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Weatherization Assistance Program

The U.S. Department of Energy's Weatherization Assistance Program (WAP) was created in 1976 to assist low-income families who lacked resources to invest in energy efficiency. WAP is operated in all 50 states, the District of Columbia, Native American tribes, and U.S. Territories. ...
Adams, R. and (EERE), O. Office of Energy Efficiency & Renewable Energy
Nov 25, 2014
1 Resources
0 Stars
In curation

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
6 Resources
0 Stars
Publicly accessible

Active Source Seismic (Ultrasonic) Data from Double-Direct Shear Lab Experiments

Active source ultrasonic data from lab experiments p5270 and p5271 including raw waveforms (WF) and mechanical data (mat). From the PSU team working on the "Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties" project. The fric...
Marone, C. Pennsylvania State University
May 05, 2021
1 Resources
0 Stars
Publicly accessible

StingRAY WEC Risk Register

Risk Registers for major subsystems of the StingRAY WEC completed in compliance with the DOE Risk Management Framework developed by NREL.
Rhinefrank, K. Columbia Power Technologies, Inc.
Feb 24, 2017
18 Resources
0 Stars
Publicly accessible

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-...
Lu, G. et al University of Pittsburgh
Aug 30, 2024
2 Resources
0 Stars
Publicly accessible

Error-Level-Controlled Synthetic Forecasts for Renewable Generation

Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the imp...
Zhang, X. et al National Renewable Energy Laboratory (NREL)
Jun 01, 2021
3 Resources
0 Stars
Publicly accessible

StingRAY Updated WEC Risk Registers

Updated Risk Registers for major subsystems of the StingRAY WEC completed according to the methodology described in compliance with the DOE Risk Management Framework developed by NREL.
Rhinefrank, K. and Ondusko, M. Columbia Power Technologies, Inc.
Jun 27, 2018
17 Resources
0 Stars
Publicly accessible

Federal Energy Management Program

FEMP EISA 432 Compliance Tracking System (CTS) enables the public to analyze and generate custom reports of CTS facility level data. This data warehouse includes only non-restricted CTS data. The data warehouse consists of five data sets (or "data cubes") that may be used to build...
Temper, C. and (EERE), O. Office of Energy Efficiency & Renewable Energy
Nov 25, 2014
1 Resources
0 Stars
In curation

USDA Census of Irrigation

The 2018 Irrigation and Water Management Survey (formerly called the Farm and Ranch Irrigation Survey) is a follow-on to the 2017 Census of Agriculture by the U.S. Department of Agriculture (USDA). This survey provides the only comprehensive information on irrigation activities an...
Census of Irrigation, U. U.S. Department of Agriculture
Oct 19, 2020
5 Resources
0 Stars
Publicly accessible

GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information...
Vesselinov, V. Los Alamos National Laboratory
Mar 29, 2021
4 Resources
0 Stars
Publicly accessible

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
0 Stars
Publicly accessible

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
3 Resources
0 Stars
Publicly accessible

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
3 Resources
0 Stars
Publicly accessible

National Forest System Lands in geothermal potential area

A GIS layer from the BLM nationwide PEIS of lands within the U.S. National Forest system that contain geothermal potential. The GIS layer contains .SBX, .XML, .SHX, .DBF (.XLS), .PRJ, .SBN, and .SHP data. ### License Info Made open by BLM. http://www.doi.gov/privacy.cfm
Hill, G. and (BLM), U. National Renewable Energy Laboratory
Jul 29, 2014
2 Resources
0 Stars
In curation

Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments

Previous moderate and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable mode...
Mordensky, S. et al United States Geological Survey
Feb 07, 2023
7 Resources
0 Stars
Publicly accessible

USGS Geophysics, Heat Flow, and Slip and Dilation Tendency Data used in Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project, with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geoph...
DeAngelo, J. et al Nevada Bureau of Mines and Geology
Jun 01, 2021
1 Resources
0 Stars
Publicly accessible

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
3 Resources
0 Stars
Publicly accessible

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
1 Stars
Publicly accessible

Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States

This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events....
Gunda, T. and Jackson, N. Sandia National Laboratories
Apr 01, 2021
2 Resources
0 Stars
Publicly accessible

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
Publicly accessible

Gulf of Mexico Outer Continental Shelf Mapping Data

The US Department of Interior's (DOI) Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE) published geographic data of offshore activities in the Gulf of Mexico Outer Continental Shelf (OCS) area. The data were built from the Technical Information Management Sys...
Hallett, K. and (BOEMRE), U. National Renewable Energy Laboratory
May 30, 2007
14 Resources
0 Stars
In curation

Utah FORGE 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...
Mustafa, A. et al Battelle Memorial Institute
Sep 28, 2023
3 Resources
0 Stars
Publicly accessible

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework ...
Buster, G. et al Upflow
Jun 30, 2021
11 Resources
0 Stars
Publicly accessible

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be develop...
Taverna, N. et al National Renewable Energy Laboratory
Jun 30, 2023
4 Resources
0 Stars
Publicly accessible
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