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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
University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy
The University of Miami Coupled Model (UMCM) is a coupled model that integrates atmospheric, wave, and ocean components to produce wind, wave, and current data. Atmospheric data is produced using the [Weather Research and Forecasting model](https://www.mmm.ucar.edu/weather-researc...
Phillipes, C. et al National Renewable Energy Laboratory
Sep 30, 2015
5 Resources
0 Stars
Curated
5 Resources
0 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
High Resolution Ocean Surface Wave Hindcast (US Wave) Data
The development of this dataset was funded by the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, Water Power Technologies Office to improve our understanding of the U.S. wave energy resource and to provide critical information for wave energy project de...
Yang, Z. et al National Renewable Energy Laboratory
Jul 01, 2020
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting
The BuildingsBench datasets consist of:
Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock.
7 real residential and com...
Emami, P. and Graf, P. National Renewable Energy Laboratory
Dec 31, 2018
6 Resources
0 Stars
Publicly accessible
6 Resources
0 Stars
Publicly accessible