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DOE OSTI2012

No file left behind - monitoring transfer latencies in PhEDEx

The CMS experiment has to move Petabytes of data among dozens of computing centres with low latency in order to make efficient use of its resources. Transfer operations are well established to achieve the desired level of throughput, but operators lack a system to identify early on transfers that will need manual intervention to reach completion. File transfer latencies are sensitive to the underlying problems in the transfer infrastructure, and their measurement can be used as prompt trigger for preventive actions. For this reason, PhEDEx, the CMS transfer management system, has recently implemented a monitoring system to measure the transfer latencies at the level of individual files. For the first time now, the system can predict the completion time for the transfer of a data set. The operators can detect abnormal patterns in transfer latencies early, and correct the issues while the transfer is still in progress. Statistics are aggregated for blocks of files, recording a historical log to monitor the long-term evolution of transfer latencies, which are used as cumulative metrics to evaluate the performance of the transfer infrastructure, and to plan the global data placement strategy. In this contribution, we present the typical patterns of transfer latencies that may be identified with the latency monitor, and we show how we are able to detect the sources of latency arising from the underlying infrastructure (such as stuck files) which need operator intervention.

Chwalek, T.↗

DOE OSTI2020

City-Scale Building Anthropogenic Heating during Heat Waves

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

HydraGNN_OPF_GFM_2026 - Ensemble of predictive graph foundation models for power grid applications

This dataset supports research on graph foundation models for optimal power flow (OPF) on electric grids using HydraGNN. It contains heterogeneous graph representations of PGLib-OPF cases spanning systems from 14 to 13,659 buses, together with packed HDF5 datasets for pretraining, feasibility classification, and N-1 contingency analysis. The release includes OPF solution data, downstream fine-tuning datasets, pretrained HeteroSAGE and HeteroHEAT model checkpoints, hyperparameter-optimization summaries across multiple heterogeneous GNN architectures, and aggregated fine-tuning results for sample-efficiency studies. The dataset is designed to enable scalable training, evaluation, and transfer-learning studies for OPF surrogate modeling, including node-level AC-OPF solution prediction, graph-level prediction, feasibility classification, operating-condition generalization, and contingency-response tasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ~ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ~ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Comparison of observed and simulated spatial patterns of ice microphysical processes in tropical oceanic mesoscale convective systems

To equitably compare the spatial pattern of ice microphysical processes produced by three microphysical parameterizations with each other, observations, and theory, simulations of tropical oceanic mesoscale convective systems (MCSs) in the Weather Research and Forecasting (WRF) model were forced to develop the same mesoscale circulations as observations by assimilating radial velocity data from a Doppler radar. The same general layering of microphysical processes was found in observations and simulations with deposition anywhere above the 0°C level, aggregation at and above the 0°C level, melting at and below the 0°C level, and riming near the 0°C level. Thus, this study is consistent with the layered ice microphysical pattern portrayed in previous conceptual models and indicated by dual-polarization radar data. Spatial variability of riming in the simulations suggests that riming in the midlevel inflow is related to convective-scale vertical velocity perturbations. Lastly, this study sheds light on limitations of current generally available bulk microphysical parameterizations. In each parameterization, the layers in which aggregation and riming took place were generally too thick and the frequency of riming was generally too high compared to the observations and theory. Additionally, none of the parameterizations produced similar details in every microphysical spatial pattern. Discrepancies in the patterns of microphysical processes between parameterizations likely factor into creating substantial differences in model reflectivity patterns. It is concluded that improved parameterizations of ice-phase microphysics will be essential to obtain reliable, consistent model simulations of tropical oceanic MCSs.

58 GEOSCIENCES↗

DOE OSTI2021

Deterioration of concrete due to ASR: Experiments and multiscale modeling

The process of ASR (Alkali-Silica Reaction) induced expansion and damage in pavement concrete specimens is investigated using laboratory experiments and computational modeling. In the experimental program, the concrete specimens are subject to CS-CPT (climate simulation concrete prism test) to obtain ASR induced expansion with and without external supply of alkali. The dissolution rates of the granodiorite used in the concrete mix and the gel formation rates are determined under concrete-like conditions (pH 13.8, with/without Ca(OH){sub 2} and NaCl) at different temperatures. A micromechanics based computational model with aggregate-scale diffusion and reaction kinetics coupled to an Eigenstrain based micromechanics damage model is developed for the simulation of ASR induced expansion and damage. Data from the experimental program are used to calibrate and validate the computational model. Model predictions show that for the given concrete mixture, ASR induced expansion in the specimen exposed to water is predominantly governed by microcracking in the aggregate, while the expansion in the specimen subjected to external alkali supply is governed by microcracking in both the aggregates and the cement paste.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Effect of nanoparticles size and polyelectrolyte on nanoparticles aggregation in a cellulose fibrous matrix

Controlling nanoparticles (NPs) aggregation in cellulose/NPs composites allows to optimise NPs driven properties and their applications. Polyelectrolytes are used to control NPs aggregation and their retention within the fibrous matrix. Here in this study, we aim at evaluating how a polyelectrolyte (Cationic Polyacrylamide; CPAM, molecular weight: 13 MDa, charge: 50%, Radius of gyration: 30–36 nm) adsorbs and re-conforms onto the surface of silica(SiO 2 ) NPs differing in diameter (8, 22 and 74 nm) and to investigate the respective NPs aggregation in cellulose matrices. SEM shows the local area distribution of NPs in composites. Ultra-SAXS (USAXS) allows to evaluate the average NPs size distribution and the inter-particle interactions at length scale ranging from 1 to 1000 nm. USAXS data analysis reveals that CPAM covers multiple NPs of the smaller diameter (8 nm), presumably with a single chain to form large size NPs aggregates. As the NPs diameter is increased to 22 nm, CPAM re-conforms over NP surface forming a large shell of thickness 5.5 nm. For the composites with NPs of diameter 74 nm, the CPAM chain re-conforms further onto NP surface and the surrounding shell thickness decreases to 2.2 nm. Lastly, structure factor analysis reveals higher structural ordering for NPs as increases their diameter, which is caused by different conformations adopted by CPAM onto NPs surface.

36 MATERIALS SCIENCE↗

DOE OSTI2017

FASTMap v. 2010.01

FASTMap is mapping application available for the web or on mobile devices (IOS and Android) that browses geospatial data and produces detailed reports of objects within any area of analysis. FASTMap can access any geospatial dataset. The software can provide immediate access to the selected data through a fully symbolized interactive mapping interface. FASTMap can load arbitrary contours that represent a region of interest and can dynamically identify and geospatially select objects that reside within the region. The software can produce a report listing the objects and aggregations for the region, as well as producing publication quality maps. FASTMap also has the ability to post and maintain authored maps, any GIS data included in the map, areas of interest, as well as any titles, and labels. These defining ingredients of a map are called map contexts. These mao contexts can be instantly broadcast via the internet through any of an infinite number of named channels to small or large numbers of users monitouring any of the channels being posted to, so a user can author a map and immediately share that map with others instantly, whether they are on traditional desktop computer, laptop, mobile tablet or smartphone. Further, users receiving broadcast maps can also alter the maps can also alter the maps, or create new ones and publish back to the channel in a collaborative manner. FASTMap can be configured to access virtually any geospatial data.

Bynum, Leo↗

DOE OSTI2014

Mixed Aggregates of 1-Methoxyallenyllithium with Lithium Chloride

A combined computational and 13 C NMR study was used to investigate the formation of mixed aggregates of 1-methoxyallenyllithium and lithium chloride in tetrahydrofuran (THF) solution. The observed and calculated chemical shifts, as well as the calculated free energies of mixed aggregate formation (MP2/6-31+G(d)), are consistent with the formation of a mixed dimer as the major species in solution. Free energies of mixed dimer, trimer, and tetramer formation were calculated by using the B3LYP and MP2 methods and the 6-31+G(d) basis set. The two methods generated different predictions of which mixed aggregates will be formed, with B3LYP/6-31+ G(d) favoring mixed trimers and tetramers in THF solution, and MP2/6-31+G(d) favoring mixed dimers. Formation of the sterically unhindered mixed dimers is also consistent with the enhanced reactivity of these compounds in the presence of lithium chloride. The spectra are also consistent with some residual 1-methoxyallenyllithium tetramer, as well as small amounts of higher mixed aggregates. Although neither computational method is perfect, for this particular system, the calculated free energies derived using the MP2 method are in better agreement with experimental data than those derived using the B3LYP method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Synergistic Extraction of Dysprosium and Aggregate Formation in Solvent Extraction Systems Combining TBP and HDBP

During treatment of nuclear fuel in the Plutonium/URanium EXtraction (PUREX) process, the extractant tri-n-butyl phosphate (TBP) is known to degrade to dibutylphosphoric acid (HDBP), which increases the extraction of metal ions, thereby inhibiting their stripping from the organic phase. To better understand this phenomenon, we investigated how mixtures of TBP and HDBP influenced the extraction of metal, nitric acid, and water, and correlated the results to aggregated structures in the organic phase. The mole ratios of TBP-HDBP mixtures had a non-linear effect on the extraction of Dy 3+ and water from 0.2 M HNO 3 , indicating synergism. In 2 M HNO 3 , the TBP:HDBP mole ratio had a more linear relation to Dy 3+ and water extraction, so the synergistic effect was less pronounced than in the low acid system. The extraction of nitric acid showed no synergistic effect and follows closely what would be expected in a system using TBP only. The small-angle X-ray scattering (SAXS) data of the 0.2 M acid system showed maximum contrast at a TBP:HDBP mole ratio of 0.25, so that the synergistic mixture is also the most aggregated at 0.2 M acid. The 2 M acid system also showed that the mixed system is more aggregated than the end members, although this does not result in peak extraction. Previous studies of synergistic extraction of metal cations explain the enhanced extraction by increased dehydration of the metal ion. Although our data do not rule out the formation of mixed complexes according to the classical mechanism of synergism, our evidence of increased water extraction and aggregate formation in systems combining TBP and HDBP are complementary to the metal-centric dehydration aspects of the process. The findings in this study give insights into the complex chemistry of solvent extraction, thus providing a possible link between formation of aggregates in the organic phase and synergistic extraction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Trustworthiness modeling and evaluation for a nearly autonomous management and control system

The Nearly Autonomous Management and Control (NAMAC) system supports the advanced reactor operation by recommending control actions to operators based on real-time measurements and digital twins (DTs) learning from the knowledge base. To enable the safe and reliable use of autonomous technologies, NAMAC and its recommendations should be trustworthy to operators and regulators at both the design and operation stages. This study proposes a NAMAC trustworthiness modeling and evaluation framework supported by trustworthiness ontologies and evidence-based approaches. The development-time and run-time ontologies are separately constructed and then converted to Bayesian networks to quantitatively evaluate the NAMAC trustworthiness. This evaluation is demonstrated by collecting and characterizing evidence from NAMAC practices, such as the development and assessment of the NAMAC system, data coverage assessment, and the training and optimizations of neural-network-based DTs. Our proposed approach can aggregate various trustworthiness attributes of complex artificial-intelligence-supported systems for safety-critical applications. It also considers the interaction between different DTs and extends beyond the trustworthiness evaluation of a single DT. In conclusion, the evidence-based method enhances the transparency of the trustworthiness modeling and evaluation processes and helps identify uncertainties and subjectivity involved in the processes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

Quantitative assessment of alkali-reactive aggregate mineral content through XRD using polished sections as a supplementary tool to RILEM AAR-1 (petrographic method)

The mineral content of 5 aggregate samples from 4 different countries, including reactive and non-reactive aggregate types, was assessed quantitatively by X-ray diffraction (XRD) using polished sections. Additionally, electron probe microanalyzer (EPMA) mapping and cathodoluminescence (CL) were used to characterize the opal-CT identified in one of the aggregate samples. Critical review of results from polished sections against traditionally powdered specimen has demonstrated that for fine-grained rocks without preferred orientation the assessment of mineral content by XRD using polished sections may represent an advantage over traditional powder specimens. Comparison of data on mineral content and silica speciation with expansion data from PARTNER project confirmed that the presence of opal-CT plays an important role in the reactivity of one of the studied aggregates. Used as a complementary tool to RILEM AAR-1, the methodology suggested in this paper has the potential to improve the strength of the petrographic method.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Electrification Futures Study Flexible Load Profiles

This data set includes hourly profiles for flexible load developed for the Electrification Futures Study (EFS). The load profiles represent projected end-use electricity demand that is assumed to be flexible (i.e., can be shifted throughout a day) for various scenarios of flexibility (Base, Enhanced), electrification (Reference, Medium, High), and technology advancement (Slow, Moderate, Rapid), and were developed as inputs into the ReEDS model. The quantity of flexible load is estimated using assumptions on the level of flexibility and customer participation within each subsector modeled in the EFS. Detailed assumptions and modeling implementation will be documented in ongoing EFS analyses. Flexible load profiles are provided for a subset of years (2018, 2020, 2024, 2030, 2040, 2050) and are aggregated to the state and sector level. Total electricity load profiles can be found in a related EFS data set (https://dx.doi.org/10.7799/1593122). NOTE: Due to the file size, Mac users may experience issues decompressing the zip files using the Mac Archive Utility. In those cases, decompressing using the command line is recommended. - Mai, Trieu, Paige Jadun, Jeffrey Logan, Colin McMillan, Matteo Muratori, Daniel Steinberg, Laura Vimmerstedt, Ryan Jones, Benjamin Haley, and Brent Nelson. 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. National Renewable Energy Laboratory. NREL/TP-6A20-71500. https://doi.org/10.2172/1459351. - Murphy, Caitlin, Trieu Mai, Yinong Sun, Paige Jadun, Matteo Muratori, Brent Nelson, Ryan Jones. Forthcoming. Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States. National Renewable Energy Laboratory. - Sun, Yinong, Paige Jadun, Brent Nelson, Matteo Muratori, Caitlin Murphy, Jeffrey Logan, and Trieu Mai. Forthcoming. Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification. National Renewable Energy Laboratory.

buildings↗

DOE OSTI2022

Evaluation of Aggregated EV Flexibility With TSO-DSO Coordination

Electric vehicles (EVs) are becoming a promising source of grid ancillary services due to the temporal and spatial charging flexibility, quick response and storage capability. Such advantages are increasing with government policy promotion and technology improvement. However, the exploration of EV flexibility requires the coordination of both transmission system operators (TSOs) and distribution system operators (DSOs), to ensure the safe and reliable operation of power network. In this paper, we propose a coordinated evaluation method that determines the optimal utilization of EV temporal flexibility without compromising EV owners’ usage. At the distribution level, DSOs first evaluate EV aggregators’ operational boundaries to exploit distribution level services. At the transmission level, TSOs then determine EV charging schedules and ancillary service capacity simultaneously, taking into account the requirement from DSOs. Here, we validate the model in a case study using the IEEE 123 node test feeder and EV charging sessions obtained from a transportation simulation tool that uses real-world data.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

The second most disastrous windstorm of the nineteenth century in the Czech Lands, 26-27 October 1870

One of the most disastrous windstorms to take place over the Czech Lands occurred on the night of 26/27 October 1870. It is here analysed through the use of documentary data (narrative sources, newspapers, forestry journals, printed documents) and systematic meteorological observations (wind force and direction). Combining this evidence with information derived from an atmospheric reanalysis dataset allows the severity of the windstorm to be attributed to the passage of a cold front, a frontal system associated with a secondary low in a typically storm-prone synoptic environment. Its social impacts were characterised by great material damage, particularly to buildings and other structures, trees and forests. These are recorded not only for 174 places around the countryside and lesser settlements of the Czech Lands, but also for 28 city quarters in Prague, the capital city. The windstorm occurred in the night hours, so only a few people were killed or injured. However, the 1870 windstorm totally devastated many forested areas of the Šumava Mts. in south-west Bohemia. Damage to forests in other parts of the Czech Lands was also severe, but difficult to quantify exactly for lack of high-resolution spatial data. Because this windstorm followed only shortly upon a previous similarly disastrous wind event on 7 December 1868, the enormous quantity of windthrown wood in forests, which simply could not be fast-processed, contributed significantly to a subsequent bark-beetle infestation calamity in the 1870s. In certain forest stands, imprints of these aggregate effects appear to this day. The central-European scale of 1870 windstorm is also well documented by meteorological and documentary data from Germany, Austria and Slovakia.

Meteorology & Atmospheric Sciences↗

DOE OSTI2014

OTRA-THS MAC to reduce Power Outage Data Collection Latency in a smart meter network

The deployment of advanced metering infrastructure by the electric utilities poses unique communication challenges, particularly as the number of meters per aggregator increases. During a power outage, a smart meter tries to report it instantaneously to the electric utility. In a densely populated residential/industrial locality, it is possible that a large number of smart meters simultaneously try to get access to the communication network to report the power outage. If the number of smart meters is very high of the order of tens of thousands (metropolitan areas), the power outage data flooding can lead to Random Access CHannel (RACH) congestion. Several utilities are considering the use of cellular network for smart meter communications. In 3G/4G cellular networks, RACH congestion not only leads to collisions, retransmissions and increased RACH delays, but also has the potential to disrupt the dedicated traffic flow by increasing the interference levels (3G CDMA). In order to overcome this problem, in this paper we propose a Time Hierarchical Scheme (THS) that reduces the intensity of power outage data flooding and power outage reporting delay by 6/7th, and 17/18th when compared to their respective values without THS. Also, we propose an Optimum Transmission Rate Adaptive (OTRA) MAC to optimize the latency in power outage data collection. The analysis and simulation results presented in this paper show that both the OTRA and THS features of the proposed MAC results in a Power Outage Data Collection Latency (PODCL) that is 1/10th of the 4G LTE PODCL.

Garlapati, Shravan K↗

DOE OSTI2021

Gaussian Process Regression for Aggregate Baseline Load Forecasting

Demand response (DR) is one of the most effective ways to maintain the reliability and improve the flexibility of power systems. Accurate forecasts of baseline loads are essential for DR programs. In the era of big data, machine learning-based approaches present a unique opportunity for baseline load forecasting. Thus, this paper presents a machine learning-based approach using a relatively less explored algorithm, Gaussian process regression (GPR), to forecast aggregate baseline loads. As such, a dataset was generated using a set of EnergyPlus simulations. Using the generated dataset, a GPR-based forecasting model was developed. In addition, support vector regression (SVR)-, artificial neural network (ANN)-, and averaging-based models were developed as baseline models for comparison. These models were compared in terms of accuracy, simplicity, and integrity. The prediction performance of the models showed that the GPR-based model is more accurate and reliable than the others. Such high performance shows the potential of the GPR in baseline load forecasting. GPR, therefore, can be used for DR applications.

Amasyali, Kadir↗

DOE OSTI2022

What you get is not always what you see—pitfalls in solar array assessment using overhead imagery

Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Here our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.

14 SOLAR ENERGY↗