GOOML Kahunanui Data Curation, Historical Modeling, Forecast Modeling, and Genetic Optimization Examples

This dataset contains example files and Jupyter Notebooks associated with the Geothermal Operational Optimization using Machine Learning (GOOML) framework, specifically for the fictional Kahunanui (KHN) geothermal power plant. The dataset includes synthetic time series data, configuration files, trained machine learning models, and a collection of example notebooks demonstrating data curation, historical modeling, regression, neural networks, hindcast modeling, forecasting, pressure solvers, and genetic optimization. These materials showcase GOOML's capabilities in modeling and optimizing geothermal power plant operations.

The submission includes supporting resources such as system diagrams, well equations, and flash plant dimensions used in constructing the fictional Kahunanui steam field. A data dictionary is provided within the time series data archive, detailing data labels, descriptions, and units. Additionally, webinar slides and a recording of a GOOML overview presentation are included. While example notebooks are provided as PDFs, they cannot be executed without access to the proprietary GOOML codebase, which is not publicly available. Users should note that additional unreleased files are required to run the models and reproduce results. Additionally, an associated dataset for the GOOML Big Kahuna fictional power plant is linked, containing forecast modeling and genetic optimization files.