GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples
This submission includes example files and Jupyter Notebooks associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Kahunanui (KHN) fictional geothermal power plant, which uses synthetic data to model a fictional plant. Includes data curation, historical modeling, regression, neural network, hindcast modeling, forecasting, pressure solver, and genetic optimization notebooks produced using the GOOML framework, fictional input data, and configuration files.
Note that the data and Jupyter notebooks 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 notebooks and reproduce these results.
Units can be found in the data dictionary resource in the Timeseries Data Files archive.
Citation Formats
Upflow. (2023). GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples [data set]. Retrieved from https://gdr.openei.org/submissions/1466.
Taverna, Nicole, Buster, Grant, Konstantopoulos, Iraklis, Weers, Jon, Siratovich, Paul, and Blair, Andy. GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples. United States: N.p., 30 Jan, 2023. Web. https://gdr.openei.org/submissions/1466.
Taverna, Nicole, Buster, Grant, Konstantopoulos, Iraklis, Weers, Jon, Siratovich, Paul, & Blair, Andy. GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples. United States. https://gdr.openei.org/submissions/1466
Taverna, Nicole, Buster, Grant, Konstantopoulos, Iraklis, Weers, Jon, Siratovich, Paul, and Blair, Andy. 2023. "GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples". United States. https://gdr.openei.org/submissions/1466.
@div{oedi_1466, title = {GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples}, author = {Taverna, Nicole, Buster, Grant, Konstantopoulos, Iraklis, Weers, Jon, Siratovich, Paul, and Blair, Andy.}, abstractNote = {This submission includes example files and Jupyter Notebooks associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Kahunanui (KHN) fictional geothermal power plant, which uses synthetic data to model a fictional plant. Includes data curation, historical modeling, regression, neural network, hindcast modeling, forecasting, pressure solver, and genetic optimization notebooks produced using the GOOML framework, fictional input data, and configuration files.
Note that the data and Jupyter notebooks 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 notebooks and reproduce these results.
Units can be found in the data dictionary resource in the Timeseries Data Files archive.}, doi = {}, url = {https://gdr.openei.org/submissions/1466}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {01}}
Details
Data from Jan 30, 2023
Last updated Feb 2, 2023
Submission in progress
Organization
Upflow
Contact
Paul Siratovich
Authors
Keywords
geothermal, energy, machine learning, GOOML, power plant, optimization, genetic optimization, regression, neural network, operations, synthetic data, Kahunanui, forecast, hindcast, data curation, inputs, outputs, configuration, example, phygnn, physics guided neural networks, steamfield, steam field, wells, flash plants, processed data, python, jupyter notebook, model, modeling, codeDOE Project Details
Project Name Geothermal Operational Optimization with Machine Learning (GOOML)
Project Lead Angel Nieto
Project Number EE0008766