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Brady's Geothermal Field March 2016 Vibroseis SEG-Y Files and UTM Locations

PoroTomo March 2016 Updated vibroseis source locations with UTM locations. Supersedes gdr.openei.org/submissions/824. Updated vibroseis source location data for Stages 1-4, PoroTomo March 2016. This revision includes source point locations in UTM format (meters) for all four Stage...

Feigl, K. University of Wisconsin

Mar 31, 2016
10 Resources
0 Stars
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Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar One

Wind loading is a main contributor to structural design costs of Concentrating Solar Power (CSP) collectors, such as heliostats and parabolic troughs. These structures must resist the mechanical forces generated by turbulent wind. At the same time, the reflector surfaces must exhi...

Egerer, U. et al National Renewable Energy Laboratory (NREL)

Oct 01, 2021
4 Resources
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Seismic Survey 2016 Data at San Emidio Nevada

Included here are seismic data recorded at the San Emidio Geothermal field in Nevada. This passive seismic data were collected as part of the DOE-funded Subsurface Technology and Engineering R&D (SubTER) project to advance imaging and characterization of geothermal permeability. ...

Lord, N. et al University of Wisconsin-Madison

Jan 05, 2016
4 Resources
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2021 Annual Technology Baseline (ATB) Cost and Performance Data for Electricity Generation Technologies

Starting in 2015 NREL has presented the Annual Technology Baseline (ATB) in an Excel workbook that contains detailed cost and performance data, both current and projected, for renewable and conventional technologies. The workbook includes a spreadsheet for each technology. This ve...

Vimmerstedt, L. et al National Renewable Energy Laboratory (NREL)

Jul 12, 2021
11 Resources
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Demand-Side Grid Model (dsgrid) Data from the Electrification Futures Project (EFS)

This data set contains the full-resolution and state-level data described in the linked technical report (https://www.nrel.gov/docs/fy18osti/71492.pdf). It can be accessed with the NREL-dsgrid-legacy-efs-api, available on GitHub at https://github.com/dsgrid/dsgrid-legacy-efs-api a...

Hale, E. et al National Renewable Energy Laboratory

Jul 08, 2018
6 Resources
0 Stars
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Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI)

Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes model...

Buster, G. et al The National Renewable Energy Lab (NREL)

Oct 16, 2024
2 Resources
0 Stars
Curated

PV Rooftop Database

The National Renewable Energy Laboratory's (NREL) Photovoltaic (PV) Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The PVRDB data are organized...

Mooney, M. National Renewable Energy Laboratory (NREL)

Jan 01, 2016
5 Resources
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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
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Bias Corrected HRRR Wind Resource Data for Grid Integration Applications

Many weather years of high-quality wind data are widely accepted in the grid integration community to be important for studying wind energy technical potential, energy system operations, and grid resilience. NREL makes high-quality wind and solar resource data available. NREL's G...

Pinchuk, P. et al The National Renewable Energy Lab (NREL)

Oct 15, 2024
5 Resources
0 Stars
In progress

2022 Annual Technology Baseline (ATB) Cost and Performance Data for Transportation Technologies

The 2022 Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates: time-series through 2050 for light, medium, and hea...

Vimmerstedt, L. et al National Renewable Energy Laboratory (NREL)

Nov 17, 2023
4 Resources
0 Stars
Curated

National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2023. The NSRDB is updated annually and provides foundational information t...

Sengupta, M. et al National Renewable Energy Laboratory

Sep 28, 2018
7 Resources
2 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
1 Stars
Curated

Airfoil Computational Fluid Dynamics 9k shapes, 2 AoA's

This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 8,996 airfoil shapes, computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes ...

Ramos, D. et al National Renewable Energy Laboratory (NREL)

Feb 10, 2023
3 Resources
0 Stars
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Airfoil Computational Fluid Dynamics 2k shapes, 25 AoA's, 3 Re numbers

This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes w...

Ramos, D. et al National Renewable Energy Laboratory (NREL)

Feb 10, 2023
3 Resources
0 Stars
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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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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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Distributed Generation Market Demand (dGen) model

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sector...

Stanley, T. et al National Renewable Energy Laboratory (NREL)

Oct 16, 2020
4 Resources
0 Stars
Curated

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transpor...

Yip, A. et al National Renewable Energy Laboratory

Aug 29, 2023
8 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
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