submissions/6158.
@div{oedi_6158, title = {The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017}, author = {McMillan, Colin, Schoeneberger, Carrie, Supekar, Sarang, and Thierry, David.}, abstractNote = {The Foundational Industry Energy Dataset (FIED) addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization by facility. Each facility is identified by a unique registryID, based on the U.S. Environmental Protection Agency (EPA) Facility Registry Service, and includes its coordinates and other geographic identifiers. Energy-using units are characterized by design capacity, as well as their estimated energy use, greenhouse gas emissions, and physical throughput using 2017 data from the EPA's National Emissions Inventory and Greenhouse Gas Reporting Program.

An overview of the derivation methods is provided in a separate technical report which will be linked after publication. The Python code used to compile the dataset is available in a GitHub repository. An updated 2020 version is under development. }, doi = {10.25984/2437657}, url = {https://data.openei.org/submissions/6158}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {07}}
https://dx.doi.org/10.25984/2437657

Details

Data from Jul 1, 2024

Last updated Sep 9, 2024

Submitted Aug 17, 2024

Organization

National Renewable Energy Laboratory (NREL)

Contact

Colin McMillan

202.488.2251

Authors

Colin McMillan

National Renewable Energy Laboratory NREL

Carrie Schoeneberger

National Renewable Energy Laboratory NREL

Sarang Supekar

Argonne National Laboratory

David Thierry

Argonne National Laboratory

DOE Project Details

Project Name Foundational Industrial Data Development - Industrial Data Platform

Project Number 28759

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