Results for “Energy Data” — Page 36

DOE OSTI2025

Understanding Commercial Building Energy Use in San Antonio to Waco: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Greater Gulf Coastal Plains: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in Northern Arizona and Washington County, Utah: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Rural Midwest North Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Tulsa to Wichita Falls I-44 Corridor: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Central Gulf Coast Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Greater Dallas-Fort Worth Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Huntsville-Florence Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Duluth-Grand Forks-Fargo Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Understanding Commercial Building Energy Use in the Omaha-Lincoln Area: Building Stock Segmentation for Retrofit Planning

This report is part of the second phase of a publication series focusing on approximately 100 different local geographies, or "clusters." Each report provides characteristic features and energy data for commercial buildings in a specific area to help policy makers at the city, county, and state level better understand building energy use and emissions. This report breaks down the energy consumption and emissions of the building stock in the counties shown in Figure 2 by building type, building size, end use, energy consumption, emissions, and segment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Croplands are extremely productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

The Dark Energy Camera All Data Everywhere cosmic shear project V: Constraints on cosmology and astrophysics from 270 million galaxies across 13,000 deg$^2$ of the sky

We present constraints on models of cosmology and astrophysics using cosmic shear data vectors from three datasets: the northern and southern Galactic cap of the Dark Energy Camera All Data Everywhere (DECADE) project, and the Dark Energy Survey (DES) Year 3. These data vectors combined consist of 270 million galaxies spread across 13,000 ${\rm deg}^2$ of the sky. We first extract constraints for $Λ$CDM cosmology and find $S_8= 0.805^{+0.019}_{-0.019}$ and $Ω_{\rm m} = 0.262^{+0.023}_{-0.036}$, which is consistent within $1.9 σ$ of constraints from the Planck satellite. Extending our analysis to dynamical dark energy models shows that lensing provides some (but still minor) improvements to existing constraints from supernovae and baryon acoustic oscillations. Finally, we study six different models for the impact of baryons on the matter power spectrum. We show the different models provide consistent constraints on baryon suppression, and associated cosmology, once the astrophysical priors are sufficiently wide. Current scale-cut approaches for mitigating baryon contamination result in a residual bias of $\approx 0.3σ$ in the $S_8, Ω_{\rm m}$ posterior. Using all scales with dedicated baryon modeling leads to negligible improvement as the new information is used solely to self-calibrate the baryon model on small scales. Additional non-lensing datasets, and/or calibrations of the baryon model, will be required to access the full statistical power of the lensing measurements. The combined dataset in this work represents the largest lensing dataset to date (most galaxies, largest area) and provides an apt testing ground for analyses of upcoming datasets from Stage IV surveys. The DECADE shear catalogs, data vectors, and likelihoods are made publicly available.

Anbajagane, D. [Chicago U., Astron. Astrophys. Ctr↗

DOE OSTI2021

Overcoming Communications Outages in Inverter Downtime Analysis

This paper presents two methods of detecting inverter downtime and estimating lost production from downtime events using timeseries system production measurements. The methods focus on distinguishing communications interruptions from true production outages and are successful in most cases. To enable fleet-scale analysis of inverter availability, the methods are designed to be semi-autonomous. The first method uses only inverter and system meter AC power measurements while the second uses cumulative production and expected energy data to classify outages and estimate lost production. The methods are demonstrated using real field data and the results are discussed.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2025

Projected Operational Energy Life Cycle Data 2025

These excel files contain the environmental emission projections for key building energy sources: electricity, natural gas, propane (LPG), and fuel oil. The sources and methods are provided in the report "Projected Operational Energy Life Cycle Data Development: 2025 Update." The report and data are an update to the data previously posted here: https://netl.doe.gov/energy-analysis/details?id=f8890fac-be55-44ac-aaa9-e2888bfabe93

annual energy outlook↗

DOE OSTI2015

Data, exergy, and energy analysis of a vertical-bore, ground-source heat pump to for domestic water heating under simulated occupancy conditions

Evidence is provided to support the view that greater than two-thirds of energy required to produce domestic hot water may be extracted from the ground which serves as renewable energy resource. The case refers to a 345 m2 research house located in Oak Ridge, Tennessee, 36.01 N 84.26 W in a mixed-humid climate with HDD of 2218 C-days (3993 F-days) and CDD of 723 C-days (1301 F-days). The house is operated under simulated occupancy conditions in which the hot water use protocol is based on the Building America Research Benchmark Definition (Hendron 2008; Hendron and Engebrecht 2010) which captures the water consumption lifestyles of the average family in the United States. The 5.275 (1.5-ton) water-to-water ground source heat pump (WW-GSHP) shared the same vertical bore with a 7.56 KW water-to-air ground source heat pump for space conditioning the same house. Energy and exergy analysis of data collected continuously over a twelve month period provide performance metrics and sources of inherent systemic inefficiencies. Data and analyses are vital to better understand how WW-GSHPs may be further improved to enable the ground to be used as a renewable energy resource.

15 GEOTHERMAL ENERGY↗

DOE OSTI2013

Validation of Criticality Safety Calculations with SCALE 6.2

SCALE 6.2 provides numerous updates in nuclear data, nuclear data processing, and computational tools utilized in the criticality safety calculational sequences relative to SCALE 6.1. A new 252-group ENDF/B-VII.0 multigroup neutron library, improved ENDF/B-VII.0 continuous energy data, as well as the previously deployed 238-group ENDF/B-VII.0 neutron library are included in SCALE 6.2 for criticality safety analysis. The performance of all three libraries for keff calculations is examined with a broad sampling of critical experiment models covering a range of fuels and moderators. Critical experiments from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (IHECSBE) that are available in the SCALE Verified, Archived Library of Inputs and Data (VALID) are used in this validation effort. Over 300 cases are used in the validation of KENO V.a, and a more limited set of approximately 50 configurations are used for KENO-VI validation. Additionally, some KENO V.a cases are converted to KENO-VI models so that an equivalent set of experiments can be used to validate both codes. For continuous-energy calculations, SCALE 6.2 provides improved performance relative to SCALE 6.1 in most areas with notable improvements in fuel pin lattice cases, particularly those with mixed oxide fuel. Multigroup calculations with the 252-group library also demonstrate improved performance for fuel lattices, uranium (high and intermediate enrichment) and plutonium metal experiments, and plutonium solution systems. Overall, SCALE 6.2 provides equivalent or smaller biases than SCALE 6.1, and the two versions of KENO provide similar results on the same suite of problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Weak Lensing Analysis of SPT selected Galaxy Clusters using Dark Energy Survey Science Verification Data

We present weak-lensing (WL) mass constraints for a sample of massive galaxy clusters detected by the South Pole Telescope (SPT) via the Sunyaev-Zel'dovich effect (SZE). We use griz imaging data obtained from the Science Verification (SV) phase of the Dark Energy Survey (DES) to fit the WL shear signal of 33 clusters in the redshift range 0.25 <= z <= 0.8 with NFW profiles and to constrain a four-parameter SPT mass-observable relation. To account for biases in WL masses, we introduce a WL mass to true mass scaling relation described by a mean bias and an intrinsic, lognormal scatter. We allow for correlated scatter within the WL and SZE mass-observable relations and use simulations to constrain priors on nuisance parameters related to bias and scatter from WL. We constrain the normalization of the zeta-M-500 relation, A(SZ) = 12.0(-6.7)(+2.6) when using a prior on the mass slope B-SZ from the latest SPT cluster cosmology analysis. Without this prior, we recover A(SZ) = 10.8(-5.2)(+2.3) and B-SZ = 1.30(-0.44)(+0.22). Results in both cases imply lower cluster masses than measured in previous work with and without WL, although the uncertainties are large. The WL derived value of B-SZ is approximate to 20 per cent lower than the value preferred by the most recent SPT cluster cosmology analysis. The method demonstrated in this work is designed to constrain cluster masses and cosmological parameters simultaneously and will form the basis for subsequent studies that employ the full SPT cluster sample together with the DES data.

cosmology: observations↗

DOE OSTI2022

Generation of Data-Driven Expected Energy Models for Photovoltaic Systems

Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant over-estimations, which impacts accurate diagnoses of field operations and maintenance issues. This research addresses this issue by using machine learning to develop a data-driven expected energy model that can more accurately generate inferences for energy production of PV systems. Irradiance and system capacity information was used from 172 sites across the United States to train a series of models using Lasso linear regression. The trained models generally perform better than the commonly used expected energy model from international standard (IEC 61724-1), with the two highest performing models ranging in model complexity from a third-order polynomial with 10 parameters (Radj2 = 0.994) to a simpler, second-order polynomial with 4 parameters (Radj2=0.993), the latter of which is subject to further evaluation. Subsequently, the trained models provide a more robust basis for identifying potential energy anomalies for operations and maintenance activities as well as informing planning-related financial assessments. We conclude with directions for future research, such as using splines to improve model continuity and better capture systems with low (≤1000 kW DC) capacity.

14 SOLAR ENERGY↗