ASSIST 10 (NOAA postcampaign) / Raw Data
This dataset contains AWAKEN - NREL Thermodynamic profiler (Assist II-10) raw summary data collected at NOAA in December 2023 - January 2024.
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SEARCH · Engineering Papers
Results for “nrel”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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5-minute Wind Power Data based on WFIP2 WRF Simulation
The second Wind Forecast Improvement Project (WFIP2) was a public-private partnership funded by the U.S. Department of Energy and NOAA, aimed at enhancing the forecast skill of numerical weather prediction models for turbine-height winds in regions with complex terrain. An 18-month Weather Research and Forecasting (WRF) model simulation was conducted over the Pacific Northwest, with model outputs validated against observational data collected during WFIP2. Simulated wind speeds were used to estimate wind power generation using reV (the Renewable Energy Potential Model developed by NREL) at ten wind project sites. Two sets of results were produced: one using wind speeds extracted from the model grid cell at the project centroid, and another using wind speeds from the actual turbine locations. For each dataset, power output was calculated using both actual turbine-specific power curves and nine generic power curves to convert wind speed into power.
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Front-of-Meter Model Results
These files contains aggregations of key variables from the NREL Distributed Wind Futures Study using full parcel level data. These variables describe total technical and economic potential for distributed wind turbine deployment. Aggregations are available at the (1) county, (2) zipcode (zip code tabulation area or zcta), and (3) US Census block group level. Each scenario is coded with the scenario name (e.g., baseline) and year (e.g., 2022). Those files postfixed with 'econpot' contain results for only those parcels that are economically viable while the files postfixed with 'techpot' include results for all parcels that are technically feasible. Hence these correspond to technoeconomic and technical potential respectively. The data are available as CSV or Geopackage. Columns in the files are as follows: * geoid: geographic identifier (FIPS code or similar) * min_techpot_sum_kw: technical potential for all parcels in kW using turbines downsized to demand when appropriate * max_techpot_sum_kw: technical potential for all parcels in kW without downsizing turbines * aep_sum_kwh: annual energy production estimate in kWh * cf_mean_ratio: mean capacity factor * lcoe_mean_cents_per_kwh: mean levelized cost of energy for parcels in geography in cents per kWh * lcoe_std_cents_per_kwh: standard deviation of the above * parcel_area_sum_acres: total area of viable parcels in acres * n_turbines: number of cited turbines (one per viable parcel currently) Note: These are preliminary results from the full-parcel 2024 update of the Distributed Wind Energy Futures study. Please take care when making use of the data, and feel free to contact the team with any questions. Full documentation in support of these data is in progress and will follow.
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Behind-the-Meter Model Results
These files contains aggregations of key variables from the NREL Distributed Wind Futures Study using full parcel level data. These variables describe total technical and economic potential for distributed wind turbine deployment. Aggregations are available at the (1) county, (2) zipcode (zip code tabulation area or zcta), and (3) US Census block group level. Each scenario is coded with the scenario name (e.g., baseline) and year (e.g., 2022). Those files postfixed with 'econpot' contain results for only those parcels that are economically viable while the files postfixed with 'techpot' include results for all parcels that are technically feasible. Hence these correspond to technoeconomic and technical potential respectively. The data are available as CSV or Geopackage. Columns in the files are as follows: * geoid: geographic identifier (FIPS code or similar) * min_techpot_sum_kw: technical potential for all parcels in kW using turbines downsized to demand when appropriate * max_techpot_sum_kw: technical potential for all parcels in kW without downsizing turbines * aep_sum_kwh: annual energy production estimate in kWh * cf_mean_ratio: mean capacity factor * lcoe_mean_cents_per_kwh: mean levelized cost of energy for parcels in geography in cents per kWh * lcoe_std_cents_per_kwh: standard deviation of the above * parcel_area_sum_acres: total area of viable parcels in acres * n_turbines: number of cited turbines (one per viable parcel currently)
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RAAW
High-fidelity wind turbine modeling advances necessitate model validation datasets of equally high fidelity. The Rotor Aerodynamics, Aeroelastics & Wake (RAAW) project was designed to address this need. This three-year project runs from October 2021 through October 2024 starting with a field data collection period followed by model validation activities. RAAW is a partnership between GE Renewable Energy and the United States Department of Energy (DOE) through the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories. The field experiment is designed to make detailed measurements of the inflow, wind turbine response, and the resulting wake. The experimental dataset will be suitable for validating wind turbine models across the fidelity spectrum from actuator disk methods to blade-resolved codes.
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Data for Validating Models for PV Module Performance
This data encompasses performance data measured for flat-plate photovoltaic (PV) modules installed in Cocoa, Florida; Eugene, Oregon; and Golden, Colorado. The data include PV module current-voltage curves and associated meteorological data for approximately one-year periods. The data was acquired with the NREL Performance and Energy Rating Testbed (PERT) and the mobile Performance and Energy Rating Testbed (mPERT).
PV, Validation, IV Curves, Field performance, mPER↗
Design-Space Exploration for Inverse-Design of Wind Turbine Blades Using Data-Driven Methods
The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.
aerodynamic design methods↗
PV Rooftop Database for Puerto Rico (PVRDB-PR)
The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.
Array↗
Photovoltaic Data Acquisition (PVDAQ) Public Datasets
The NREL PVDAQ is a large-scale time-series database containing system metadata and performance data from a variety of experimental PV sites and commercial public PV sites. The datasets are used to perform on-going performance and degradation analysis. Some of the sets can exhibit common elements that effect PV performance (e.g. soiling). The dataset consists of a series of files devoted to each of the systems and an associated set of metadata information that explains details about the system hardware and the site geo-location. Some system datasets also include environmental sensors that cover irradiance, temperatures, wind speeds, and precipitation at the site.
Array↗
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 technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...
aerodynamics↗
LBNL Fault Detection and Diagnostics Datasets
These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.
AC↗
ARPA-E PERFORM datasets
Time-coincident load, wind, and solar data including actual and probabilistic forecast datasets at 5-min resolution for ERCOT, MISO, NYISO, and SPP. Wind and solar profiles are supplied for existing sites as well as planned sites based on interconnection queue projects as of 2021. For ERCOT actuals are provided for 2017 and 2018 and forecasts for 2018, and for the remaining ISOs actuals are provided for 2018 and 2019 and forecasts for 2019. There datasets were produced by NREL as part of the ARPA-E PERFORM project, an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. For more information on the datasets and methods used to generate them see https://github.com/PERFORM-Forecasts/documentation.
5-min data↗
ResStock: Annual Baseline Results with Component Loads
The ResStock Analysis Tool was developed by NREL with support from the U.S. Department of Energy to provide a new approach to large-scale residential analysis by combining large public and private data sources, statistical sampling, detailed sub hourly building simulations, and high-performance computing. This combination achieves unprecedented granularity and accuracy in modeling the diversity of the housing stock and the distributional impacts of building technologies in different communities. The annual baseline energy results from a national-scale ResStock run use typical meteorological year 3 (TMY3) files for energy simulations. Results include heating and cooling loads for individual components of each building. Component loads describe the heating/cooling load that can be attributed to specific elements of a home, such as heat transfer through walls or internal gains. Additionally, these results include the standard ResStock outputs for housing characteristics and numerous energy outputs by end-use and fuel. A snapshot of the ResStock version used to produce this data, including a configuration file for the run can be found using the Source Code resource link.
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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 exhibit the necessary rigidity to maintain their optimal optical performance in windy conditions. Over two years, NREL conducted comprehensive field measurements of the atmospheric turbulent wind conditions and the resulting structural wind loads on parabolic troughs at the Nevada Solar One (NSO) plant. The measurement set-up included meteorological masts and structural load sensors on four trough rows. Additionally, we commissioned a lidar scanning the horizontal plane over the trough field. This data set catalogs the high-resolution data set characterizing the complex flow field and resulting structural loads on parabolic trough collectors. By providing this first-of-its-kind data set to the CSP community, we aim to enhance the community's understanding of wind-loading experienced by CSP collector structures. This data set will also help design next-generation solar collectors and photovoltaic trackers.
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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 airfoil dataset in two main stages. First, we identified 13 baseline airfoils from the NREL 5MW and IEA 15MW reference wind turbines. We reparameterized these shapes using least-squares fits of 8-order CST parametrizations, which involve 18 coefficients. By uniformly perturbing all 18 CST coefficients by +/-20% around each baseline airfoil, we generated 1,000 unique airfoils. Each airfoil was sampled with 1,001 shape landmarks whose x-coordinates followed a cosine distribution along the chord. This process resulted in a total of 13,000 airfoil shapes, each with 1,001 landmarks. In the second phase, we gathered additional airfoils from the extensive BigFoil database, which consolidates data from sources such as the University of Illinois Urbana-Champaign (UIUC) airfoil database, the JavaFoil database, the NACA-TR-824 database, and others. We undertook a thorough pre-processing step to filter out shapes with sparse, noisy, or incomplete data. We also removed airfoils with sharp leading edge and those exceeding our threshold for trailing edge thickness. Additionally, we thinned out the collection of NACA airfoils-- parametric sweeps of NACA airfoils with increasing thickness and camber present in BigFoil database-- by selecting every fourth step in the parameter sweeps. Finally, we regularized the airfoils by reparametrizing them with an 8-order CST parametrization (with 1,001 shape landmarks with x coordinated following cosine distribution along the chord) and removing airfoils with high reconstruction errors. This data pre-processing resulted in a set of 6,164 airfoils. In total, our curated airfoil dataset comprises 19,164 airfoils, each with 1,001 landmarks, and is stored in the curated_airfoils.npz file. Using this curated airfoil dataset, we utilized the separable shape tensors framework to develop a data-driven parameterization of airfoils based on principal geodesic analysis (PGA) of separable shape tensors. This PGA space is provided in PGAspace.npz file.
airfoils↗
National Transmission Planning Study - Long-Term Modeling Results
The National Transmission Planning Study (NTP Study) analyzed the transformation needed to ensure the U.S. transmission system continues to reliably meet electricity demand as the power sector evolves. The study linked several long- and short-term power systems models to test numerous interregional and regional transmission buildout scenarios. The results included in this dataset are from the long-term capacity expansion modeling, completed with NREL's ReEDS model. This includes nearly 100 future transmission scenarios with a wide range of economic, reliability, and resilience conditions. The dataset includes all the results visualized in the Tableau Public site, as linked below.
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U.S. Wind Siting Regulation and Zoning Ordinances (2025)
A machine readable collection of documented wind siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled using the Infrastructure Continuous Ordinance Mapping for Planning and Siting Systems (INFRA-COMPASS) tool, which leverages Large Language Models (LLMs) to automate the collection of local codes and ordinances applicable to energy infrastructure. URLs for the ordinance source documents are included in the Wind Ordinances spreadsheet. The GeoPackage file included below contains the jurisdiction shapes for each ordinance. Note that the GeoPackage file is formatted for ingestion by NREL's reVX setbacks tool and therefore does not contain any of the state-level regulations. NOTE: This data was collected with the help of generative AI. The Large Language Models used for this effort make mistakes. Always validate the data for critical use cases. This data is an update to a previously developed database of wind ordinances found in OEDI Submission 5733: see the "U.S. Wind Siting Regulation and Zoning Ordinances 2022" link below. INFRA-COMPASS version used for collection: v0.8.2 LLMs used for collection: GPT-4.1, GPT-4.1 mini, GPT-4.1 nano, GPT-4o mini
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Modeling, Load Profile Validation, and Assessment of Solar-Rooftop Energy Potential for Low-and-Moderate-Income Communities in the Caribbean
This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community.
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