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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources
Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...
Ahmmed, B. Stanford University
Apr 04, 2022
3 Resources
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
Pilgrim Hot Springs: GEOPHIRES Inputs and Outputs for Direct-Use Geothermal District Heating and Cooling
This dataset includes files for a techno-economic analysis conducted using the GEOPHIRES simulator to examine the feasibility of expanding a larger district heating site in a remote location: Pilgrim Hot Springs, Alaska. Files included here are GEOPHIRES inputs and outputs for fiv...
Pauling, H. et al National Renewable Energy Laboratory
Mar 21, 2024
21 Resources
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21 Resources
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Demand-Side Grid (dsgrid) Building Load Profiles using ResStock and ComStock v2021
This dataset contains simulated hourly end use load profiles of the residential and commercial building sector in the contiguous United States for every other year from 2010 to 2050. Data were produced in 2021 using ResStock and ComStock, which are building stock energy models of ...
Liu, L. et al National Renewable Energy Lab (NREL)
Apr 12, 2021
9 Resources
0 Stars
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9 Resources
0 Stars
Publicly accessible