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SMART-DS Synthetic Electrical Network Data OpenDSS Models for SFO, GSO, and AUS
The SMART-DS datasets (Synthetic Models for Advanced, Realistic Testing: Distribution systems and Scenarios) are realistic large-scale U.S. electrical distribution models for testing advanced grid algorithms and technology analysis. This document provides a user guide for the data...
Palmintier, B. et al National Renewable Energy Laboratory (NREL)
Dec 18, 2020
5 Resources
0 Stars
Curated
5 Resources
0 Stars
Curated
Tracking the Sun
Berkeley Lab's Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed thro...
Barbose, G. and Darghouth, N. Lawrence Berkeley National Laboratory (LBNL)
Oct 01, 2019
6 Resources
0 Stars
Curated
6 Resources
0 Stars
Curated
Sample IEEE123 Bus system for OEDI SI
Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder...
Elgindy, T. and Balasubramaniam, K. National Renewable Energy Laboratory
Sep 01, 2022
4 Resources
0 Stars
Publicly accessible
4 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
Curated
6 Resources
0 Stars
Curated
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
Curated
4 Resources
0 Stars
Curated
PV Rooftop Database for Puerto Rico (PVRDB-PR)
The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded a...
Mooney, M. and Waechter, K. National Renewable Energy Laboratory (NREL)
Dec 16, 2020
5 Resources
0 Stars
Curated
5 Resources
0 Stars
Curated
End-Use Load Profiles for the U.S. Building Stock
The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, i...
Wilson, E. et al National Renewable Energy Laboratory (NREL)
Oct 14, 2021
8 Resources
1 Stars
Curated
8 Resources
1 Stars
Curated