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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Introducing Nuclear Data Evaluations of Prompt Fission Neutron Spectra [Slides]

Nuclear data evaluations provide recommended data sets for nuclear data applications such as reactor physics, stockpile stewardship or nuclear medicine. The evaluated data are often based on information from multiple experimental data sets and nuclear theory using statistical methods. Therefore, they are collaborative efforts of evaluators, theoreticians, experimentalists, benchmark experts, statisticians and application area scientists. In this talk, an introductions is given to the field of nuclear data evaluation at the specific example of a recent evaluation of the outgoing neutron energy spectrum emitted promptly after fission from 239 Pu and induced by neutrons from thermal to 30 MeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

BENEFIT with Northeastern University: HVAC Hardware-in-the-Loop Experimental Testing of a Heat Pump and Air Conditioner

This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

42 ENGINEERING↗
DOE OSTI2024

Cambium 2023 Scenario Descriptions and Documentation

The National Renewable Energy Laboratory's (NREL's) Cambium data sets are annually released sets of simulated hourly emission, cost, and operational data for a range of modeled futures of the U.S. electric sector with metrics designed to be useful for long-term decision- making. The 2023 Cambium data set is the fourth annual release. The data sets are a companion product to NREL's Standard Scenarios, which are likewise released annually and are a set of projections of how the U.S. electric sector could evolve across a suite of different potential futures, but covering more scenarios with less temporal granularity (Gagnon et al. 2024). Information about Cambium and related publications can be found at https://www.nrel.gov/analysis/cambium.html, and the Cambium data sets can be viewed and downloaded at https://scenarioviewer.nrel.gov/. In this documentation, we describe Cambium 2023's scenarios, define the metrics, and document the Cambium-specific methods for calculating those metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2022

Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods

Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐ Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗
DOE OSTI2018

Calibration curves for commercial copper and aluminum alloys using handheld laser-induced breakdown spectroscopy

Handheld laser-induced breakdown spectroscopy (HH LIBS) was used to study the elemental composition of four copper alloys and four aluminum alloys to produce calibration curves. The HH LIBS instrument used is a SciAps Z-500, commercially available, that contains a class-1 solid-state laser with an output wavelength of 1532 nm, a laser energy of 5 mJ/pulse, and a pulse duration of 5 ns. Test samples were solid specimens comprising of copper and aluminum alloys and data were collected from the samples’ surface at three different locations, employing a 12-point-grid pattern for each data set. All three data sets of the spectra were averaged, and the intensity, corrected by subtraction of background, was used to produce the elemental calibration curves. Calibration curves are presented for the matrix elements, copper and aluminum, as well as several minor elements. The surface damage produced by the laser was examined by microscopy. The alloys were tested in air and in a glovebox to evaluate the instrument’s ability to identify the constituents within materials under different environmental conditions. The main objective of using this HH LIBS technology is to determine its capability to fingerprint the presence of certain elements related to subpercent level within materials in real time and in-situ, as a starting point for undertaking future complex material characterization work.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Power law inflation with electromagnetism

We generalize Ringström’s global future causal stability results (Ringström 2009) [11] for certain expanding cosmological solutions of the Einstein-scalar field equations to solutions of the Einstein–Maxwell-scalar field system. In particular, after noting that the power law inflationary spacetimes (M{sup n+1},g{sup -hat}, ϕ{sup -hat}) considered by Ringström (2009) in [11] are solutions of the Einstein–Maxwell-scalar field system (with exponential potential) as well as of the Einstein-scalar field system (with the same exponential potential), we consider (nonlinear) perturbations of initial data sets of these spacetimes which include electromagnetic perturbations as well as gravitational and scalar perturbations. We show that if (as in Ringström (2009) [11]) we focus on pairs of relatively scaled open sets U{sub R{sub 0}}⊂U{sub 4R{sub 0}} on an initial slice of (M{sup n+1},g{sup -hat}), and if we choose a set of perturbed data which on U{sub 4R{sub 0}} is sufficiently close to that of (M{sup n+1},g{sup -hat},ϕ{sup -hat}, A{sup -hat} = 0), then in the maximal globally hyperbolic spacetime development (M{sup n+1},g,ϕ,A) of this data via the Einstein–Maxwell-scalar field equations, all causal geodesics emanating from U{sub R{sub 0}} are future complete (just as in (M{sup n+1},g{sup -hat})). We also verify that, in a certain sense, the future asymptotic behavior of the fields in the spacetime developments of the perturbed data sets does not differ significantly from the future asymptotic behavior of (M{sup n+1},g{sup -hat}, ϕ{sup -hat}, A{sup -hat} = 0). -- Highlights: •We prove stability of expanding solutions of the Einstein–Maxwell-scalar field equations. •All nearby solutions are geodesically complete. •The topology of the initial slice is irrelevant to our stability results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Background evaluation for the neutron sources in the Daya Bay experiment

Here, we present an evaluation of the background induced by 241Am–13C neutron calibration sources in the Daya Bay reactor neutrino experiment. Furthermore, as a significant background for electron-antineutrino detection at 0.26 ± 0.12 detector per day on average, it has been estimated by a Monte Carlo simulation that was benchmarked by a special calibration data set. This dedicated data set also provides the energy spectrum of the background.

241AM-13C neutron source↗
DOE OSTI2012

REVISITING {rho}{sup 1} CANCRI e: A NEW MASS DETERMINATION OF THE TRANSITING SUPER-EARTH

We present a mass determination for the transiting super-Earth {rho}{sup 1} Cancri e based on nearly 700 precise radial velocity (RV) measurements. This extensive RV data set consists of data collected by the McDonald Observatory planet search and published data from Lick and Keck observatories. We obtained 212 RV measurements with the Tull Coude Spectrograph at the Harlan J. Smith 2.7 m Telescope and combined them with a new Doppler reduction of the 131 spectra that we have taken in 2003-2004 with the High-Resolution Spectrograph (HRS) at the Hobby-Eberly Telescope for the original discovery of {rho}{sup 1} Cancri e. Using this large data set we obtain a five-planet Keplerian orbital solution for the system and measure an RV semi-amplitude of K = 6.29 {+-} 0.21 m s{sup -1} for {rho}{sup 1} Cnc e and determine a mass of 8.37 {+-} 0.38 M {sub Circled-Plus }. The uncertainty in mass is thus less than 5%. This planet was previously found to transit its parent star, which allowed them to estimate its radius. Combined with the latest radius estimate from Gillon et al., we obtain a mean density of {rho} = 4.50 {+-} 0.20 g cm{sup -3}. The location of {rho}{sup 1} Cnc e in the mass-radius diagram suggests that the planet contains a significant amount of volatiles, possibly a water-rich envelope surrounding a rocky core.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Generating Synthetic Time Series Photovoltaic Data with Real-World Physical Challenges and Noise for Use in Algorithm Test and Validation

The PV Fleet Data Initiative and other projects seek the develop algorithms for automated analysis of PV time series data for extraction of statistical information and other parameters of the data such as degradation rates, soiling loss information, tracker performance, clipping or curtailment, system availability and other valuable information. While there is a vast body of PV data available for application of said extraction algorithms it is difficult to validate these algorithms because the true parameters to be extracted are not known. There has been a wide use of synthetic data in the literature for algorithm validation but this synthetic data is typically very bounded by the problem or topic at hand. The PV Fleet Data Initiative project has demonstrated that real time series PV data almost always includes a host of data quality and physical problems that, in reality, any automated PV abstraction algorithm must handle appropriately. For this reason, this work describes the development of a complex synthetic PV times series data set that includes data quality and physical problems that have been experienced in real world PV data. The various quality and physical problems are documented in the synthetic data so that users can test the validity of various PV extraction algorithms as well as develop new algorithms to solve problems this data set can support.

14 SOLAR ENERGY↗
DOE OSTI2024

Measuring Neutron Polarisation in Deuteron Photo-disintegration with the CLAS Start Counter [Thesis]

Deuteron photo-disintegration (γd → γp) is a reaction that represents the simplest case in which nuclear and hadron physics models can be tested. Despite this, associated polarization analyses are limited in terms of angular coverage and energy ranges, especially in observables related to the recoil neutron. This is largely due to a lack in dedicated polarimetry equipment, and represents a roadblock in global progress to understand high-energy phenomena such as hexaquarks, and quark-gluon degrees of freedom. To address this problem, this PhD thesis pioneers a new methodology for the parasitic measurement of nucleon polarization using kinematic reconstruction of (spin-dependent) nucleon-nucleus scattering of reaction products, prior to their detection in large acceptance particle detector apparatus. Following this novel approach, which requires no dedicated polarimeter, a determination of the double polarization observable, $C^n_{x'}$, from deuteron photo-disintegration is presented, using Jefferson Lab’s CLAS detector. The analysis utilizes the (n,p) charge exchange reaction in CLAS’s "start counter" (plastic scintillator) to determine the final state neutron polarizations. The results present the first ever data for this observable above 0.7 GeV (photon beam energy) and significantly extend the angular range of the world data set. This new data is largely statistically consistent with the previous measurement of $C^n_{x'}$ by Bashkanov et al . in the overlapping energy range of 0.4-0.7 GeV. It is planned for the statistical accuracy of the presented result to be increased by the inclusion of additional data. The analysis herein serves as a key proof of concept for future applications, including a recommended similar analysis to be implemented with data from the more modern CLAS12 detector. This paves the way for a plethora of additional analyses using existing data sets that would provide crucial new constraints for hadron and nuclear physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

GD358: Three Decades of Observations for the In-depth Asteroseismology of a DBV Star

Here, we report on the analysis of 34 years of photometric observations of the pulsating helium atmosphere white dwarf GD358. The complete data set includes archival data from 1982 to 2006, and 1195.2 hr of new observations from 2007 to 2016. From this data set, we extract 15 frequencies representing g-mode pulsation modes, adding 4 modes to the 11 modes known previously. We present evidence that these 15 modes are ℓ = 1 modes, 13 of which belong to a consecutive sequence in radial overtone k. We perform a detailed asteroseismic analysis using models that include parameterized, complex, carbon and oxygen core composition profiles to fit the periods. Recent spectroscopic analyses place GD358 near the red edge of the DBV instability strip, at 24,000 ± 500 K and a $\mathrm{log}\,{\text{}}g$ of 7.8 ± 0.08 dex. The surface gravity translates to a mass range of 0.455–0.540 ${M}_{\odot }$. Our best-fit model has a temperature of 23,650 K and a mass of 0.5706 ${M}_{\odot }$. That is slightly more massive than what is suggested by the most recent spectroscopy. We find a pure helium layer mass of 10–5.50, consistent with the result of previous studies and the outward diffusion of helium over time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

TP84227_FS83049_OPData

The data set represents the data used for the analysis of operational carbon emissions for a carbon case study of a typical production builder's home in two common climate zones, Climate Zone 2 (Warm) and CZ 5 (Cold). A data set for embodied carbon emissions analysis for the same project is in a separate workbook.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

TP84227_FS83049_ECData

The data set represents the data used for the analysis of embodied carbon emissions for a carbon case study of a typical production builder's home in two common climate zones, Climate Zone 2 (Warm) and CZ 5 (Cold). A data set for operational carbon emissions analysis for the same project is in a separate workbook.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗