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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (French Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the French translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Arabic Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Arabic translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2024

SEED Platform for Building Performance Standards Implementation Guide (Mandarin Translation)

This guide provides an overview of the Standard Energy Efficiency Data (SEED) Platform. The SEED Platform developed by the U.S. Department of Energy (DOE) to provide a low-cost, user-friendly tool for jurisdictions to launch and manage energy benchmarking and Building Performance Standard (BPS) programs. It has been translated into Spanish. This is the Mandarin translation of NREL/FS-5500-90691.

benchmarking↗
DOE OSTI2023

HERMES: A cyber-energy SAAS platform to provide real-time 2d and 3d interactive visualizations [SWR-23-15]

HERMES is a novel visualization SAAS platform for achieving real-time visualization of large-scale environments involving cyber-energy devices. The platform receives data from emulated environments which include physical hardware and network devices that represent a complex IT / OT system. The platform is capable of streaming, filtering, storing, and visualizing all data within the environment and scaling vertically and horizontally. This capability enables high fidelity visual analysis of events to be performed in real time as well as the collection and storage of historical data for forensic analysis.

Van Natta, Joshua↗
DOE OSTI2022

First Plant Cell Atlas symposium report

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Pecan Street Smart Grid Extension Service at the University of Texas

Through funding from the Department of Energy’s Electricity Delivery and Reliability Office, Pecan Street Inc., in partnership with Austin Energy and Oncor, developed and tested third- party data access platforms and services for Green Button offerings and for other home energy use data providers. As more utilities seek to offer Green Button-compliant data to their customers, the question continually arises of how this data can be used to help customers better manage their energy use.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Building near-real-time processing pipelines with the spark-MPI platform

Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three V's (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities. Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report)

The Department of Energy (DOE) ensures America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The DOE’s Office of Science (SC) delivers groundbreaking scientific discoveries and major scientific tools that transform our understanding of nature and advance the energy, economic, and national security of the United States. The SC’s programs advance DOE mission science across a wide range of disciplines and have developed the research infrastructure needed to remain at the forefront of scientific discovery. The DOE SC’s world-class research infrastructure — exemplified by the 28 SC scientific user facilities — provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. A hallmark of many facilities is the large population of students, postdoctoral researchers, and early-career scientists who contribute as full-fledged users. These facility staff and users collaborate over years to devise new approaches to utilizing the user facility’s core capabilities. The history of the SC user facilities has many examples of wildly inventive researchers challenging operational orthodoxy to pioneer new vistas of discovery; for example, the use of the synchrotron X-ray light sources for study of proteins and other large biological molecules. This continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high performance data, networking, and computing solutions. The DOE SC’s three HPC user facilities and the Energy Sciences Network (ESnet) high-performance research network serve all of the SC’s programs as well as the global research community. Argonne Leadership Computing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), and Oak Ridge Leadership Computing Facility (OLCF) conceive, build, and provide access to a range of supercomputing, advanced computing, and large-scale data-infrastructure platforms, while ESnet interconnects DOE SC research infrastructure and enables seamless exchange of scientific data. All four facilities operate testbeds to expand the frontiers of computing and networking research. Together, the ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements reviews (RRs) methodology. In recent years, the research communities around the SC user facilities have begun experimenting with and demanding solutions integrated with HPC and data infrastructure. This rise of integrated-science approaches is documented in many community and high-level government reports. At the dawn of the era of exascale science and the acceleration of artificial intelligence (AI) innovation, there is a broad need for integrated computational, data, and networking solutions. In response to these drivers, DOE has developed a vision for an Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation.

42 ENGINEERING↗
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

SNL ATDM Data and Visualization: IOSS and FAODEL

The SNL ATDM Data and Visualization project is developing data management software to improve how applications store and exchange large datasets efficiently on Exascale platforms. The data portion of this project is composed of two related efforts: (1) production work focused on improving Sandia's IOSS library for mesh datasets and (2) research work focused on developing new communication software named FAODEL that enables applications in a workflow to exchange data more efficiently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

PhotonIDs: ML-Powered Photon Identification System for Dark Count Elimination

Reliable single photon detection is the foundation for practical quantum communication and networking. However, today's superconducting nanowire single photon detector(SNSPD) inherently fails to distinguish between genuine photon events and dark counts, leading to degraded fidelity in long-distance quantum communication. In this work, we introduce PhotonIDs, a machine learning-powered photon identification system that is the first end-to-end solution for real-time discrimination between photons and dark count based on full SNSPD readout signal waveform analysis. PhotonIDs ~demonstrates: 1) an FPGA-based high-speed data acquisition platform that selectively captures the full waveform of signal only while filtering out the background data in real time; 2) an efficient signal preprocessing pipeline, and a novel pseudo-position metric that is derived from the physical temporal-spatial features of each detected event; 3) a hybrid machine learning model with near 98% accuracy achieved on photon/dark count classification. Additionally, proposed PhotonIDs ~ is evaluated on the dark count elimination performance with two real-world case studies: (1) 20 km quantum link, and (2) Erbium ion-based photon emission system. Our result demonstrates that PhotonIDs ~could improve more than 31.2 times of signal-noise-ratio~(SNR) on dark count elimination. PhotonIDs ~ marks a step forward in noise-resilient quantum communication infrastructure.

Linne, Karl C. [Chicago U.] (ORCID:000900091870358↗
DOE OSTI2019

Synchrophasor Visual Integration and Event Evaluation for Utilities (SynchroVIEEU) with High Penetration of Renewables

The Hawaiian Electric Companies (Companies), operating independent grid systems on the islands of Oahu, Hawaii, Maui, Molokai and Lanai, have been contending with some of the highest levels of renewable generation from both utility-scale and distributed generation resources. Renewable penetration levels exceeding 50% of generation are some of the highest in the nation and are changing the system characteristics and requiring operational changes to grid management. The Companies have aggressively leveraged federal, multi-state and industry grant funding to demonstrate new technologies for monitoring, visualization, forecasting and analysis to inform transformation needs and to improve operational awareness, especially at the distribution level. As part of the American Recovery and Reinvestment Act (ARRA) supported efforts to facilitate integration of renewables, synchrophasor devices were deployed on the islands of Hawaii and Maui in 2010. The devices have potential to provide Hawaii Electric Light Company (HELCO) on Hawaii Island and Maui Electric Company (MECO) with additional information to support post-event analysis and to help quantify observed area-wide changes in voltage and frequency on the systems. Consistent with the U.S. Department of Energy’s (DOE) Office of Electricity Delivery and Energy Reliability (OE) FOA objectives, the goal of this project was to accelerate the integration of synchrophasor information into production grade data visualization and analysis platforms/models to inform timely, efficient and reliable operations of a modern grid in light of high penetrations of renewable resources. The project comprised a two-year development effort to investigate the integration and visualization of synchrophasor field data into production-ready transmission and distribution proactive modeling efforts and a common data analysis platform application currently being used by the Companies. Conceptually, the project was about getting the right data to the right people at the right time in an easily accessible and timely environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Applications of Autonomous Data Collection and Active Learning

Advances in sensors and robotics have dramatically improved the diversity of experimental approaches available to the materials community. Autonomous data collection platforms, either custom-made or commercially available, provide researchers with novel tools with which to probe materials behavior and perform advanced materials characterization. The application of novel control algorithms and active learning approaches can create much more robust experimental data, or can be used to improve the performance of existing characterization tools. Five papers within this special topic focus on experimental and computational methodologies for use in automatic data collection routines for materials characterization. From novel platforms for materials discovery to new statistical frameworks for assessing the autonomous experimentation process, these five papers highlight the diverse range of applications of automation for advancing materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Using Micro-Synchrophasor Data for Advanced Distribution Grid Planning and Operations Analysis

This report reviews the potential for distribution-grid phase-angle data that will be available from new micro-synchrophasors (µPMUs) to be utilized in existing distribution-grid planning and operations analysis. This data could augment the current diagnostic capabilities of grid analysis software, used in both planning and operations for applications such as fault location, and provide data for more accurate modeling of the distribution system. µPMUs are new distribution-grid sensors that will advance measurement and diagnostic capabilities and provide improved visibility of the distribution grid, enabling analysis of the grid’s increasingly complex loads that include features such as large volumes of distributed generation. Large volumes of DG leads to concerns on continued reliable operation of the grid, due to changing power flow characteristics and active generation, with its own protection and control capabilities. Using µPMU data on change in voltage phase angle between two points in conjunction with new and existing distribution-grid planning and operational tools is expected to enable model validation, state estimation, fault location, and renewable resource/load characterization. Our findings include: data measurement is outstripping the processing capabilities of planning and operational tools; not every tool can visualize a voltage phase-angle measurement to the degree of accuracy measured by advanced sensors, and the degree of accuracy in measurement required for the distribution grid is not defined; solving methods cannot handle the high volumes of data generated by modern sensors, so new models and solving methods (such as graph trace analysis) are needed; standardization of sensor-data communications platforms in planning and applications tools would allow integration of different vendors’ sensors and advanced measurement devices. In addition, data from advanced sources such as µPMUs could be used to validate models to improve/ensure accuracy, providing information on normally estimated values such as underground conductor impedance, and characterization of complex loads. Although the input of high-fidelity data to existing tools will be challenging, µPMU data on phase angle (as well as other data from advanced sensors) will be useful for basic operational decisions that are based on a trend of changing data.

25 ENERGY STORAGE↗
DOE OSTI2015

A High-Speed, Real-Time Visualization and State Estimation Platform for Monitoring and Control of Electric Distribution Systems: Implementation and Field Results

Continued deployment of renewable and distributed energy resources is fundamentally changing the way that electric distribution systems are controlled and operated; more sophisticated active system control and greater situational awareness are needed. Real-time measurements and distribution system state estimation (DSSE) techniques enable more sophisticated system control and, when combined with visualization applications, greater situational awareness. This paper presents a novel demonstration of a high-speed, real-time DSSE platform and related control and visualization functionalities, implemented using existing open-source software and distribution system monitoring hardware. Live scrolling strip charts of meter data and intuitive annotated map visualizations of the entire state (obtained via DSSE) of a real-world distribution circuit are shown. The DSSE implementation is validated to demonstrate provision of accurate voltage data. This platform allows for enhanced control and situational awareness using only a minimum quantity of distribution system measurement units and modest data and software infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Digital Twin for Hydropower System Object Modeling: Alder Dam (FY2023)

Hydropower is the world's largest source of renewable electricity, and hydropower plants are distributed all over the world. Typical major components of a hydropower plant are the governor, excitation, generator, thrust bearing, hydraulic turbine, transformer, the main lead, metering and control, tailwater depression, and dissolved oxygen. For each component, various measures are taken. The measurements are acquired by various heterogeneous systems, including standalone sensors, programmable logic controllers (PLC), Supervisory control and data acquisition (SCADA), Internet of Things (IoT), and data acquisition and integration platforms such as OSI/PI. The measured data are often archived within the plant by a data management platform, and many institutions have cloud-based archive systems, such as Hydropower Research Institution (HRI), U.S. Army Corps of Engineers (USACE), and Columbia River Data Access in Real Time (DART). Object Modeling is a general framework for designing information systems. It focuses on objects, the actions they perform, and the messages they send to one another to cause those actions to be taken. The major differences among object modeling, network modeling, data modeling, and process modeling are that in the first we focus on the actions in response to information, objects which form the system, the actions they perform, and how they pass information to one another, while in the second we concentrate on where, when and how much information is moved, while in the third we focus on what information is moved and where it is moved, while in the last we focus on how it is moved and when it is moved. Object modeling was developed basically as a method to develop object-oriented systems and to support object-oriented programming. It describes the static structure of the system. The object Modeling Technique is easy to draw and use. That is why we choose object modeling to connect physical hydropower plants to Digital Twin. It recognizes the objects and the relationship between them. It identifies the attributes and functions of each class. Dynamic Modeling: It explains how objects respond to events. Functional Modeling indicates the processes executed in an object and how data changes when it moves to objects. It has been used in many applications like telecommunication, transportation, etc.

13 HYDRO ENERGY↗