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The Foundational Industry Energy Dataset: Unit-level Characterization and Derived Energy Estimates for Industrial Facilities in 2017

The Foundational Industry Energy Dataset (FIED) addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization by facility. Each facility is identified by a uni...

McMillan, C. et al National Renewable Energy Laboratory (NREL)

Jul 01, 2024
4 Resources
0 Stars
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AquaPV: Foundational Analysis and Industry Guidance on Floating PV Results

This dataset contains results of the technical potential analysis of floating photovoltaics (FPV) on federally owned or permitted reservoirs in the United States. Estimates of the area of reservoirs that is technically feasible for FPV development are provided for reservoirs that...

Rosenlieb, E. et al National Renewable Energy Lab

Nov 08, 2024
3 Resources
0 Stars
Publicly accessible

INTEGRATE Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design techno...

Vijayakumar, G. et al National Renewable Energy Laboratory (NREL)

May 04, 2021
8 Resources
0 Stars
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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
Publicly accessible

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especia...

Duzgun, H. et al Colorado School of Mines

Jan 08, 2021
12 Resources
0 Stars
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University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy

The University of Miami Coupled Model (UMCM) is a coupled model that integrates atmospheric, wave, and ocean components to produce wind, wave, and current data. Atmospheric data is produced using the [Weather Research and Forecasting model](https://www.mmm.ucar.edu/weather-researc...

Phillipes, C. et al National Renewable Energy Laboratory

Sep 30, 2015
5 Resources
0 Stars
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Fish Detection AI, Optic and Sonar-trained Object Detection Models

The Fish Detection AI project aims to improve the efficiency of fish monitoring around marine energy facilities to comply with regulatory requirements. Despite advancements in computer vision, there is limited focus on sonar images, identifying small fish with unlabeled data, and ...

Slater, K. et al Water Power Technology Office

Jun 25, 2014
12 Resources
0 Stars
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Utah FORGE 6-3712: Report on a Data Foundation for Real-Time Identification of Microseismic Events

This submission is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of extracting events from the borehole seismic sensors. To be effective once deployed, the process ...

Williams, J. et al Global Technology Connection, Inc.

Jan 21, 2025
3 Resources
0 Stars
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Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) ...

Ayyanar, R. et al Arizona State University

Feb 01, 2025
12 Resources
0 Stars
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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...

Ahmmed, B. Stanford University

Apr 04, 2022
3 Resources
0 Stars
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Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging Software and Data

Software and testing data from the OH Hinsdale Wave lab for DOE-funded project on Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging. This project will perform foundational research and testing to accelerate the sector-wide development a...

Hollinger, G. et al Oregon State University

Oct 26, 2022
11 Resources
0 Stars
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WaterTAP3 Model Input Data for NAWI's Eight Source Water Baseline Analyses

This folder contains the input data for the WaterTAP3 model that was used for the eight NAWI (National Alliance for Water Innovation) source water baselines studies published in the Environmental Science and Technology special issue: Technology Baselines and Innovation Priorities ...

Miara, A. et al National Renewable Energy Laboratory

Feb 01, 2022
16 Resources
0 Stars
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NREL GIS data: Bhutan Wind Power Density at 50m Above Ground Level

GIS data for Bhutan's Wind Power Density at 50m Above Ground Level. NREL developed estimates of Bhutans wind resources at a spatial resolution of 1 km^2 using NREL's Wind Resource Assessment and Mapping System (WRAMS). Wind turbine output at a given site can be predicted using win...

Heimiller, D. et al National Renewable Energy Laboratory

Nov 25, 2014
3 Resources
0 Stars
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Wind Resources in Alaska

Wind resource data for Alaska and southeast Alaska, both high resolution wind resource maps and gridded wind parameters. The two high resolution wind maps are comprised of a grid of cells each containing a single value of average wind speed (m/s) at a hub height of 30, 50, 70, and...

AEDI, A. National Renewable Energy Laboratory

Dec 31, 2006
3 Resources
0 Stars
In curation

Wind Energy: Bans or Moratoriums (2022)

This dataset identifies counties and municipalities that had a wind energy ban or moratorium as of April 2022. A TIF data file and a PNG map of the data are provided, showing areas where wind energy bans or moratoriums exist across the contiguous United States. For further detail...

Geospatial Data Science, N. National Renewable Energy Laboratory (NREL)

Jan 01, 2024
3 Resources
0 Stars
Publicly accessible

Wind Turbine Road Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from roads based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from a road that an energy project may be developed, and these varied widely across the counties in which they exis...

Geospatial Data Science, N. National Renewable Energy Laboratory (NREL)

Jan 01, 2024
5 Resources
0 Stars
Publicly accessible

Wind Turbine Railroad Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from railroad based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from a railroad that an energy project may be developed, and these varied widely across the counties in which th...

Geospatial Data Science, N. National Renewable Energy Laboratory (NREL)

Jan 01, 2024
5 Resources
0 Stars
Publicly accessible

Wind Turbine Transmission Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from transmission based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from transmission infrastructure that an energy project may be developed, and these varied widely across the...

Geospatial Data Science, N. National Renewable Energy Laboratory (NREL)

Jan 01, 2024
5 Resources
0 Stars
Publicly accessible

Wind Turbine Structure Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from structures based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from a structure that an energy project may be developed, and these varied widely across the counties in which...

Geospatial Data Science, N. National Renewable Energy Laboratory (NREL)

Jan 01, 2024
5 Resources
0 Stars
Publicly accessible

Minnesota High Resolution Wind Resource

Annual average wind resource potential for Minnesota at a 50 meter height. This data set has been validated by NREL and wind energy meteorological consultants. *Note:* This data is not suitable for micro-siting potential development projects. ###Original data Data from [...