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Quantifying and Monetizing Renewable Energy Resiliency

As part of the seed LDRD in 2017 we generated catastrophe models to look at the value of resiliency from an insurance perspective. These data sets reflect the inputs and outputs of that analysis.
Lisell, L. et al National Renewable Energy Laboratory
Mar 13, 2018
3 Resources
0 Stars
Publicly accessible

BUTTER Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...
Tripp, C. et al National Renewable Energy Laboratory
May 20, 2022
4 Resources
0 Stars
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HERO WEC V1.0 WEC-Sim Detailed Simulation Runs and Summary Data

Data sets from simulation runs include 144 wave cases that were run based on the WEC-Sim model (https://mhkdr.openei.org/submissions/483). The 144 wave cases represent waves with the following wave height and wave period ranges: Significant Wave Height: 0.25 4.0m in 0.25m incre...
Jenne, S. National Renewable Energy Laboratory
Aug 12, 2022
9 Resources
0 Stars
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Wind Turbine Gearbox Condition Monitoring Vibration Analysis Benchmarking Datasets

Wind turbine condition monitoring (CM) can potentially help the wind industry reduce turbine downtime and operation and maintenance (O&M) cost. NREL CM research has investigated various condition-monitoring techniques such as acoustic emission (AE specifically stress wave), vibrat...
Sheng, S. National Renewable Energy Laboratory
Mar 28, 2014
5 Resources
2 Stars
Publicly accessible