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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. . et al National Renewable Energy Laboratory

Mar 13, 2018
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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
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BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...

Tripp, C. et al National Renewable Energy Laboratory

Dec 30, 2022
9 Resources
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OPFLearnData: Dataset for Learning AC Optimal Power Flow

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to t...

Joswig-Jones. . et al National Renewable Energy Laboratory

Oct 26, 2021
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Event-correlated Outage Dataset in America

This dataset includes an aggregated and event-correlated analysis of power outages in the United States, synthesized by integrating three data sources: the Environment for the Analysis of Geo-Located Energy Information (EAGLE-I), the Electric Emergency Incident Disturbance Report ...

She, B. et al Pacific Northwest National Laboratory

Oct 01, 2024
8 Resources
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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
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