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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

Multi-scale↗
DOE OSTI2024

Heat pumps for all? Distributions of the costs and benefits of residential air-source heat pumps in the United States

Electrification of fossil-fuel combustion in buildings is a key component of achieving global greenhouse gas emissions targets. We use physics simulations of 550,000 statistically representative households to analyze distributions of the costs and benefits of three air-to-air heat pump performance levels, with and without insulation upgrades, across the diversity of the US housing stock. We find positive greenhouse gas reductions in every US state for all performance levels across five 2022-2038 electric grid scenarios, with full adoption reducing national emissions by 5%-9%. We find that air-to-air heat pumps could be cost effective without subsidies in 59% of households (65 million). However, efficiency is key: whereas minimum-efficiency equipment could increase energy bills in 39% of households, this fraction is only 19% when also upgrading insulation or 5% when using higher-efficiency equipment, though both strategies have higher upfront costs. Such affordability challenges could be addressed through supportive incentives, policy, and innovation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Model America - data and models of every U.S. building

The 5-year goal of the 'Model America' concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) - OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus(v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy's (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). 'Model America - data and models of every U.S. building. [Data set].' Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Community-Scale Energy Efficiency Assessment for Zero Net Energy Using the URBANopt Simulation Platform: Preprint

This project explored an innovative modeling approach for assessing the energy efficiency potential of the existing low-income Oceano neighborhood, in unincorporated San Luis Obispo County, and its effects on renewable generation requirements for achieving zero net energy (ZNE). One particular challenge faced was ascertaining the technical energy savings potential across a large neighborhood in a timely manner. The project overcame this challenge through the use of the URBANopt simulation platform, developed at the National Renewable Energy Laboratory. URBANopt manages the automated model creation, simulation, and result aggregation for different design scenarios of existing or candidate buildings in a large neighborhood. Customized energy efficiency packages considered for the residential and commercial sector in the neighborhood included lighting, HVAC, plug load, water heating, envelope, and energy education measures. As a result of the analysis for the Oceano community, energy efficiency measures that are available through incentive programs may provide a 7.4% reduction in the required distributed energy resource (DER) size, corresponding to a 25,000 ft2 decrease in PV array area, to achieve ZNE. Through this work, a computational tool was developed and is being leveraged by the County of San Luis Obispo (CoSLO). The tool has enabled the CoSLO staff to perform similar analysis of the technical potential for energy efficiency, and an understanding of the DER asset requirements and potential savings in other communities within the county.

building energy modeling↗
DOE OSTI2024

An Open-Source Framework for Characterizing Urban Energy Models: Integrating Top-Down and Bottom-Up Methods to Predict Residential Buildings Characteristics: Preprint

Bottom-up urban energy models are crucial for understanding current energy use patterns and informing design strategies. However, accurately characterizing these models to represent different communities remains a challenge due to the extensive data needed for simulating existing energy use behavior. This data includes information related to human activities and building characteristics, all of which correlate with socioeconomic factors. To overcome this challenge, we developed an automated framework that utilizes both top-down and bottom-up data, to predict unknown building and occupant characteristics that are needed for more accurate and equitable modeling and analytics. Our framework, integrated into the URBANopt district energy modeling platform, uses statistical data models from ResStock. URBANopt models co-located buildings and neighborhoods. At this scale there are data gaps in building characteristic data, such as materials, insulation, occupancy, income, and energy usage of the buildings. To address this data gap, we use ResStock data, representative at the census tract scale, and develop machine-learning and deeplearning techniques to disaggregate it to individual buildings. By mapping unique occupant, building and economic properties to URBANopt energy models, we gain detailed insights into the variability of building energy use across different neighborhoods. This insight helps deploy technologies for co-located buildings and supports targeted upgrades for communities with unique economic and demographic characteristics, ensuring energy equity. Accurate characterization of energy models allows us to develop equitable strategies tailored to diverse neighborhoods, whether underserved or affluent. Our automated framework streamlines energy modeling and provides a reliable tool for building energy characterization.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2025

Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks

Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT devices in smart grids. This paper investigates the IoT device modeling of a thermostatic load and implements the recurrent neural networks model for short-term load forecasting in this IoT-based thermostatic load. The recurrent neural network structure is leveraged to build a load forecasting model on temporal correlation. The temporal recurrent neural network layers including long short-term memory cells are employed to learn the data from both the simulation platform and New South Wales residential datasets. The simulation results are provided for demonstration.

electric load forecasting↗
DOE OSTI2020

Evaluating Building Energy Code Compliance and Savings Potential through Large-Scale Simulation with Models Inferred by Field Data

Building energy code compliance is the crucial link between the actual energy savings and the efficiency prescribed in energy codes. A research project aiming to identify opportunities to reduce energy consumption in new single-family residential construction by increasing compliance with the building energy code was conducted in several states of the United States. The study was comprised of three phases: (1) a baseline study to document typical practice and identify opportunities for improvement based on empirical data gathered from the field; (2) an education and training phase targeting the opportunities identified; and (3) a post-study to assess whether a reduction in average state-wide energy use could be achieved following the education and training phase. We proposed a novel methodology based on large-scale building energy simulation inferred by limited field data to assess the performance of a large population of homes. This paper presents the methodology, findings, and results of this study. The state-wide average energy consumption decreased at Phase III from Phase I for five of the seven states involved in the analysis. The measure-level savings potential analysis shows an overall reduction. Overall, the training and education phase plays a recognizable role in improving compliance with building energy codes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A Machine Learning-Assisted Framework to Control Thermally Anisotropic Building Envelopes in Residential Buildings

To curb the energy consumption of buildings and their related CO2 emissions, Oak Ridge National Laboratory (ORNL) has developed the thermally anisotropic building envelope (TABE) —a multi-layer design comprising insulation materials and metal foils connected to thermal loops. In this study, a machine learning-assisted framework was developed to control the TABE in residential buildings to reduce the computation load for future optimal rule-based control and application. First, a 2D finite element model was established in COMSOL to calculate the hourly heat flux through exterior walls installed with the TABE. Then, TABE wall heat fluxes were simulated for various given indoor and outdoor boundary conditions, as well as thermal loops fluid temperatures and flow rates. Since the finite element simulations are computationally expensive, an artificial neural network (ANN) was then trained to use as a proxy of the finite element (COMSOL) modeling. Finally, the trained ANN model was coupled with the EnergyPlus model to predict the energy consumption of a US Department of Energy prototype single-family house installed with the TABE. An optimal simple rule-based control was determined from predefined rules for a case study. The results demonstrate that the developed machine learning–assisted framework can reduce 99.9% of the computation time while efficiently managing residential building energy for installed TABE walls.

Shen, Zhenglai↗
DOE OSTI2021

OCHRE™ (Object-Oriented Controllable High-Resolution Residential Energy Model) [SWR-19-71]

OCHRE™: Object-Oriented Controllable High-Resolution Residential Energy Model A high-fidelity, high-resolution residential building model with behind-the-meter DERs and flexible load models that integrates with controllers and distribution models in building-to-grid co-simulation platforms. The full documentation for OCHRE can be found at https://ochre-docs-final.readthedocs.io/en/latest/

Maguire, Jeffrey↗
DOE OSTI2024

Nova Analysis: Holistically Valuing the Contributions of Residential Efficiency, Solar and Storage

Policies to address climate change and grid modernization, in combination with cost reductions and technological advancements in energy efficiency (EE) and distributed energy resources (DER), are driving rapid deployment of building electrification and energy efficiency retrofits, rooftop solar photovoltaics (PV), smart thermostats, smart water heaters, and battery energy storage. In residential buildings, there are multiple stakeholders (occupant, utility, aggregator, society at large) that are each focused on different value streams. This project uses a suite of metrics intended to all of these value streams to try to holistically analyze the benefits that come from solar, storage, and energy efficiency. To demonstrate the value of these metrics, a semi annual study was performed simulating hundreds of homes across the U.S. under several different upgrade scenarios.

14 SOLAR ENERGY↗
NASA NTRS1972

Sonic-boom-induced building structure responses including damage.

Concepts of sonic-boom pressure loading of building structures and the associated responses are reviewed, and results of pertinent theoretical and experimental research programs are summarized. The significance of sonic-boom load time histories, including waveshape effects, are illustrated with the aid of simple structural elements such as beams and plates. Also included are discussions of the significance of such other phenomena as three-dimensional loading effects, air cavity coupling, multimodal responses, and structural nonlinearities. Measured deflection, acceleration, and strain data from laboratory models and full-scale building tests are summarized, and these data are compared, where possible, with predicted values. Damage complaint and claim experience due both to controlled and uncontrolled supersonic flights over communities are summarized with particular reference to residential, commercial, and historic buildings. Sonic-boom-induced building responses are compared with those from other impulsive loadings due to natural and cultural events and from laboratory simulation tests.

Clarkson, B. L.↗
DOE OSTI2022

Zero Export Feeder Through Transactive Markets

This project is aimed at creating a transactive energy market to address the challenges faced by utility providers when increasing distributed energy resource (DER) adoption in their service area. One major challenge is mitigating export back to the grid during times of excess production. The transactive energy market operates at the distribution level and balances the supply and demand on the feeder, thus maintaining a zero-energy export at the primary feeder head. The market participants in this case are the residential customers on the feeder, who bid into the market. Building controls are then optimized based on the settled price. Market performance was demonstrated in this study by simulating different levels of DER penetration on a selected Pepco feeder. The feeder successfully achieved a zero export while providing cost-effective electricity to the participants, demonstrating that this market design can enable high DER penetration on existing feeders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Performance assessment of active insulation systems in residential buildings for energy savings and peak demand reduction

Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ~980 to 2,290 kWh in a single-family home with a floor area of ~220 m 2 compared with an IECC 2018 baseline. We found the energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

City-Wide Distributed Roof-Top Photovoltaic System Adoption Forecast, Grid Impact Simulation, & Neighborhood Microgrid Contribution Assessment

The adoption of distributed photovoltaic (PV) systems grew significantly in recent years. Market projections anticipate future growth for both residential and commercial installations. To understand grid impacts associated with distributed PV, useful hosting capacity studies require accurate representations of the spatial distribution of PV adoptions. Prediction of PV locations and numbers depends on median income data, building use zoning maps, and permit records to understand existing trends and predict future adoption rates and locations throughout an entire city. Using the PV adoption data, advanced and realistic simulations were performed to capture the distributed PV impacts on the grid. Also, using graph theory community detection hundreds of neighborhood microgrids can be discovered for the entire city by identifying densely connected loads that are sparsely connected to other communities. Then, based on the PV adoption predictions, this work identified the contribution of PV within each of the newly discovered graph theory defined microgrid communities.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Model America - Arizona extract from ORNL's AutoBEM v1.1

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States:Data (846.3MB *.csv) - minimalist list of each building (rows) for the following fields (columns) • ID - unique building ID • Centroid - building center location in latitude/longitude (from Footprint2D) • Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) • State_abbr - state name • Area - estimate of total conditioned floor area (ft2) • Area2D - footprint area (ft2) • Height - building height (ft) • NumFloors - number of floors (above-grade) • WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) • CZ - ASHRAE Climate Zone designation • BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards • Standard - building vintage • Sample Models (114GB*.zip by county) - OpenStudio and EnergyPlus building energy models named according to IDThis data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The hygrothermal performance of continuous and cavity wood fiber insulation

Wood fiber insulation is an emerging material known for its effective thermal performance and moisture management properties, making it a compelling alternative to conventional insulation. Its vapor permeability and ability to regulate indoor humidity contribute to improved building durability and comfort, particularly in varying climate conditions. The goal of this project is to characterize the hygrothermal performance of a new wood fiber insulation product line in U.S. climate zones and to facilitate the design and construction of the product in residential and light commercial building envelopes. This study investigates the thermal and hygrothermal performance of wood fiber insulation, both as continuous and cavity insulation. The research employs an exhaustive simulation task and an environmental chamber test of a wood fiber insulation. Over 400 simulations were conducted to study the hygrothermal characteristics of bio-based wood fiber insulation in various building envelope configurations across four climate zones. Environmental chamber tests were conducted under controlled winter conditions representative of Climate Zone 5A (Chicago, IL) to complement the simulation results. The chamber tests focused on the hygrothermal performance of the wood fiber insulation and the sheathing board to assess the insulation's ability to manage moisture in a cold climate. Simulation and environmental chamber test results were analyzed to evaluate the insulation's consistency and efficacy across diverse climatic zones. Results show that with a proper moisture control strategy, both cavity and continuous wood fiber insulation can work properly in both new and retrofit constructions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Hygrothermal Performance of Continuous and Cavity Wood Fiber Insulation

Wood fiber insulation is an emerging material known for its effective thermal performance and moisture management properties, making it a compelling alternative to conventional insulation. Its vapor permeability and ability to regulate indoor humidity contribute to improved building durability and comfort, particularly in varying climate conditions. The goal of this project is to characterize the hygrothermal performance of a new wood fiber insulation product line in U.S. climate zones and to facilitate the design and construction of the product in residential and light commercial building envelopes. This study investigates the thermal and hygrothermal performance of wood fiber insulation, both as continuous and cavity insulation. The research employs an exhaustive simulation task and an environmental chamber test of a wood fiber insulation. Over 400 simulations were conducted to study the hygrothermal characteristics of bio-based wood fiber insulation in various building envelope configurations across four climate zones. Environmental chamber tests were conducted under controlled winter conditions representative of Climate Zone 5A (Chicago, IL) to complement the simulation results. The chamber tests focused on the hygrothermal performance of the wood fiber insulation and the sheathing board to assess the insulation's ability to manage moisture in a cold climate. Simulation and environmental chamber test results were analyzed to evaluate the insulation's consistency and efficacy across diverse climatic zones. Results show that with a proper moisture control strategy, both cavity and continuous wood fiber insulation can work properly in both new and retrofit constructions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Evaluation of Uplight Energy Saving Actions Using ResStock TM (Summary of Results)

As part of a portfolio of product offerings, Uplight produces energy savings estimates to inform a utility’s residential customers of the expected benefits that would arise from adopting energy efficiency measures, which are termed “energy saving actions. ”Uplight’s current practice is to use a proprietary building simulation model to estimate energy and cost benefits from these actions, as discussed in Maguire et al. Uplight requested that the National Renewable Energy Laboratory (NREL) provide an independent estimate of the energy savings for a defined list of energy saving actions. NREL performed this study using two established building energy simulation tools: EnergyPlus ® ( EnergyPlus ) and ResStock TM .(

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗