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National Renewable Energy Laboratory×

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...
Beckers, K. et al National Renewable Energy Laboratory
Feb 18, 2021
1 Resources
0 Stars
Publicly accessible

Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and reside...
Ong, S. and Clark, N. National Renewable Energy Laboratory
Nov 25, 2014
22 Resources
1 Stars
Publicly accessible

Form EIA-861, "Annual Electric Power Industry Report."

This is an electric utility data file that includes such information as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity. The data sou...
Aabakken, J. and Laboratory, N. National Renewable Energy Laboratory
Nov 25, 2014
4 Resources
0 Stars
In curation

UNH TDP Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time f...
Wosnik, M. et al National Renewable Energy Laboratory
Dec 21, 2021
30 Resources
0 Stars
Publicly accessible
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