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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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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Burning questions for a warming and changing world: 15 unknowns in plant abiotic stress

Abstract We present unresolved questions in plant abiotic stress biology as posed by 15 research groups with expertise spanning eco-physiology to cell and molecular biology. Common themes of these questions include the need to better understand how plants detect water availability, temperature, salinity, and rising carbon dioxide (CO2) levels; how environmental signals interface with endogenous signaling and development (e.g. circadian clock and flowering time); and how this integrated signaling controls downstream responses (e.g. stomatal regulation, proline metabolism, and growth versus defense balance). The plasma membrane comes up frequently as a site of key signaling and transport events (e.g. mechanosensing and lipid-derived signaling, aquaporins). Adaptation to water extremes and rising CO2 affects hydraulic architecture and transpiration, as well as root and shoot growth and morphology, in ways not fully understood. Environmental adaptation involves tradeoffs that limit ecological distribution and crop resilience in the face of changing and increasingly unpredictable environments. Exploration of plant diversity within and among species can help us know which of these tradeoffs represent fundamental limits and which ones can be circumvented by bringing new trait combinations together. Better defining what constitutes beneficial stress resistance in different contexts and making connections between genes and phenotypes, and between laboratory and field observations, are overarching challenges.

Biochemistry & Molecular Biology↗
DOE OSTI2019

Genome-wide transcriptomic analysis of a desert willow, Salix psammophila, reveals the function of hub genes SpMDP1 and SpWRKY33 in drought tolerance

Background: Drought is a major environmental constraint to plant growth, development and productivity. Compared with most willows that are generally susceptible to drought, the desert willow Salix psammophila has extraordinary adaptation to drought stress. However, its molecular basis of drought tolerance is still largely unknown.Results: During polyethylene glycol 6000 (PEG 6000)-simulated drought stress, we found that the osmotic adjustment substances were accumulated and the antioxidant enzyme activities were enhanced in S. psammophila roots. A total of 8172 differentially expressed genes were identified in roots of S. psammophila through RNA-Sequencing. Based on K-means clustering, their expression patterns were classified into nine clusters, which were enriched in several stress-related processes including transcriptional regulation, response to various stresses, cell death, etc. Moreover, 672 transcription factors from 45 gene families were differentially expressed under drought stress. Furthermore, a weighted gene co-expression network was constructed, and eight genes were identified as hub genes. We demonstrated the function of two hub genes, magnesium-dependent phosphatase 1 (SpMDP1) and SpWRKY33, through overexpression in Arabidopsis thaliana. Overexpression of the two hub genes enhanced the drought tolerance in transgenic plants, suggesting that the identification of candidate drought tolerance genes in this study was highly efficient and credible.Conclusions: Our study analyzed the physiological and molecular responses to drought stress in S. psammophila, and these results contribute to dissect the mechanism of drought tolerance of S. psammophila and facilitate identification of critical genes involved in drought tolerance for willow breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Primary and Low-Strain Creep Models for 9Cr Tempered Martensitic Steels Including the Effects of Irradiation Softening and High-Helium Re-Hardening

Primary and low-strain creep represents a very important integrity challenge to large, complex structures, like fusion reactors. Here, we develop a predictive empirical primary creep model for 9Cr tempered martensitic steels (TMS), relating the applied stress (σ) to strain (ε), time (t) and temperature (T). The most accurate model is based on the applied σ normalized by the steel’s T-dependent ultimate tensile stress (σo), σ/σo(T). The model, fit to 17 heats of 9Cr TMS, yielded a σ root mean square error (RMSE) of ≈±11 MPa. Notably, the model also provides robust predictions for all the other TMS, when calibrated only by the fusion candidate Eurofer97 database. The model was extended to explore two possible effects of neutron irradiation, which produces both displacements per atom (dpa) and helium (He in atomic parts per million, appm) damage. These effects, which have not been previously considered, include: (a) softening, as a function of dpa, at T > ≈400–450 °C, in low-He fission environments (<1 He/dpa); and (b) subsequent re-hardening in high-He (≥10 He/dpa) fusion first-wall environments. The irradiation effect models predict (a) accelerated primary creep due to irradiation softening; and (b) fully arrested creep due to high-He re-hardening.

Alam, Md Ershadul (ORCID:0000000345968026)↗
DOE OSTI2017

Best Practices of Uncertainty Estimation for the National Solar Radiation Database (NSRDB 1998-2015): Preprint

It is essential to apply a traceable and standard approach to determine the uncertainty of solar resource data. Solar resource data are used for all phases of solar energy conversion projects, from the conceptual phase to routine solar power plant operation, and to determine performance guarantees of solar energy conversion systems. These guarantees are based on the available solar resource derived from a measurement station or modeled data set such as the National Solar Radiation Database (NSRDB). Therefore, quantifying the uncertainty of these data sets provides confidence to financiers, developers, and site operators of solar energy conversion systems and ultimately reduces deployment costs. In this study, we implemented the Guide to the Expression of Uncertainty in Measurement (GUM) 1 to quantify the overall uncertainty of the NSRDB data. First, we start with quantifying measurement uncertainty, then we determine each uncertainty statistic of the NSRDB data, and we combine them using the root-sum-of-the-squares method. The statistics were derived by comparing the NSRDB data to the seven measurement stations from the National Oceanic and Atmospheric Administration's Surface Radiation Budget Network, National Renewable Energy Laboratory's Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement program's Southern Great Plains Central Facility, in Billings, Oklahoma. The evaluation was conducted for hourly values, daily totals, monthly mean daily totals, and annual mean monthly mean daily totals. Varying time averages assist to capture the temporal uncertainty of the specific modeled solar resource data required for each phase of a solar energy project; some phases require higher temporal resolution than others. Overall, by including the uncertainty of measurements of solar radiation made at ground stations, bias, and root mean square error, the NSRDB data demonstrated expanded uncertainty of 17 percent - 29 percent on hourly and an approximate 5 percent - 8 percent annual bases.

14 SOLAR ENERGY↗
DOE OSTI2023

Mechanistic modeling of copper corrosions in data center environments

Air-side economizers are increasingly used to take advantage of “free-cooling” in data centers with the intent of reducing the carbon footprint of buildings. However, they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology equipment. Here, to evaluate the reliability of information technology equipment under various thermal and air-pollution conditions, a mechanistic model based on multi-ion transport and chemical reactions was developed. The model was used to predict Cu corrosion caused by Cl2-containing pollutant mixtures. It also accounted for the effects of temperature (25 °C and 28 °C), relative humidity (50%, 75%, and 95%), and synergism. It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator, which agreed well with the experimental results. The average root mean square error of the prediction was 13.7 Å. The model can be used to evaluate the thermal guideline for data centers design and operation when Cl2 is present based on pre-established acceptable risk of corrosion in data centers’ environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗
DOE OSTI2026

Switchgrass-associated soil microbes have subtle but distinct effects on germination vs. growth under drought

Soil microbial communities can play a large role in plant fitness, including plants’ response to drought. How microbes mediate drought response may also differ across the plants’ life stage, but studies rarely study causal effects of microbes on plant fitness at multiple stages. We conducted a greenhouse experiment on switchgrass (Panicum virgatum L.) to investigate how microbial presence (sterile bulk vs. live bulk soils) and microbial source (communities from bulk vs. rhizosphere soil) affect germination and seedling growth (life-stage) during drought. We also investigated how drought and life-stage alter the assembly of the inoculated communities. Both drought conditions and the absence of microbes reduced germination (49% and 50% fewer seedlings, respectively, p < 0.05), but there were few effects of microbial presence on older seedlings. In addition, microbial source had no effect on the evaluated plant traits, but interestingly, the initially similar bulk and rhizosphere communities became compositionally distinct after 34 days with germinating seeds, but not with seedlings. Therefore, it may be that the plants did respond to microbial source through changes in seed or root exudates, and calls for further study. Finally, while sometimes microbes did benefit plants under drought, we did not find evidence that they are especially beneficial in this condition. We suggest that microbes’ effects differ across plant life stage, and may be both positive and negative. Further research should advance understanding of plant stress tolerance within a framework of plant-microbiome co-development.

drought↗
DOE OSTI2020

Molecular Dynamics Simulation of Atomic Interactions in the Vancomycin Binding Site

Vancomycin is a glycopeptide antibiotic produced by Amycolaptopsis orientalis used to treat serious infections by Grampositive pathogens including methicillin-resistant Staphylococcus aureus. Vancomycin inhibits cell wall biosynthesis by targeting lipid II, which is the membrane-bound peptidoglycan precursor. The heptapeptide aglycon structure of vancomycin binds to the D-Ala-D-Ala of the pentapeptide stem structure in lipid II. The third residue of vancomycin aglycon is asparagine, which is not directly involved in the dipeptide binding. Nonetheless, asparagine plays a crucial role in substrate recognition, as the vancomycin analogue with asparagine substituted by aspartic acid (V D ) shows a reduction in antibacterial activities. To characterize the function of asparagine, binding of vancomycin and its aspartic-acid-substituted analogue V D to L-Lys-D-Ala-D-Ala and L-Lys-D-Ala-D-Lac was investigated using molecular dynamic simulations. Binding interactions were analyzed using root-mean-square deviation (RMSD), two-dimensional (2D) contour plots, hydrogen bond analysis, and free energy calculations of the complexes. The analysis shows that the aspartate substitution introduced a negative charge to the binding cleft of V D , which altered the aglycon conformation that minimized the repulsive lone pair interaction in the binding of a depsipeptide. Our findings provide new insight for the development of novel glycopeptide antibiotics against the emerging vancomycin-resistant pathogens by chemical modification at the third residue in vancomycin to improve its binding affinity to the D-Ala-D-Lac-terminated peptidoglycan in lipid II found in vancomycin-resistant enterococci and vancomycinresistant S. aureus.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Comparison of a 2D nonlocal transport model to ECRH experiments in LHD

In this work, a recently developed model of nonlocal transport in two dimensions (2D), the “iterative method”, is applied to the published results of ECRH experiments performed in the Large Helical Device. It is found from the temperature profiles obtained from the 2D model that the sharply peaked electron temperature profiles observed in such experiments, and previously associated with local transport and “internal transport barriers” or Core Electron-Root Confinement, are also consistent with nonlocal transport. It is shown that the iterative method is suited to handling both scenarios

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Global Sensitivity Analysis of Bulk Properties of an Atomic Nucleus

We perform a global sensitivity analysis of the binding energy and the charge radius of the nucleus 16 O to identify the most influential low-energy constants in the next-to-next-to-leading order chiral Hamiltonian with two- and three-nucleon forces. For this purpose, we develop a subspace-projected coupled-cluster method using eigenvector continuation [Frame D. et al., Phys. Rev. Lett. 121, 032501 (2018)]. With this method, we compute the binding energy and charge radius of 16 O at more than 106 different values of the 16 low-energy constants in one hour on a standard laptop computer. For relatively small subspace projections, the root-mean-square error is about 1% compared to full-space coupled-cluster results. We find that 58(1)% of the variance in energy can be apportioned to a single contact term in the 3 S 1 wave, whereas the radius depends sensitively on several low-energy constants and their higher-order correlations. The results identify the most important parameters for describing nuclear saturation and help prioritize efforts for uncertainty reduction of theoretical predictions. The achieved acceleration opens up an array of computational statistics analyses of the underlying description of the strong nuclear interaction in nuclei across the Segrè chart.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

The Genetic Basis of Mutation Rate Variation in Yeast

Mutations are the root source of genetic variation and underlie the process of evolution. Although the rates at which mutations occur vary considerably between species, little is known about differences within species, or the genetic and molecular basis of these differences. Here, we leveraged the power of the yeast Saccharomyces cerevisiae as a model system to uncover natural genetic variants that underlie variation in mutation rate. We developed a high-throughput fluctuation assay and used it to quantify mutation rates in seven natural yeast isolates and in 1040 segregant progeny from a cross between BY, a laboratory strain, and RM, a wine strain. We observed that mutation rate varies among yeast strains and is heritable (H 2 = 0.49). We performed linkage mapping in the segregants and identified four quantitative trait loci underlying mutation rate variation in the cross. We fine-mapped two quantitative trait loci to the underlying causal genes, RAD5 and MKT1, that contribute to mutation rate variation. These genes also underlie sensitivity to the DNA-damaging agents 4NQO and MMS, suggesting a connection between spontaneous mutation rate and mutagen sensitivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Surrogate Models for TREAT Transient Calculations

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is currently preparing for resumption of operation after more than 20 years in a long-term standby state. In parallel with preparation for operations and a new transient testing program, the DOE-NE Advanced Modeling and Simulation program (NEAMS) has been supporting the development of three-dimensional transient multi-physics modeling capabilities within the reactor physics application MAMMOTH [1]. Recently, the MAMMOTH reactor physics team has begun development of uncertainty and sensitivity analysis for the TREAT core. This research will be used to support the analysis of reactor operation and experiment planning. Traditional approaches, i.e., Monte Carlo simulations, for such tasks require a large number of model evaluations under both steady state and transient conditions. However, exhaustive sets of sampling runs are simply not practical here since evaluations of each model can take a substantial amount of time. A solution to this problem is to build a surrogate model employing a smaller number of model evaluations. The surrogate model is designed to provide an accurate and fast approximation to the detailed TREAT model. In this manner, the surrogate model can be used in place of the TREAT transient model for uncertainty and sensitivity analysis at a fraction of the computational cost. The high dimensional model representation (HDMR) [2] method is employed here for approximating the responses of interest in terms of functions of lower dimensions. This method has been implemented in RAVEN [3] and all TREAT transient calculations are performed using the multiphysics code MAMMOTH. In addition, a code interface has also been developed in order to couple RAVEN with MAMMOTH. The surrogate model is then tested and compared for the MAMMOTH model of the TREAT startup testing program Transient Test #15 core [4], and its accuracy is measured by its ability to estimate the mean, coefficient of variance, skewness, excess kurtosis and probability density function of the outputs of Transient Test #15 model, as well as root mean square error metric.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Rapid Detection of Anomalies in Battery Energy Storage System Data

Data analytics is pivotal in assessing the technical characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy To address the challenge, this paper presents a novel methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. We’ve collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities. These datasets serve dual roles: enabling comprehensive data exploration and analysis for understanding underlying challenges and method development, while also acting as a vital validation resource, demonstrating practical effectiveness. The proposed method detects anomalies and aids in their resolution, improving system performance characterization precision. It also reveals recurring data anomaly sources, offering insights for data collection and handling enhancement. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.

Crawford, Aladsair J.↗
DOE OSTI2022

It’s Not What You Take Up, It’s What You Keep: How Discoveries from Diverse Disciplines Directed the Development of the FDG PET/CT Scan

Although imaging glucose metabolism with positron emission tomography combined with X-ray CT (FDG-PET/CT) has become a standard diagnostic modality for the discovery and surveillance of malignant tumors and inflammatory processes, its origins extend back to more than a century of notable discoveries in the fields of inorganic and organic chemistry, nuclear physics, mathematics, biochemistry, solute transport physiology, metabolism, and imaging, accomplished by pioneering and driven investigators, of whom at least ten were recipients of the Nobel Prize. These tangled and diverse roots eventually coalesced into the FDG-PET/CT method, that through its many favorable characteristics inherent in the isotope used ( 18 F), the accurate imaging derived from coincidence detection of positron annihilation radiation combined with computed tomography, and the metabolic trapping of 2-deoxy-2-[ 18 F] fluoro-d-glucose (FDG) in tissues, provides safety, sensitivity, and specificity for tumor and inflammation detection. Here, the authors hope that this article will increase the appreciation among its readers of the insight, creativity, persistence, and drive of the many investigators who made this technique possible. This article is followed by a review of the many applications of FDG-PET/CT to the gastrointestinal tract and hepatobiliary system (Mandelkern in Dig Dis Sci 2022)

2-DG↗
DOE OSTI2019

Northern forest winters have lost cold, snowy conditions that are important for ecosystems and human communities

Winter is an understudied but key period for the socioecological systems of northeastern North American forests. A growing awareness of the importance of the winter season to forest ecosystems and surrounding communities has inspired several decades of research, both across the northern forest and at other mid- and high-latitude ecosystems around the globe. Despite these efforts, we lack a synthetic understanding of how winter climate change may impact hydrological and biogeochemical processes and the social and economic activities they support. Here, we take advantage of 100 years of meteorological observations across the northern forest region of the northeastern United States and eastern Canada to develop a suite of indicators that enable a cross-cutting understanding of (1) how winter temperatures and snow cover have been changing and (2) how these shifts may impact both ecosystems and surrounding human communities. We show that cold and snow covered conditions have generally decreased over the past 100 years. These trends suggest positive outcomes for tree health as related to reduced fine root mortality and nutrient loss associated with winter frost but negative outcomes as related to the northward advancement and proliferation of forest insect pests. In addition to effects on vegetation, reductions in cold temperatures and snow cover are likely to have negative impacts on the ecology of the northern forest through impacts on water, soils, and wildlife. The overall loss of coldness and snow cover may also have negative consequences for logging and forest products, vector-borne diseases, and human health, recreation, and tourism, and cultural practices, which together represent important social and economic dimensions for the northern forest region. These findings advance our understanding of how our changing winters may transform the socioecological system of a region that has been defined by the contrasting rhythm of the seasons. Our research also identifies a trajectory of change that informs our expectations for the future as the climate continues to warm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neural networks for estimation of divertor conditions in DIII-D using C III imaging

Deep learning approaches have been applied to images of C III emission in the lower divertor of DIII-D to develop models for estimating the level of detachment and magnetic configuration (X-point location and strike point radial location). The poloidal distance from the target to the C III emission front is used to represent the level of detachment. The models perform well on a test dataset not used in training, achieving $F_1$ scores as high as 0.99 for detachment state classification and root mean squared error (RMSE) as low as 2cm for front location regression. Predictions for shots with intermittent reattachment are studied, with class activation mapping used to aid in interpretation of the model predictions. Based on the success of these models, a third model was trained to predict the X-point location and strike point radial position from C III images. Though the dataset covers only a small range of possible magnetic configurations, the model shows promising results, achieving RMSE around 1cm for the test data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Non-Destructive, Three-Dimensional Imaging of Processes in the Rhizosphere Utilizing High Energy Photons

Soil structure, which can be described as the aggregation and distribution of pore spaces, regulates carbon, nutrient, and water cycling across the Earth system. Yet the inability to make quantitative, dynamic, in situ measurements of soil structure and rhizosphere carbon flow has prevented meaningful incorporation of soil structural processes into Earth System Models (ESMs). The key limitations are: (i) Lack of scale integration between micron-scale soil structure and ecosystem-scale models, (ii) Poor functional linkage between soil structural properties and biogeochemical processes, and (iii) Absence of dynamic 3D measurements of rhizosphere structural changes and carbon transformations. To address these challenges, we developed a new integrated positron emission tomography (PET) - microcomputed tomography (CT) imaging platform that enables the first 4D (spatial 3D and time), non-invasive, quantitative imaging of carbon allocation and rhizosphere structural dynamics in living plants and intact soils. The system combines: • Rhizo-PET (R-PET): a high-resolution positron emission tomography scanner optimized for 11 CO 2 tracing in plant roots. • a-Se micro-CT: a high-contrast, high-spatial resolution CT system based on amorphous selenium (a-Se) direct conversion technology, enabling micron-scale visualization of soil structure and root–soil interfaces. Together, these advances allow us to quantify how carbon exudates move, transform, and stabilize within the rhizosphere, directly informing missing processes in BER-relevant carbon cycle models.

42 ENGINEERING↗
DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗