We present data-driven estimates of hidden hydrothermal technical capacity of the Great Basin. Geothermal (hydrothermal) has recently been included in large-scale renewable energy generation models, with the same resolution as wind and solar. Previous estimates for hydrothermal did not include measurements of permeability or uncertainty. The Great Basin area provides the most data-rich location to identify positive and negative hydrothermal sites and data observables (features), which have been used in eXtreme Gradient Boosting (XGBoost) regression to make hydrothermal capacity predictions. This work is novel in its use of 37 nameplate capacities (megawatts) as the positive labels and 248 negative (0-MW) locations. The independent variables or features are 48 geophysical and geologic attributes. Three XGBoost predictions were input into the Renewable Energy Potential (reV) model. These predictions were chosen because of their fits to the 37 geothermal plants and negative locations as well as their reasonable prediction ranges and totals within the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) study area. We present a method for subsampling the negative sites to bring the labels into balance that uses the geologic domain knowledge to proportionally represent negatives. Overall, the distributions of the hydrothermal technical capacity and the site levelized cost of energy are respectively much tighter and lower than the previous estimates for the Great Basin, which only used contiguous U.S. scaled temperature estimates. Percentile (50th and 90th) models provide bookends for these metrics.

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