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United States County-Level Industrial Energy Use

Estimated industrial manufacturing agriculture construction and mining energy estimated by North American Industrial Classification System NAICS code county and fuel type for 2014. Additional disaggregation by end use e.g. machine drive process heating facility lighting is provide...

McMillan and NarwadeNational Renewable Energy Laboratory

Sep 27, 2018
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Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including...

Moraga, J. Colorado School of Mines

Apr 27, 2021
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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
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Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: brady_som_output.gri, brady_som_output.grd, brady_som_output.* desert_som_output.gri, desert_som_output.grd, desert_som_outpu...

Moraga, J. et al Colorado School of Mines

Sep 01, 2020
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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
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Fish Detection AI, sonar image-trained detection, counting, tracking models

Gutstein, S. et al Water Power Technology Office

Aug 25, 2024
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NAWI Seedling Project: Foundational Control Methods for Water Treatment Systems Online Actuator and Sensor Data

This repository contains online actuator and sensor data and associated meta-data collected during the NAWI-funded seedling project with title: Foundational Control Methods for Water Treatment Systems. For detailed information on each dataset, please check the README included there.

Villez, K. et al Oak Ridge National Laboratory

Jan 01, 2020
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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
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Reverse Osmosis Simulation Data

This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help im...

Nadakkal Appukuttan, S. et al National Renewable Energy Lab NREL

Apr 22, 2024
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Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-proces...

Moraga, J. et al Colorado School of Mines

Apr 27, 2021
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Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there ar...

Moraga, J. et al Colorado School of Mines

Apr 27, 2021
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Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (po...

Moraga, J. et al Colorado School of Mines

Apr 27, 2021
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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
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Wind for Schools Wind Turbine Data

This dataset is a series of wind turbine data collected for the Wind for Schools project. The U.S. Department of Energy funded the Wind for Schools project, which helped develop a future wind energy workforce by encouraging students at higher education institutions to join Wind Ap...

Summerville, B. et al National Renewable Energy Laboratory (NREL)

Jan 01, 2012
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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
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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
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In curation

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 [...