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GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

Publicly accessible License 

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots.

A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications.

Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results.

Units can be found in the readme data resource.

Citation Formats

Upflow. (2021). GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files [data set]. Retrieved from https://dx.doi.org/10.15121/1812319.
Export Citation to RIS
Buster, Grant, Taverna, Nicole, Rossol, Michael, Weers, Jon, Siratovich, Paul, Blair, Andy, and Huggins, Jay. GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files. United States: N.p., 30 Jun, 2021. Web. doi: 10.15121/1812319.
Buster, Grant, Taverna, Nicole, Rossol, Michael, Weers, Jon, Siratovich, Paul, Blair, Andy, & Huggins, Jay. GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files. United States. https://dx.doi.org/10.15121/1812319
Buster, Grant, Taverna, Nicole, Rossol, Michael, Weers, Jon, Siratovich, Paul, Blair, Andy, and Huggins, Jay. 2021. "GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files". United States. https://dx.doi.org/10.15121/1812319. https://data.openei.org/submissions/4472.
@div{oedi_4472, title = {GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files}, author = {Buster, Grant, Taverna, Nicole, Rossol, Michael, Weers, Jon, Siratovich, Paul, Blair, Andy, and Huggins, Jay.}, abstractNote = {This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots.

A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications.

Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results.

Units can be found in the readme data resource.}, doi = {10.15121/1812319}, url = {https://data.openei.org/submissions/4472}, journal = {}, number = , volume = , place = {United States}, year = {2021}, month = {06}}
https://dx.doi.org/10.15121/1812319

Details

Data from Jun 30, 2021

Last updated Nov 24, 2021

Submitted Aug 6, 2021

Organization

Upflow

Contact

Paul Siratovich

Authors

Grant Buster

National Renewable Energy Laboratory

Nicole Taverna

National Renewable Energy Laboratory

Michael Rossol

National Renewable Energy Laboratory

Jon Weers

National Renewable Energy Laboratory

Paul Siratovich

Upflow

Andy Blair

Upflow

Jay Huggins

National Renewable Energy Laboratory

DOE Project Details

Project Name Geothermal Operational Optimization with Machine Learning (GOOML)

Project Lead Angel Nieto

Project Number EE0008766

Research Areas

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