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

McMillan, Colin; Schoeneberger, Carrie; Supekar, Sarang; Thierry, David

doi:10.25984/2437657

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

Dataset
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Abstract

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.

Authors:

; Schoeneberger, Carrie;

; Thierry, David

National Renewable Energy Laboratory (NREL)

Publication Date:: Mon Jul 01 00:00:00 EDT 2024

Other Number(s):: 6158

Research Org.:: DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory (NREL)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)

Collaborations:: National Renewable Energy Laboratory (NREL)

Subject:: Array; FIED; Python; boilers; capacity; code; combustion units; data; dataset; energy; energy analysis; energy estimate; energy use; facility; furnaces; greenhouse gas; greenhouse gas emissions; industrial energy; industry; ovens; process heat; processed data; throughput; unit-level

OSTI Identifier:: 2437657

DOI:: https://doi.org/10.25984/2437657

Citation Formats


                    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., 2024. 
        Web.  doi:10.25984/2437657.

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                    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.  doi:https://doi.org/10.25984/2437657

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                    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.  doi:https://doi.org/10.25984/2437657.  https://www.osti.gov/servlets/purl/2437657. Pub date:Mon Jul 01 00:00:00 EDT 2024

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@article{osti_2437657,

  title        = {The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017},

  author       = {McMillan, Colin and Schoeneberger, Carrie and 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},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Jul 01 00:00:00 EDT 2024},

  month        = {Mon Jul 01 00:00:00 EDT 2024}

}

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DOI: https://doi.org/10.25984/2437657

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