GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files
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 -
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: 2025-05-29},
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
Original Source
https://gdr.openei.org/submissions/1314Research Areas
Keywords
geothermal, energy, machine learning, optimization, operations, synthetic data, power plant, Big Kahuna, GOOML, genetic optimization, forecast, inputs, outputs, configuration, example, phygnn, physics guided neural networks, steamfield, steam field, wells, flash plants, neural network, data, processed data, code, python, simulation, modelDOE Project Details
Project Name Geothermal Operational Optimization with Machine Learning (GOOML)
Project Lead Angel Nieto
Project Number EE0008766
