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"ANN"×

Community Geothermal: Energy, Cost, and Carbon Modeling for District Design Ann Arbor, MI

Jan 01, 2024
410.48 MB
Publicly accessible
This data includes results on an analysis of existing and projected energy, cost, and carbon for the City of Ann Arbor District Geothermal Design and Deployment to Equitably Decarbonize Low Income Neighborhoods in Ann Arbor project. The scope of the project includes designing and ...
Authors
Lee, J. and McMillen, A. IMEG Corp
geothermalenergyenergy plusdesign buildermichigancommunity geothermaldistrict geothermalcommgeoann arborheating and coolingenergy loadenergy modelcostcarbonbenchmarkingprocessed dataload timeseriesmodelresstockeconomicenvironmentalassessmentenergy auditbuilding modelingdevelopment

Machine Learning Model Geotiffs Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Jun 01, 2021
590.94 MB
Publicly accessible
This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Ma...
Authors
Faulds, J. et al Nevada Bureau of Mines and Geology
geothermalenergyneural networkbayesianannelmbnnprincipal componentpcanmfmachine learningalgorithmplay fairwaynevadapfagreat basingeotiffexplorationcharacterizationinputsoutputsrasterfeature settraining sites

GIS Resource Compilation Map Package Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Jun 01, 2021
831.16 MB
Publicly accessible
This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups incl...
Authors
Brown, S. et al Nevada Bureau of Mines and Geology
geothermalenergynevadamachine learningmap packagegispcanmfbnnannelmgeochemistrygeophysicsheat flowslip and dilationstructureplay fairwaypfaexplorationcharacterizationgreat basindlipdilationgeodatabasehydrothermaldatamodelsprocessed datapaleo-geothermal featurestest sittessupervisedunsupervisedcultural

Programs and Code for Geothermal Exploration Artificial Intelligence

Apr 27, 2021
710.72 kB
Publicly accessible
The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including...
Authors
Moraga, J. Colorado School of Mines
geothermalenergycodershell scriptsgeothermal aimachine learningself organizing mapk-meanspythonaiartificial intelligencedeep learningexplorationgeothermal explorationremote sensingblindsite detectionlstland surface temperaturenumpyrastertensorflowk meananomaly detectionlandsat adr lstsbatchslurmshell

Python Codebase and Jupyter Notebooks Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Jun 30, 2022
8.37 GB
Publicly accessible
Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, a...
Authors
Brown, S. and Smith, C. Nevada Bureau of Mines and Geology
geothermalenergymachine learningnevadapythonjupytergitcodealgorithmmodelpytorchpandasscriptgeotiffbayesian neural networkartificial neural networkpcanmfkbnnannprincipal component analysisnon-negative matrix factorizationpfaexplorationcharaterizationjupyter notebookgreat basinresultsdatadocumentation

Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

Sep 28, 2023
3.69 MB
Publicly accessible
This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...
Authors
Mustafa, A. et al Battelle Memorial Institute
geothermalenergyutahforgegeomechanicsmachine learningffnnartificial neural networkannin-situ stressstress characterizationlabtuvtriaxialstressfeed forward artificial neural networkmlegsmodellingexploratory data analysiseda

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

Apr 15, 2022
113.79 MB
Publicly accessible
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...
Authors
Jin, W. et al Idaho National Laboratory
reservoir thermal energy storagestochastic simulationgeotesmachine learningmodelingtesht-rtescharacterizationnumerical modelstochastichydrogeologic formationsimulated datasimulation datahigh-temperaturethermal energy storageoptimizationartificial neural network regressionannneural networkoperation scenariosseasonal-cyclepareto frontsseasonal operationcontinuous operationfalconmoose