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Energy Appliance Data 2009-2010

Datasets for residential appliances energy usage/efficiency. The Federal Trade Commission (FTC) collects manufacturer data about the energy use and efficiency of appliances. All of the data was submitted by manufacturers under the FTC's Appliance Labeling Rule (see "FTC Appliance ...

Trade Commission, F. National Renewable Energy Laboratory

Jul 29, 2014
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State Energy Efficient Appliance Rebate Program (SEEARP) Reports

The State Energy Efficient Appliance Rebate Program (SEEARP) reports database includes rebate reports (xls) and summary factsheets (pdf) from states and territories that participated in SEEARP. With funding provided by the American Recovery and Reinvestment Act of 2009 (ARRA), the...

Hoffmeyer, D. Office of Energy Efficiency and Renewable Energy

Jun 18, 2015
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Residential Loads May 2, 2016

Power measurements for various residential appliances in the System Performance Lab (SPL) in the Energy System Integration Facility (ESIF). This data set was collected as part of a project evaluating a new type of power meter that can reduce the cost of submetering many circuits i...

SparnNational Renewable Energy Laboratory

Jun 14, 2016
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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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Modeled Performance of the Hathaway Solar Patriot house Washington D.C

This Dataset contains field research raw data, analysis spreadsheet, photos, and final report from the Hathaway Solar Patriot House Building America Case Study project. This dataset details the monitored and modeled performance of a solar home outside of Washington, D.C. We model...

Norton, P. et al National Renewable Energy Laboratory

Jun 20, 2016
5 Resources
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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...

Marone, C. et al Pennsylvania State University

May 14, 2021
3 Resources
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Simulations of Brady's-Type Fault Undergoing CO2 Push-Pull: Pressure-Transient and Sensitivity Analysis

Input and output files used for fault characterization through numerical simulation using iTOUGH2. The synthetic data for the push period are generated by running a forward simulation (input parameters are provided in iTOUGH2 Brady GF6 Input Parameters.txt [InvExt6i.txt]). In gene...

Jung, Y. and Doughty, C. Lawrence Berkeley National Laboratory

Mar 09, 2018
11 Resources
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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
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Performance and Costs of Ductless Heat Pumps in Marine-Climate High-Performance Homes Habitat for Humanity The Woods

The Washington State University (WSU) Energy Program's Building America (BA) team conducted a case study of a high-performance affordable housing community: The Woods (Figure 1). This BA effort is part of a larger-scale study of 30 homes funded from 2013-2016 by Tacoma Public Util...

Lubliner, M. et al Washington State University

Apr 27, 2016
8 Resources
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INGENIOUS Great Basin Regional Dataset Compilation

This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while re...

Ayling, B. et al GBCGE, NBMG, UNR

Jun 30, 2022
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