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Coalbed Methane Resources in the Powder River Basin: Lithologic Data

The USGS investigated coalbed methane (CBM) resources in the Powder River Basin (WY) and adjacent basins in Wyoming and North Dakota. Specifically, the analysis looked at: total gas desorbed, coal quality, and high-pressure methane adsorption isotherm data from 963 cored coal samp...

Hallett, K. and USGS, . National Renewable Energy Laboratory

Sep 29, 2010
2 Resources
0 Stars
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U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2013)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity ra...

Huggins, J. and Laboratory, N. National Renewable Energy Laboratory

Jul 13, 2015
5 Resources
1 Stars
Publicly accessible

Maximum demand charge rates for commercial and industrial electricity tariffs in the United States

NREL has assembled a list of U.S. retail electricity tariffs and their associated demand charge rates for the Commercial and Industrial sectors. The data was obtained from the Utility Rate Database. Keep the following information in mind when interpreting the data: (1) These data...

McLaren. . et al National Renewable Energy Laboratory

Sep 19, 2017
1 Resources
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Open Energy Information (OpenEI.org)

Open Energy Information (OpenEI) is a knowledge-sharing online community dedicated to connecting people with the latest information and data on energy resources from around the world. Created in partnership with the United States Department of Energy and federal laboratories acros...

Brodt-Giles, D. and (EERE), O. National Renewable Energy Laboratory

Nov 25, 2014
1 Resources
0 Stars
In curation

NREL RSF Measured Data 2011

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers computers that communicate with the building's various control systems to...

Sheppy, M. et al National Renewable Energy Laboratory

Nov 25, 2014
2 Resources
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NREL RSF Weather Data 2011

Sheppy, M. et al National Renewable Energy Laboratory

Nov 25, 2014
2 Resources
0 Stars
Publicly accessible

U.S. Electric Utility Companies and Rates: Look-up by Zipcode (Feb 2011)

Huggins, J. and Laboratory, N. National Renewable Energy Laboratory

Nov 25, 2014
5 Resources
0 Stars
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Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

Sheppy. . et al National Renewable Energy Laboratory

Mar 25, 2016
4 Resources
0 Stars
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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
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U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2014)

Huggins, J. and Laboratory, N. National Renewable Energy Laboratory

Dec 01, 2015
7 Resources
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NREL RSF Energy Model 2011

Sheppy, M. et al National Renewable Energy Laboratory

Nov 25, 2014
2 Resources
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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
1 Stars
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Data Files for “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure"

This data set includes modeling results from “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure” including region-specific [i.e., national, state, and core-based statistical area (CBSA)—cities/towns] electric v...

Wood and BorlaugNational Renewable Energy Laboratory

Apr 26, 2023
5 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...

Beckers, K. et al National Renewable Energy Laboratory

Oct 20, 2021
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Rooftop Energy Potential of Low Income Communities in America REPLICA

The Rooftop Energy Potential of Low Income Communities in America REPLICA data set provides estimates of residential rooftop solar technical potential at the tract-level with emphasis on estimates for Low and Moderate Income LMI populations. In addition to technical potential REPL...

Mooney and SigrinNational Renewable Energy Laboratory

Apr 03, 2018
27 Resources
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Coalbed Methane Resources in the Powder River Basin: Lithologic Data

U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2013)

Maximum demand charge rates for commercial and industrial electricity tariffs in the United States

Open Energy Information (OpenEI.org)

NREL RSF Measured Data 2011

NREL RSF Weather Data 2011

U.S. Electric Utility Companies and Rates: Look-up by Zipcode (Feb 2011)

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

BUTTER Empirical Deep Learning Dataset

U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2014)

NREL RSF Energy Model 2011

BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

Data Files for “The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure"

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Rooftop Energy Potential of Low Income Communities in America REPLICA