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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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Utah FORGE Composite Raw Gravity Data 2021
This is a composite raw gravity dataset of the Utah FORGE area taken over time from December 2018 to to June 2021. The data shows differences in local gravity at different location across Utah FORGE. Included with the gravity data is the NAD83 UTM coordinates of each station in meters. More detailed information can be found in the readme included in the data resource.
Guide to Data Access and Utility Customer Confidentiality
This report explains the difference between whole-building energy usage data requests and other types of data requests utilities may receive. It outlines utility best practices for providing energy consumption information to building owners while respecting the confidentiality of utility customers.
Using Field-Metered Data to Quantify Annual Energy Use of Portable Air Conditioners
As many regions of the United States experience rising temperatures, consumers have come to rely increasingly on cooling appliances (including portable air conditioners) to provide a comfortable indoor temperature. Home occupants sometimes use a portable air conditioner (PAC) to maintain a desired indoor temperature in a single room or enclosed space. Although PACs in residential use are few compared to centrally installed and room air conditioning (AC) units, the past few years have witnessed an increase of PACs use throughout the United States. There is, however, little information and few research projects focused on the energy consumption and performance of PACs, particularly studies that collect information from field applications of PACs. The operation and energy consumption of PACs may differ among geographic locations and households, because of variations in cooling load, frequency, duration of use, and other user-selected settings. In addition, the performance of building envelope (thermal mass and air leakage) as well as inter-zonal mixing within the building would substantially influence the ability to control and maintain desirable indoor thermal conditions. Lawrence Berkeley National Laboratory (LBNL) conducted an initial field-metering study aimed at increasing the knowledge and data related to PAC operation and energy consumption in the United States. LBNL performed its field-metering study from mid-April to late October 2014. The study, which monitored 19 sites in the Northeastern United States (4 in upstate New York and 15 near Philadelphia), collected real-time data on PAC energy consumption along with information regarding housing characteristics, consumer behavior, and environmental conditions that were expected to affect PAC performance. Given the limited number of test sites, this study was not intended to be statistically representative of PAC users in the United States but rather to understand the system response to the cooling demand and to some extent, the operating hours of the studied units. Specifically, the primary objectives of the field-metering study were to (1) expand knowledge of the installation, energy consumption profiles, consumer patterns of use, and environmental parameters related to PAC use; (2) develop distributions of hours of PAC operation for three operating modes: standby, 1 fan-only, and cooling; and (3) describe how individual consumers’ selection of PAC capacity, the area of the space to be cooled, the temperature set point, and environmental conditions affect energy use. Beginning to understand the energy consumption of PACs operating in American homes and commercial settings will help develop a more accurate energy use profile that characterizes relevant variables. This report on LBNL’s field-metering study of PAC energy use describes: a general definition of a PAC and how it operates (section 2); current practices and sources of data for estimating PAC energy use (section 3); the process LBNL used to select field-metering sites, along with characteristics of the sites and the PACs studied (section 4); data collection methods and instrumentation (section 5); analysis methods (section 6); results and discussion (section 7); and conclusions (section 8).
Putting Our Industry's Data to Work: A Case Study of Large-Scale Data Aggregation: Preprint
With increasing deployment of Advanced Metering Infrastructure (AMI), Building Automation System (BAS) controls, Internet of Things (IoT) network devices, and data-driven evaluation, measurement, and verification studies, the building sector is currently generating a staggering amount of energy-related data. In the right hands, these data sets can contribute to increased comfort and energy savings for building occupants and a more reliable electrical grid; however, due to a combination of factors, including significant privacy concerns, much of the data that are presently generated and stored are not used outside of basic operational applications. In the past year, our team has dedicated over 2,000 person-hours to accessing building energy data for a project funded by the U.S. Department of Energy (DOE) Building Technologies Office. We sought whole-building or end-use (e.g., lighting) timeseries data at the individual-building or equipment level where possible or aggregated information, such as timeseries averages and quartiles by building type (e.g., office, retail, hospital), where sharing individual building information was not an option. We are additionally working with IoT and BAS data sets to derive information important to the project. We have assembled an extensive data set that will enable the development of publicly available end-use load profiles to benefit the U.S. building and electricity industries. Here we present an overview of the data set that we have assembled to date, the motivators and approaches that got us here, and the lessons we learned through our efforts. We also discuss work underway that presents additional options for future data access.
Empirical Results Suggest Quasi-Monte Carlo Sampling Increases Accuracy in the Estimation of Annual Energy Production from Operational Data
Quasi-Monte Carlo sampling was implemented in the OpenOA annual energy production analysis method. Preliminary results show that this method improves the order of the asymptotic convergence of OpenOA’s AEP estimates.
Energy-Water Dynamics of Data Centers and Their Consequences for the Electricity Grid and Water Availability
Briefing slide deck on IM3's work on the energy and water impacts of data centers. Briefing was given to BER program managers on 19-Mar and 26-Mar 2025.
Apparatus and method for data communication in an energy distribution network
A system for communicating information on an energy distribution network is disclosed. In one embodiment, the system includes a local supervisor on a communication network, wherein the local supervisor can collect data from one or more energy generation/monitoring devices. The system also includes a command center on the communication network, wherein the command center can generate one or more commands for controlling the one or more energy generation devices. The local supervisor can periodically transmit a data signal indicative of the data to the command center via a first channel of the communication network at a first interval. The local supervisor can also periodically transmit a request for a command to the command center via a second channel of the communication network at a second interval shorter than the first interval. This channel configuration provides effective data communication without a significant increase in the use of network resources.
Source energy spectra from demodulation of solar particle data by interplanetary and coronal transport
The data on source energy spectra of solar cosmic rays (SCR), i.e. the data on the spectrum form and on the absolute SCR are of interest for three reasons: (1) the SCR contain the energy comparable to the total energy of electromagnetic flare radiation (less than or equal to 10 to the 32nd power ergs); (2) the source spectrum form indicates a possible acceleration mechanism (or mechanism); and (3) the accelerated particles are efficiently involved in nuclear electromagnetic and plasma processes in the solar atmosphere. Therefore, the data on SCR source spectra are necessary for a theoretical description of the processes mentioned and for the formulation of the consistent flare model. Below it is attempted to sound solar particle sources by means of SCR energy spectrum obtained near the Sun, at the level of the roots of the interplanetary field lines in the upper solar corona. Data from approx. 60 solar proton events (SPE) between 1956-1981. These data were obtained mainly by the interplanetary demodulation of observed fluxes near the Earth. Further, a model of coronal azimuthal transport is used to demodulate those spectra, and to obtain the source energy spectra.
Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing
Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.
Searches for correlation between UHECR events and high-energy gamma-ray Fermi-LAT data
The astrophysical sources responsible for ultra high-energy cosmic rays (UHECRs) continue to be one of the most intriguing mysteries in astrophysics. Here, we present a comprehensive search for correlations between high-energy (≳ 1 GeV) gamma-ray events from the Fermi Large Area Telescope (LAT) and UHECRs (≳ 60 EeV) detected by the Telescope Array and the Pierre Auger Observatory. We perform two separate searches. First, we conduct a standard cross-correlation analysis between the arrival directions of 148 UHECRs and 360 gamma-ray sources in the Second Catalog of Hard Fermi-LAT sources (2FHL). Second, we search for a possible correlation between UHECR directions and unresolved Fermi-LAT gamma-ray emission. For the latter, we use three different methods: a stacking technique with both a model-dependent and model-independent background estimate, and a cross-correlation function analysis. We also test for statistically significant excesses in gamma rays from signal regions centered on Cen A and the Telescope Array hotspot. There was no significant correlation is found in any of the analyses performed, except a weak (≲ 2σ) hint of signal with the correlation function method on scales ~ 1°. Upper limits on the flux of possible power-law gamma-ray sources of UHECRs are derived.
United States Industrial Sector Energy End Use Analysis
The United States Department of Energy’s (DOE) Energy Information Administration (EIA) conducts the Manufacturing Energy Consumption Survey (MECS) to provide detailed data on energy consumption in the manufacturing sector. The survey is a sample of approximately 15,000 manufacturing establishments selected from the Economic Census - Manufacturing Sector. MECS provides statistics on the consumption of energy by end uses (e.g., boilers, process, electric drives, etc.) disaggregated by North American Industry Classification System (NAICS) categories. The manufacturing sector (NAICS Sector 31-33) consists of all manufacturing establishments in the 50 States and the District of Columbia. According to the NAICS, the manufacturing sector comprises establishments engaged in the mechanical, physical, or chemical transformation of materials, substances, or components into new products. The establishments are physical facilities such as plants, factories, or mills. For many of the sectors in the MECS datasets, information is missing because the reported energy use is less than 0.5 units or BTUs, or is withheld to avoid disclosing data for individual establishments, or is withheld because the standard error is greater than 50%. We infer what the missing information likely are using several approximations techniques. First, much of the missing data can be easily calculated by adding or subtracting other values reported by MECS. If this is not possible (e.g. two data are missing), we look at historic MECS reports to help identify the breakdown of energy use in the past and assume it remained the same for the current MECS. Lastly, if historic data is also missing, we assume that 3 digit NAICS classifications predict energy use in their 4, 5, or 6 digit NAICS sub-classifications, or vice versa. Along with addressing data gaps, end use energy is disaggregated beyond the specified MECS allocations using additional industry specific energy consumption data. The result is a completed table of energy end use by sector with mechanical drives broken down by pumps, fans, compressed air, and drives.
Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and Planck thermal Sunyaev-Zel’dovich effect observations. I. Measurements, systematics tests, and feedback model constraints
Here we present a tomographic measurement of the cross-correlation between thermal Sunyaev-Zel’dovich (TSZ) maps from Planck and the Atacama Cosmology Telescope and weak galaxy lensing shears measured during the first three years of observations of the Dark Energy Survey. This correlation is sensitive to the thermal energy in baryons over a wide redshift range and is therefore a powerful probe of astrophysical feedback. We detect the correlation at a statistical significance of 21σ, the highest significance to date. We examine the TSZ maps for potential contaminants, including cosmic infrared background and radio sources, finding that cosmic infrared background has a substantial impact on our measurements and must be taken into account in our analysis. We use the cross-correlation measurements to test different feedback models. In particular, we model the TSZ using several different pressure profile models calibrated against hydrodynamical simulations. Our analysis marginalizes over redshift uncertainties, shear calibration biases, and intrinsic alignment effects. We also marginalize over Ω m and σ 8 using Planck or DES priors. We find that the data prefer the model with a low amplitude of the pressure profile at small scales, compatible with a scenario with strong active galactic nuclei feedback and ejection of gas from the inner part of the halos. When using a more flexible model for the shear profile, constraints are weaker, and the data cannot discriminate between different baryonic prescriptions.
Constraints on cosmological birefringence energy dependence from CMB polarization data
We study the possibility of constraining the energy dependence of cosmological birefringence by using CMB polarization data. We consider four possible behaviors, characteristic of different theoretical scenarios: energy-independent birefringence motivated by Chern-Simons interactions of the electromagnetic field, linear energy dependence motivated by a 'Weyl' interaction of the electromagnetic field, quadratic energy dependence, motivated by quantum gravity modifications of low-energy electrodynamics, and inverse quadratic dependence, motivated by Faraday rotation generated by primordial magnetic fields. We constrain the parameters associated to each kind of dependence and use our results to give constraints on the models mentioned. We forecast the sensitivity that Planck data will be able to achieve in this respect.
Analyzing Changes in the Complexity of Climate in the Last Four Decades Using MERRA-2 Radiation Data
The energy balance of the Earth is controlled by the shortwave and longwave radiation emitted to space. Changes in the thermodynamic state of the system over time afect climate and are noticeable when viewing the system as a whole. In this paper, we study the changes in the complexity of climate in the last four decades using data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). First, we study the complexity of the shortwave and longwave radiation felds independently using Approximate Entropy and Sample Entropy, observing that the rate of complexity change is faster for shortwave radiation. Then, we study the causality of those changes using Transfer Entropy to capture the non-linear dynamics of climate, showing that the changes are mainly driven by the variations in shortwave radiation. The observed behavior of climatic complexity could be explained by the changes in cloud amount, and we research that possibility by investigating its evolution from a complexity perspective using data from the International Satellite Cloud Climatology Project (ISCCP).
Testing an Energy Balance Model for Estimating Actual Evapotranspiration Using Remotely Sensed Data
An energy-balance model is used to estimate daily evapotranspiration for 3 days for a barley field and a wheat field near Hannover, Federal Republic of Germany. The model was calibrated using once-daily estimates of surface temperatures, which may be remotely sensed. The evaporation estimates were within the 95% error bounds of independent eddy correlation estimates for the daytime periods for all three days for both sites, but the energy-balance estimates are generally higher; it is unclear which estimate is biassed. Soil moisture in the top 2 cm of soil, which may be remotely sensed, may be used to improve these evaporation estimates under partial ground cover. Sensitivity studies indicate the amount of ground data required is not excessive.