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At least 343 records · Page 19

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DOE OSTI2021

A Data-Driven Method for Estimating Behind-the-Meter Photovoltaic Generation in Hawaii

Due to the increasing penetration of distributed behind-the-meter photovoltaic (PV) systems and the installed utility revenue metering limited to monitoring only the net power import/export of the household, it is increasingly challenging for utilities to effectively plan and operate the grid. This paper proposes a methodology that estimates behind-the-meter PV generation using a selected subset of monitored PV systems. It is a data-driven approach, and the PV output is estimated utilizing a statistic regression model. A Minimum Redundancy Maximum Relevance (MRMR) algorithm is applied to preselect the optimal subset of the monitored PV systems. The performance of this approach is compared with a spatial interpolation method and a model-based approach. The proposed method is validated using high-resolution meter data recorded from 18 residential rooftop PV systems located on the island of Maui, Hawaii.

Data-driven modeling↗

DOE OSTI2026

Individual Motorist Data - Ohio EV Ownership Trends

The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Magnetic pair distribution function data using polarized neutrons and ad hoc corrections

Here, we report the first example of magnetic pair distribution function (mPDF) data obtained through the use of neutron polarization analysis. Using the antiferromagnetic semiconductor MnTe as a test case, we present high-quality mPDF data collected on the HYSPEC instrument at the Spallation Neutron Source using longitudinal polarization analysis to isolate the magnetic scattering cross section. Clean mPDF patterns are obtained for MnTe in both the magnetically ordered state and the correlated paramagnet state, where only short-range magnetic order is present. We also demonstrate significant improvement in the quality of high-resolution mPDF data through the application of ad hoc corrections that require only minimal human input, minimizing potential sources of error in the data processing procedure. We briefly discuss the current limitations and future outlook of mPDF analysis using polarized neutrons. Overall, this work provides a useful benchmark for mPDF analysis using polarized neutrons and provides an encouraging picture of the potential for routine collection of high-quality mPDF data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

GE Renewable Energy: Perdigao wind dataset from Large Eddy Simulation and ERA5

Perdigao wind research dataset including ERA5 data, high resolution and low resolution Large Eddy Simulations for the year 2020 at 1 hour frequency. Generated using Copernicus Climate Change Service information 2022[1] [1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., HorÃ¡nyi, A., MuÃ±oz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., ThÃ©paut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 05-04-2022), 10.24381/cds.adbb2d47

17 WIND ENERGY↗

DOE OSTI2026

Outdoor Deployment Data for a Four-Terminal GaAs//Si Tandem Solar Mini-Module

This dataset contains the complete outdoor measurement and analysis data for a mechanically stacked, four-terminal (4T) gallium arsenide (GaAs)//silicon (Si) tandem solar mini-module deployed from October 2019 to January 2021 at the Solar Radiation Research Laboratory (SRRL) in Golden, Colorado, USA. The data support a performance modeling and degradation analysis framework for tandem photovoltaic devices, as described in the accompanying publication. The dataset includes: (1) current–voltage (J–V) characteristics of each sub-cell measured approximately every five minutes, with extracted performance parameters; (2) spectral irradiance from an EKO MS-710 WISER spectroradiometer, along with derived spectral mismatch ratios (SMR) and average photon energy (APE); (3) one-minute resolution meteorological data from the co-located SRRL weather station and GPS-derived precipitable water vapor (PWV); (4) pre-deployment laboratory characterization (external quantum efficiency, J–V curves, standard test conditions parameters); (5) outdoor-extracted temperature and PWV correction coefficients; and (6) PVcircuit equivalent-circuit simulation outputs used for model validation. Degradation rates of −4.1 ± 0.2 %/year (GaAs) and −2.5 ± 0.9 %/year (Si) were determined using a filtering and normalization methodology adapted for fixed-tilt tandem modules. All data are provided in open, portable formats (Apache Parquet, CSV, JSON) to enable full reproducibility of the published analysis.

14 SOLAR ENERGY↗

DOE OSTI2020

Enabling Power at Sea: Opportunities for Expanded Ocean Observations through Marine Renewable Energy Integration

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and in-person interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Powering the Blue Economy: Progress Exploring Marine Renewable Energy Integration With Ocean Observations

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

marine renewable energy, powering the blue economy↗

DOE OSTI2023

A Dual-Gated Structures for Lossless Ion Manipulations-Ion Mobility Orbitrap Mass Spectrometry Platform for Combined Ultra-High-Resolution Molecular Analysis

High-resolution ion mobility spectrometry-mass spectrometry (HR-IMS-MS) instruments have enormously advanced the ability to characterize complex biological mixtures. Unfortunately, HR-IMS and HR-MS measurements are typically performed independently due to mismatches in analysis time scales. Here we overcome this limitation by using a dual-gated ion injection approach to couple an 11-meter path length structures for lossless ion manipulations (SLIM) module to a Q-Exactive Plus Orbitrap MS. The dual-gate setup was implemented by placing one ion gate before the SLIM module and a second ion gate after. The dual-gated ion injection approach allowed the new SLIM-Orbitrap platform to simultaneously perform an 11-meter SLIM separation, Orbitrap mass analysis using the highest selectable mass resolution setting (up to 140k), and high-energy collision induced dissociation (HCD) in ~25 minutes over an $m/z$ range of ~1500 amu. The SLIM-Orbitrap was initially characterized using a mixture of standard phosphazene cations and demonstrated an average SLIM CCS resolving power (Rp CCS ) of ~218 and SLIM peak capacity of ~156 while simultaneously obtaining high mass resolutions. SLIM-Orbitrap analysis with fragmentation was then performed on mixtures of standard peptides and two reverse peptides (SDGRG 1+ , GRGDS 1+ , Rp CCS = 305) to demonstrate the utility of combined HR-IMS-MS/MS measurements for peptide identification. Our new HR-IMS-MS/MS capability was further demonstrated by analyzing a complex lipid mixture and showcasing SLIM separations on isobaric lipids. In conclusion, this new SLIM-Orbitrap platform demonstrates a critical new capability for proteomics and lipidomics applications, and the high-resolution multimodal data obtainable with this system establishes the foundation for reference-free identification of unknown ion structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

CROCUS 3-D wind data from Argonne Deployable Mast during Urban Canyon IOP July 2024

During the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory, this dataset was collected by METEK uSonic-3 Class A MP sonic anemometer at 10 meter height of the Argonne Deployable Mast (ADM) from July 26 to July 28, 2024, .As part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), 3-D sonic anemometer on the ADM was set up to provide continuous measurements of wind and temperature in Chicago during July 2024. These measurements cover winds in X-, Y- and Z- direction and sonic temperature at 30-Hz resolution. The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.

cloud properties↗

DOE OSTI2022

Efficient Data Query for Gaussian Process Compressed Data through Value Range Estimation [Slides]

When the resolution of the data increases, data reduction methods are applied to simulation output, including Gaussian process, neural representation and compression algorithms. Lots of data analysis/visualization techniques requires data query, but data query from reduced representation is still challenging. This report will provide examples and provide possible answers to why data query from reduced representation is still challenging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

A Performant, Scalable Processing Pipeline for High‐Quality and FAIR Environmental Sensor Data

High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.

Pennington, Stephanie C. [Pacific Northwest Nation↗

DOE OSTI2022

A multi-scale temperature-based strategy to map hydrologic exchange flows in highly dynamic systems

Mapping and quantifying hydrologic exchange flows (HEFs) is critical to environmental monitoring and remediation at contaminated sites; however, these objectives are challenging in highly dynamic systems, e.g., along dam-regulated rivers, where HEFs vary rapidly. Direct seepage measurements are labor-intensive and difficult to automate, whereas indirect (e.g., thermal) and remote sensing methods have potential to allow continuous monitoring with limited field effort. We present a preliminary assessment of a multi-scale temperature-based strategy for monitoring HEFs along the Hanford Reach of the Columbia River, in eastern WA, United States. Five thermal methods were assessed. First, a vertical temperature profile (VTP) was installed into the streambed. The VTP data were analyzed using a data assimilation algorithm designed for automated real-time estimation in dynamic systems. Second, a thermal infrared (TIR) camera was used in roving surveys to identify seeps. Third, a TIR camera was stationed at the VTP site to collect images at 1-h intervals. Together, the two TIR datasets provided a basis to assess the potential for drone-based TIR. Fourth, temperature was measured at the sediment/water interface to assess fiber-optic distributed temperature sensing. Fifth, imagery from the ECOSTRESS satellite mission was acquired to assess the potential of spaceborne thermal monitoring. Based on our preliminary assessment, VTP, TIR, and bed temperature measurements provide complementary spatial coverage, temporal sampling, and resolution; these methods have potential for long-term, automated monitoring of HEFs. The publicly available spaceborne imagery, however, proved inadequate because of insufficient spatial resolution and data gaps resulting from cloud cover and revisit frequency.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Quantifying agricultural productive use of energy load in Sub-Saharan Africa and its impact on microgrid configurations and costs

The use of advanced energy technologies for agricultural purposes—such as irrigation, refrigeration, crop processing, and egg incubation—has the potential to increase crop yield, reduce vulnerability to changing precipitation patterns, increase shelf life, strengthen income and employment opportunities in rural areas, and reduce emissions by displacing fossil fuel-based technologies. These productive uses of energy (PUE) in remote areas could potentially be powered by microgrids that additionally serve otherwise unelectrified communities, most of which are located in rural Sub-Saharan Africa. Here, in this paper, we use high-resolution geospatial data to estimate the end-use electricity demand for a range of agricultural PUE across Sub-Saharan Africa, and we share these data in an open-access mapping tool. Next, we use REopt®, a techno-economic optimization model of energy systems, to determine the cost and system sizing implications of incorporating agricultural PUE into microgrid designs in Kenya and Zambia. We estimate the upper bound of agricultural PUE demand for irrigation, milling, shelling, refrigeration, and egg incubation across Sub-Saharan to be 16.8 TWh/yr. We find that incorporating local agricultural PUE into microgrid system designs increases the required system sizing while having minimal impact on the levelized cost of energy of these systems. Our analysis is the first to demonstrate the PUE potential in the agricultural sector at a 10x10-kilometer resolution across Sub-Saharan Africa and to show, at scale, how site-specific PUE can impact the cost and sizing of microgrids that are otherwise deployed to serve local household and community load.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

Xie, Yu↗

DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model: Preprint

DNI↗

DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗

DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗