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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 ...
Buster, G. et al Upflow
Jun 30, 2021
11 Resources
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11 Resources
0 Stars
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ResStock: Annual Baseline Results with Component Loads
The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and hi...
Speake, A. et al National Renewable Energy Lab
Mar 03, 2023
6 Resources
0 Stars
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6 Resources
0 Stars
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Sup3rWind Data (CONUS)
This data contains paired European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK) images for 2007 and 2010 over two regions in the US, with domain sizes ~800x800 (latitudes from 25.89 to 41.58, and long...
Sinha, S. et al National Renewable Energy Lab NREL
Jul 16, 2024
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
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Soda Lake Geothermal: Raw 3D and 3C Seismic-Reflection Data from 2010 Survey
This dataset contains seismic-reflection records created in 2010 around the Soda Lake geothermal field near Fallon, Nevada. The data was collected by the power plant operator at the time, Magma Energy (CYRQ Energy in 2024). This was a petroleum-industry-quality three-dimensional ...
N. Louie, J. et al University of Nevada Reno
Sep 01, 2010
5 Resources
0 Stars
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5 Resources
0 Stars
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Admiralty Inlet Hub-Height Turbulence Measurements from June 2012
This data is from measurements at Admiralty Head, in Admiralty Inlet. The measurements were made using an IMU equipped ADV mounted on a mooring, the 'Tidal Turbulence Mooring' or 'TTM'. The inertial measurements from the IMU allows for removal of mooring motion in post processing....
Kilcher, L. National Renewable Energy Laboratory
Jun 18, 2012
7 Resources
0 Stars
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7 Resources
0 Stars
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Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)
This data set contains the full-resolution and state-level data described in the linked technical report (https://www.nrel.gov/docs/fy18osti/71492.pdf). It can be accessed with the NREL-dsgrid-legacy-efs-api, available on GitHub at https://github.com/dsgrid/dsgrid-legacy-efs-api a...
Hale, E. et al National Renewable Energy Laboratory
Jul 08, 2018
6 Resources
0 Stars
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6 Resources
0 Stars
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Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
0 Stars
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6 Resources
0 Stars
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Admiralty Inlet Advanced Turbulence Measurements: May 2015
This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in May of 2015. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on a 'StableMoor' (Manufacturer: DeepWater Buoyancy) buoy and a Tidal Turbulence Mooring (TTM). Thes...
Kilcher, L. National Renewable Energy Laboratory
May 18, 2015
18 Resources
0 Stars
Publicly accessible
18 Resources
0 Stars
Publicly accessible
Admiralty Inlet Advanced Turbulence Measurements: June 2014
This data is from measurements at Admiralty Head, in Admiralty Inlet (Puget Sound) in June of 2014. The measurements were made using Inertial Motion Unit (IMU) equipped ADVs mounted on Tidal Turbulence Mooring's (TTMs). The TTM positions the ADV head above the seafloor to make mid...
Kilcher, L. National Renewable Energy Laboratory
Jun 30, 2014
26 Resources
0 Stars
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26 Resources
0 Stars
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
0 Stars
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6 Resources
0 Stars
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TEAMER: Twin Ocean Power Wave Energy Converter Comprehensive Overview
These files collectively provide a comprehensive overview of the testing process, data analysis, and validation for the Twin Ocean Power device tested at the O.H. Hinsdale Wave Research Laboratory, supported by TEAMER funding.
This resource includes an overview of power results f...
Nguyen, J. and Nguyen, N. Twin Ocean Power
Sep 11, 2024
52 Resources
0 Stars
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52 Resources
0 Stars
Publicly accessible