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.
- HTMLphygnn GitHub Repository
- PNGBig Kahuna Component Diagram.png
- CSVBig Kahuna Input Dataset.csv
- CSVBig Kahuna Forecast Output Dataset.csv
- ZIPBig Kahuna Forecast Output Plots.zip
- ZIPBig Kahuna Genetic Optimization Output Plots.zip
- TXTBig Kahuna ReadMe.txt
- ZIPBig Kahuna Flash Plant Configuration Files.zip
- ZIPBig Kahuna Well Configuration Files.zip
- JSONBig Kahuna Plant Configuration File.json
- HTMLOverview of GOOML journal article in Energies
