About this Dataset

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.
Organization: Department of Energy
Last updated: 2025-01-11T22:55:04.123947
Tags: big-kahuna, code, configuration, data, energy, example, flash-plants, forecast, genetic-optimization, geothermal, gooml, inputs, machine-learning, model, neural-network, operations, optimization, outputs, phygnn, physics-guided-neural-networks, power-plant, processed-data, python, simulation, steam-field, steamfield, synthetic-data, wells

Tables

Big Kahuna Forecast Output Dataset

@usgov.doe_gov_gooml_big_kahuna_forecast_modeling_and_genetic_273a6206.big_kahuna_forecast_output_dataset

6.82 MB
35034 rows
24 columns


CREATE TABLE big_kahuna_forecast_output_dataset (
  "unnamed_0" BIGINT,
  "fp_a_steam_out_mass_flow" DOUBLE,
  "fp_a_steam_out_pressure" DOUBLE,
  "fp_a_liquid_out_mass_flow" DOUBLE,
  "fp_a_liquid_out_pressure" DOUBLE,
  "fp_b_steam_out_mass_flow" DOUBLE,
  "fp_b_steam_out_pressure" DOUBLE,
  "fp_b_liquid_out_mass_flow" DOUBLE,
  "fp_b_liquid_out_pressure" DOUBLE,
  "w_a_mass_flow" DOUBLE,
  "w_a_pressure" DOUBLE,
  "w_b_mass_flow" DOUBLE,
  "w_b_pressure" DOUBLE,
  "w_c_mass_flow" DOUBLE,
  "w_c_pressure" DOUBLE,
  "w_d_mass_flow" DOUBLE,
  "w_d_pressure" DOUBLE,
  "w_e_mass_flow" DOUBLE,
  "w_e_pressure" DOUBLE,
  "tg_a_power_generation" DOUBLE,
  "tg_a_steam_flow" DOUBLE,
  "total_system_mass_take" DOUBLE,
  "total_fp_separated_steam_flow" DOUBLE,
  "total_system_power_gen" DOUBLE
);

Big Kahuna Input Dataset

@usgov.doe_gov_gooml_big_kahuna_forecast_modeling_and_genetic_273a6206.big_kahuna_input_dataset

912.26 KB
35034 rows
9 columns


CREATE TABLE big_kahuna_input_dataset (
  "time_index" VARCHAR,
  "w_a_condition" VARCHAR,
  "w_b_condition" VARCHAR,
  "w_c_condition" VARCHAR,
  "w_d_condition" VARCHAR,
  "w_e_condition" VARCHAR,
  "fp_a_pressure_vapor" DOUBLE,
  "fp_b_pressure_vapor" DOUBLE,
  "tg_a_status" VARCHAR
);