Results for “data visualizations” — Page 39

DOE OSTI2024

USAID Haiti Training Session #3: Capacity Expansion [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89365 for the French translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Session de formation de l'USAID en Haiti n1: Orientation de l'outil Engage [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications at from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88835 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Session de formation de l'USAID en Haiti n2: Introduction a l'outil Engage [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88836 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Seance de formation de l'USAID en Haiti n3: Expansion des capacites [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88837 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

USAID Haiti Training Session #4: Resilience and Power Sector Modeling Part I [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications at from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89408 for the French translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

USAID Haiti Training Session #4: Resilience and Power Sector Modeling Part II [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89412 for the French translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Seance de formation n4: Modelisation de la resilience et du secteur de l'electricite - Partie I [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-88838 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Seance de formation n4: Modelisation de la resilience et du secteur de l'electricite - Partie II [Slides]

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training series was developed under USAID to empower Haitian stakeholders to explore modeling scenarios in Engage to develop a Haiti Energy Master Plan. This training series includes a general overview of how Engage works along with tailored exercises that target Haiti's specific energy needs, such as modeling the capacity expansion of the bulk power system, fuel scarcity challenges, the development of microgrids, and the economic viability of transmission strategies. See NREL/PR-7A40-89389 for the English translation of this document.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Introduction to Engage: NASA Training Session

Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training session was presented to the National Aeronautics and Space Administration (NASA) to help them understand how capacity expansion modeling can help them develop single site/distribution analysis of energy to regional airports.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Waveform Simulation Framework: User Manual with Tutorials

This manuscript describes the Waveform Simulation Framework (WSF), a Python-based framework that provides a unified, programmable interface for generating synthetic seismograms for applications such as seismic array design, method development, and special event analysis. WSF standardizes how users define sources, receivers, and velocity models while abstracting simulator-specific configuration details, enabling workflows that are largely independent of the underlying numerical engine. The document provides installation guidance and tutorial-driven examples for three WSF simulator wrappers—WSF PyFK, WSF SW4, and WSF SPECFEM2D—illustrating end-to-end workflows from forward waveform simulation to common post-processing tasks (e.g., visualization and backprojection) using consistent data products (e.g., ObsPy Stream objects and SAC files).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Real-time and Autonomous Water Quality Monitoring System Based on Remotely Operated Vehicle

Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.

Salalila, Aljon L.↗

DOE OSTI2020

Scientific Visualization: New Techniques in Production Software

The field of visualization encompasses a wide range of techniques, from infographics to isosurfaces. An important subfield called "scientific visualization" is specifically dedicated to data sets with spatial components, i.e., (X, Y, Z) locations. Furthermore, this subfield's name is inspired by the fact that the data in question often come from the sciences, i.e., physics simulations or sensor networks.

Moreland, Kenneth D.↗

DOE OSTI2023

ExaWind at NREL: Upping the Ante

The objective of the ExaWind component of the Exascale Computing Project is to deliver many-turbine blade-resolved simulations in complex terrain. These simulations bring new challenges to both compute and analysis of the resulting data. In this paper/video, we visually explore the impact of ExaWind on wind simulations through two studies of a small wind farm under two atmospheric conditions. We then turn to analysis and review tools that visualization researchers at NREL use to answer the challenges that ExaWind brings.

collaborative visualization↗

DOE OSTI2020

Leveraging Environmental Monitoring Results from Department of Energy Sites for Research on the Food-Energy-Water Nexus

The Annual Site Environmental Report (ASER) from a U.S. Department of Energy (DOE) Site summarizes environmental data; environmental management performance; compliance with applicable DOE, federal, state, and local regulations; and restoration and surveillance monitoring programs. The Savannah River Site (SRS) produces ASERs that include results of monitoring mercury levels in fish samples obtained in the vicinity of SRS. Mercury is a toxic trace metal and the methylated organic form is a neurotoxin that bioaccumulates in the food web, endangering humans and other organisms. In United States, power plants are the largest source of mercury emissions. Research Objective: To determine the patterns of mercury levels in fish samples reported in the SRS environmental monitoring reports. We developed box plot visualizations to make sense of the data as well as identify fish species, dates and location with outlier mercury content. The integration and visual representation of multi-year datasets can support decision making processes on environmental compliance and environmental sustainability conducted at U.S. DOE Sites. We are constructing datasets from textual, image and multivariable data sources in the multi-year ASERs of the U.S. DOE Savannah River Site (SRS https://www.srs.gov/general/pubs/ERsum/index.html). The Fish Surveillance for mercury presents a case study for the intersection of food, energy and water systems. The dataset of 864 records (2008 and 2009) of mercury levels includes water location of fish, fish species, collection date and mercury content of fish sample. The patterns of mercury levels in the fish species as well as locations provide knowledge for further investigations. A research area for future studies is the composition of microbial communities in fish guts of varying levels of mercury.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Driving Next-Generation Workflows from the Data Plane

We observe the emergence of a new generation of scientific workflows that process data produced at a sustained rate by scientific instruments and large scale numerical simulations. This data is consumed by multiple analysis, visualization, or Machine Learning components not only to enable inference and justify the scientific program, but also to monitor and steer the evolution of these experiments. In such workflows, moving intermediate data efficiently is key to performance, more than efficiently scheduling computational tasks. However, most traditional workflow management systems focus on optimizing task scheduling and then deal with data management, assuming a “move little, compute for long” model, which makes them unfit to the efficient management of this new generation of workflows. Therefore, we advocate for a new way to manage scientific workflows. We propose to consider an efficiently and independently managed data plane that can store and stream data. Workflows compute components, in the application plane can then interact with the data plane, abstracted from complexities of data management. Then, the role of a workflow management system would become that of a control plane that allows users to connect services together to execute the workflow and manages connections between the application and data planes. In this position paper, we characterize several next-generation workflow motifs and describe how their interaction with the data plane is a challenge to traditional workflow management systems. Then, we express a set of requirements that a workflow management system should meet to efficiently manage next-generation workflows at different scales. Based on these requirements, we expose our vision of driving next-generation workflows from the data plane and list remaining open challenges.

Suter, Fred↗

DOE OSTI2022

An Interactive Visualization Tool for Large-Scale Building Stock Modeling: Preprint

Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.

big data↗

DOE OSTI2024

“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches

Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks are very brittle and are difficult to deploy on resource-limited platforms. Model pruning, i.e., reducing the size of the network, is a widely adopted strategy that can lead to a more robust and compact model. Many heuristics exist for model pruning, but our understanding of the pruning process remains limited due to the black-box nature of a neural network model. Empirical studies show that some heuristics improve performance whereas others can make models more brittle. Here, this work aims to shed light on how different pruning methods alter the network’s internal feature representation and the corresponding impact on model performance. To facilitate a comprehensive comparison and characterization of the high-dimensional model feature space, we introduce a visual geometric analysis of feature representations. We evaluated a set of critical geometric concepts decomposed from the commonly adopted classification loss and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation. The proposed tool provides an environment for an in-depth comparison of pruning methods and a comprehensive understanding of how the model responds to common data corruption. By leveraging the proposed visualization, machine learning researchers can reveal the similarities between pruning methods and redundancy in robustness evaluation benchmarks, obtain geometric insights about the differences between pruned models that achieve superior robustness performance, and identify samples that are robust or fragile to model pruning and common data corruption.

Li, Zhimin [Univ. of Utah, Salt Lake City, UT (Uni↗