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Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI)
Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes model...
Buster, G. et al The National Renewable Energy Lab (NREL)
Oct 16, 2024
2 Resources
0 Stars
Curated
2 Resources
0 Stars
Curated
Bias Corrected HRRR Wind Resource Data for Grid Integration Applications
Many weather years of high-quality wind data are widely accepted in the grid integration community to be important for studying wind energy technical potential, energy system operations, and grid resilience.
NREL makes high-quality wind and solar resource data available. NREL's G...
Pinchuk, P. et al The National Renewable Energy Lab (NREL)
Oct 15, 2024
5 Resources
0 Stars
In progress
5 Resources
0 Stars
In progress
Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)
The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios.
Sup3rCC is downscaled ...
Buster, G. et al The National Renewable Energy Lab (NREL)
Apr 19, 2023
7 Resources
1 Stars
Curated
7 Resources
1 Stars
Curated
Imperial Valley Dark Fiber Project Continuous DAS Data
The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecomm...
Ajo-Franklin, J. et al Lawrence Berkeley National Laboratory
Nov 10, 2020
4 Resources
0 Stars
Publicly accessible
4 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
Publicly accessible
4 Resources
0 Stars
Publicly accessible