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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

TY - DATA AB - 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. AU - Buster, Grant A2 - Taverna, Nicole A3 - Rossol, Michael A4 - Weers, Jon A5 - Siratovich, Paul A6 - Blair, Andy A7 - Huggins, Jay DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.15121/1812319 KW - geothermal KW - energy KW - machine learning KW - optimization KW - operations KW - synthetic data KW - power plant KW - Big Kahuna KW - GOOML KW - genetic optimization KW - forecast KW - inputs KW - outputs KW - configuration KW - example KW - phygnn KW - physics guided neural networks KW - steamfield KW - steam field KW - wells KW - flash plants KW - neural network KW - data KW - processed data KW - code KW - python KW - simulation KW - model LA - English DA - 2021/06/30 PY - 2021 PB - Upflow T1 - GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files UR - https://doi.org/10.15121/1812319 ER -
Export Citation to RIS
Buster, Grant, et al. GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files. Upflow, 30 June, 2021, GDR. https://doi.org/10.15121/1812319.
Buster, G., Taverna, N., Rossol, M., Weers, J., Siratovich, P., Blair, A., & Huggins, J. (2021). GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files. [Data set]. GDR. Upflow. https://doi.org/10.15121/1812319
Buster, Grant, Nicole Taverna, Michael Rossol, Jon Weers, Paul Siratovich, Andy Blair, and Jay Huggins. GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files. Upflow, June, 30, 2021. Distributed by GDR. https://doi.org/10.15121/1812319
@misc{OEDI_Dataset_7432, title = {GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files}, author = {Buster, Grant and Taverna, Nicole and Rossol, Michael and Weers, Jon and Siratovich, Paul and 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.}, url = {https://gdr.openei.org/submissions/1314}, year = {2021}, howpublished = {GDR, Upflow, https://doi.org/10.15121/1812319}, note = {Accessed: 2025-05-03}, doi = {10.15121/1812319} }
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

Research Areas

DOE Project Details

Project Name Geothermal Operational Optimization with Machine Learning (GOOML)

Project Lead Angel Nieto

Project Number EE0008766

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