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G2Aero Database of Airfoils Curated Airfoils
This dataset contains a curated set of 19,164 airfoil shapes from various applications and the data-driven design space of separable shape tensors (PGA space), which can be used as a parameter space for machine-learning applications focused on airfoil shapes.
We constructed the a...
Doronina, O. et al National Renewable Energy Lab NREL
Sep 24, 2024
3 Resources
0 Stars
Curated
3 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
8 Resources
0 Stars
Curated
Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)
The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios.
Sup3rCC is downscaled ...
Buster, G. et al The National Renewable Energy Lab (NREL)
Apr 19, 2023
7 Resources
1 Stars
Curated
7 Resources
1 Stars
Curated
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
Publicly accessible
6 Resources
0 Stars
Publicly accessible
Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files
This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification.
In this study, a machine-learning-assiste...
Jin, W. et al Idaho National Laboratory
Apr 15, 2022
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
Deep Direct-Use Feasibility Study Economic Analysis using GEOPHIRES for West Virginia University
This dataset contains all the inputs used and output produced from the modified GEOPHIRES for the economic analysis of base case hybrid GDHC system, improved hybrid GDHC system with heat pump and for hot water GDHC.
Software required: Microsoft Notepad, Microsoft Excel and GEOPHI...
Garapati, N. West Virginia University
Jan 09, 2020
8 Resources
0 Stars
Publicly accessible
8 Resources
0 Stars
Publicly accessible
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
6 Resources
0 Stars
Publicly accessible
Coupling Subsurface and Above-Surface Models for Optimizing the Design of Borefields and District Heating and Cooling Systems
Accurate dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange for seasonal energy storage. Current modeling approaches in building and district energy simulation tools typically consider heat conduct...
Hu, J. et al Lawrence Berkeley National Laboratory
Jan 31, 2022
10 Resources
0 Stars
Publicly accessible
10 Resources
0 Stars
Publicly accessible
An HPC-Based Hydrothermal Finite Element Simulator for Modeling Underground Geothermal Behavior with Example Simulations on The Treasure Island and UC Berkeley Campus
This submission contains the source code of the Hydrothermal Finite Element Simulator used for the Treasure Island and UC Berkeley campus geothermal simulation. It contains a report that summarizes the development and validation of this Hydrothermal Finite Element Simulator, with ...
Chen, K. et al Lawrence Berkeley National Laboratory
Aug 01, 2021
5 Resources
0 Stars
Publicly accessible
5 Resources
0 Stars
Publicly accessible
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
Publicly accessible
16 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
6 Resources
0 Stars
Publicly accessible
Imperial Valley Dark Fiber Project Continuous DAS Data
The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecomm...
Ajo-Franklin, J. et al Lawrence Berkeley National Laboratory
Nov 10, 2020
4 Resources
0 Stars
Publicly accessible
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
4 Resources
0 Stars
Publicly accessible
Demand-Side Grid (dsgrid) Highly-resolved, Long-term Energy Demand Projections for the Contiguous United States: DECARB Scenarios
Input and compiled datasets describing projected energy use by sector and for the power sector as a whole. Data are developed by a multi-national lab team as part of the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy (EERE) Office's DECARB project.
Hale, E. et al National Renewable Energy Laboratory
Jul 01, 2024
2 Resources
0 Stars
In progress
2 Resources
0 Stars
In progress
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
6 Resources
1 Stars
Curated
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
Publicly accessible
8 Resources
0 Stars
Publicly accessible
EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog from Double-Difference Seismic Tomography
This package contains a 3D Seismic velocity model and an updated microseismic catalog obtained for a double-difference seismic tomography study.
The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for we...
Chai, C. et al Oak Ridge National Laboratory
Jun 01, 2020
8 Resources
0 Stars
Publicly accessible
8 Resources
0 Stars
Publicly accessible
Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets
This dataset contains turbine and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in ...
Ramos, D. et al National Renewable Energy Laboratory
Feb 12, 2021
5 Resources
0 Stars
Publicly accessible
5 Resources
0 Stars
Publicly accessible
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
Publicly accessible
3 Resources
0 Stars
Publicly accessible
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
Publicly accessible
3 Resources
0 Stars
Publicly accessible
NREL Eagle supercomputer jobs
High performance computing dataset with 11M+ jobs from NREL's Eagle supercomputer. These jobs were submitted to run on Eagle between Nov 2018 and Feb 2023. The data are sufficiently anonymized and do not include sensitive user or project data. HPC research community does not have ...
Duplyakin, D. and Menear, K. National Renewable Energy Laboratory
Feb 28, 2023
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
HERO WEC V1.0 Electrical PTO Deployment Data and Analysis
This submission includes the processed and raw field electrical data from NREL's August 2022 HERO WEC (hydraulic and electric reverse osmosis wave energy converter) deployment at Jennette's Pier for the electrical PTO (power take-off).
Nakhai, A. et al National Renewable Energy Laboratory
Aug 12, 2022
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
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
0 Stars
Publicly accessible
5 Resources
0 Stars
Publicly accessible
Sup3rWind Data (CONUS)
This data contains paired European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK) images for 2007 and 2010 over two regions in the US, with domain sizes ~800x800 (latitudes from 25.89 to 41.58, and long...
Sinha, S. et al National Renewable Energy Lab NREL
Jul 16, 2024
6 Resources
0 Stars
Curated
6 Resources
0 Stars
Curated
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
Publicly accessible
3 Resources
0 Stars
Publicly accessible