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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development of building energy asset rating using stock modelling in the USA

The US Building Energy Asset Score helps building stakeholders quickly gain insight into the efficiency of building systems (envelope, electrical and mechanical systems). A robust, easy-to-understand 10-point scoring system was developed to facilitate an unbiased comparison of similar building types across the country. The Asset Score does not rely on a database or specific building baselines to establish a rating. Rather, distributions of energy use intensity (EUI) for various building use types were constructed using Latin hypercube sampling and converted to a series of stepped linear scales to score buildings. A score is calculated based on the modelled source EUI after adjusting for climate. A web-based scoring tool, which incorporates an analytical engine and a simulation engine, was developed to standardize energy modelling and reduce implementation cost. This paper discusses the methodology used to perform several hundred thousand building simulation runs and develop the scoring scales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

An open-source framework for simulation-based testing of buildings control strategies

Here this paper presents a simulation framework for evaluating building control strategies (BCSs), developed with VOLTTRON, an opensource platform that integrates data, devices, and systems for sensing and control applications, and is based on co-simulation interfaces. It realizes an integrated environment for both testing and deploying BCSs, thereby eliminating the need for having a dedicated implementation of BCS for testing. For the first time, this framework provides critical functionalities, including scalable communication management and time-drive simulation advance, i.e., advancing the simulation based on clock time. We applied this framework to evaluating two BCSs from ASHRAE Guideline 36-2008 and ASHRAE Standard 90.1-2019. The evaluation results show that those BCSs primarily benefit the heating operation by reducing gas usage but yield insignificant savings in electricity consumption. The results also emphasize the importance of tuning the parameters of the BCSs to achieve better performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Model America: Data and Models for 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 with "Model America v1". 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 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2". This dataset contains OSM and IDF zip files for every U.S. county. Each zip file contains the generated buildings from that county. The .csv input data contains the following data fields: 1. ID - the Unique Building Identifier (UBID), generated using the Pacific Northwest National Laboratory (PNNL) BuildingID framework 2. Centroid - building center location in latitude/longitude (from Footprint2D) 3. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 4. State_abbr - state name 5. Area - estimate of total conditioned floor area (ft2) 6. Area2D - footprint area (ft2) 7. Height - building height (ft) 8. NumFloors - number of floors (above-grade) 9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 10. CZ - ASHRAE Climate Zone designation 11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 12. Standard - building vintage This 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) 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). Update (September 23, 2025): We corrected the ID field in all state-level.csv input files to ensure one-to-one consistency with the corresponding .osm and .idf output files. The schema and file structure are unchanged; only the values in the ID column were modified. No files were added or removed, and the .zip bundles (containing .osm / .idf) are unchanged. The corrected .csv inputs were re-extracted in March 2025 from the original data generated ~ 2021 (Theta supercomputer runs), and published here to align input IDs with model outputs. Update (September 6, 2026): The Model America dataset was updated to replace the previous building ID field with the Unique Building Identifier (UBID), using the Pacific Northwest National Laboratory (PNNL) BuildingID framework. UBIDs provide standardized, location-based identifiers for individual building footprints and improve interoperability with other building and geospatial datasets. The data files containing the previous building identifiers were updated to include UBIDs. This update standardizes building identification; the underlying Model America building characteristics and energy simulation results were not recomputed as part of this update.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Solar-Reflective “Cool” Walls: Benefits, Technologies, and Implementation

Raising the albedo (solar reflectance) of a building’s walls reduces unwanted solar heat gain in the cooling season. This saves electricity and lowers peak power demand by decreasing the need for air conditioning. It can also cool the outside air, which can mitigate the urban heat island effect and also improve air quality by slowing the reactions that produce smog. This project quantified the energy savings, peak demand reduction, urban cooling, and air quality improvements attainable from solar-reflective “cool” walls in California; collaborated with industry to assess the performance of existing cool-wall technologies, and to develop innovative cool-wall solutions; and worked with state and federal government agencies, utilities, and industry to create a cool-wall infrastructure, including application guidelines, a product rating program, incentives, and building code credits. Simulations indicate that cool walls provide annual energy savings, peak demand reduction, annual emission reduction, and summer heat island mitigation benefits comparable to those yielded by cool roofs, and are helpful across California and in most of the southern half of the United States (that is, in U.S. climate zones 1—4). Natural exposure trials conducted at three sites in California and another three sites across the United States indicate that cool-wall materials tend to stay clean and reflective. Significant advances were made in novel cool-wall technologies, such as fluorescent cool pigments that expand the color palette for cool-wall products. We prepared guidelines for the climate- and building-appropriate use of cool walls, convened a stakeholder workshop, and created a working group. Ongoing efforts seek to introduce or expand cool-wall provisions in building energy standards, green building programs, and energy efficiency incentive programs, and to develop a cool-wall product rating system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Energy and Energy Cost Savings Analysis of the 2015 IECC for Commercial Buildings

As required by statute (42 USC 6833), DOE recently issued a determination that ANSI/ASHRAE/IES Standard 90.1-2013 would achieve greater energy efficiency in buildings subject to the code compared to the 2010 edition of the standard. Pacific Northwest National Laboratory (PNNL) conducted an energy savings analysis for Standard 90.1-2013 in support of its determination . While Standard 90.1 is the model energy standard for commercial and multi-family residential buildings over three floors (42 USC 6833), many states have historically adopted the International Energy Conservation Code (IECC) for both residential and commercial buildings. This report provides an assessment as to whether buildings constructed to the commercial energy efficiency provisions of the 2015 IECC would save energy and energy costs as compared to the 2012 IECC. PNNL also compared the energy performance of the 2015 IECC with the corresponding Standard 90.1-2013. The goal of this analysis is to help states and local jurisdictions make informed decisions regarding model code adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Solar-Reflective “Cool” Walls: Benefits, Technologies, and Implementation

Raising the albedo (solar reflectance) of a building’s walls reduces unwanted solar heat gain in the cooling season. This saves electricity and lowers peak power demand by decreasing the need for air conditioning. It can also cool the outside air, which can mitigate the urban heat island effect and also improve air quality by slowing the reactions that produce smog. This project quantified the energy savings, peak demand reduction, urban cooling, and air quality improvements attainable from solar-reflective “cool” walls in California; collaborated with industry to assess the performance of existing cool-wall technologies, and to develop innovative cool-wall solutions; and worked with state and federal government agencies, utilities, and industry to create a cool-wall infrastructure, including application guidelines, a product rating program, incentives, and building code credits. Simulations indicate that cool walls provide annual energy savings, peak demand reduction, annual emission reduction, and summer heat island mitigation benefits comparable to those yielded by cool roofs, and are helpful across California and in most of the southern half of the United States (that is, in U.S. climate zones 1—4). Natural exposure trials conducted at three sites in California and another three sites across the United States indicate that cool-wall materials tend to stay clean and reflective. Significant advances were made in novel cool-wall technologies, such as fluorescent cool pigments that expand the color palette for cool-wall products. We prepared guidelines for the climate- and building-appropriate use of cool walls, convened a stakeholder workshop, and created a working group. Ongoing efforts seek to introduce or expand cool-wall provisions in building energy standards, green building programs, and energy efficiency incentive programs, and to develop a cool-wall product rating system.

utility incentives↗
DOE OSTI2020

Multizone Residential HVAC Modeling

This report will evaluate several building physics modeling tools to determine which are best suited for developing next-generation zoning technology. These modeling tools will be evaluated for their ability to predict room-level thermal loads, HVAC system operation, temperature and comfort predictions, and scalability. Any gaps in the existing tools will be identified and a path forward will be discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Energy Transitions Initiative Partnership Project: Wainwright, Alaska. Cohort 1 Technical Assistance: Assessment of Energy Efficiency, Renewable Energy, and Energy Storage Options

This project identified pathways to reduce energy consumption and enhance indoor environmental quality and resilience of a 1,500-square-foot former federal armory building owned by the tribe as it is renovated as a community multipurpose facility. The scope included thermal evaluation of multiple building efficiency measures, and a techno-economic exploration of an on-site renewable energy and integrated energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Lab Evaluation of Downward Capacity of Radiant Ceiling Panel Systems

This project investigated the cooling delivery effectiveness of radiant ceiling panels as a function of attic insulation level using multiple laboratory testing and analytical methodologies. Delivery effectiveness is the heating or cooling energy delivered to a conditioned space divided by the total heating or cooling energy added or removed by the space conditioning system. The lower the losses of the heating or cooling delivery method, the higher the delivery effectiveness. For ducted systems, delivery effectiveness is reduced by both air leakage and thermal losses (especially if the ducts are installed in attics), while the delivery effectiveness of a radiant system supplied by hot and cold water is only reduced by thermal losses, which can be mitigated by sufficient insulation above, or at the "back" of the panel. Being installed at or below the ceiling plane, sufficient back insulation should be provided by default in the form of the attic insulation above the radiant ceiling panels. Site-built radiant ceiling panels were evaluated at Frontier Energy’s Building Science Research Laboratory (BSRL) in a uniquely designed environmental test chamber with independently controllable indoor and attic spaces and a height-adjustable ceiling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Figures and scripts from Taylor, et al's "A multi-scale calibration approach for process-oriented aggregated building energy demand models"

This dataset includes all figures that were generated in the course of writing Taylor, et al's "A multi-scale calibration approach for process-oriented aggregated building energy demand models." It includes the figures and tables from the paper, a large number of additional figures that were not included in the paper, and the underlying data and R scripts that were used to generate the figures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Data Sets for Evaluation of Building Fault Detection and Diagnostics Algorithms

This documentation and dataset can be used to test the performance of automated fault detection and diagnostics algorithms for buildings. The dataset was created by LBNL, PNNL, NREL, ORNL and ASHRAE RP-1312 (Drexel University). It includes data for air-handling units and rooftop units simulated with PNNL's large office building model.

AHU↗
DOE OSTI2019

Curated Modeled Fault Data Set

The curated fault model simulation data set consists of tagged and fully-described time series representing simulated faults for the AFDD test building (ORNLs Flexible Research Platform (FRP)), including baseline performance, faulty performance, and corresponding energy impact. A total of 26 fault models are considered for 99 simulation scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Simulation performer: Janghyun Kim Janghyun.Kim@nrel.gov

AFDD↗
DOE OSTI2019

Curated Test Fault Data Set

The curated fault experiment data set consists of tagged and fully described time series representing measured faults from the AFDD test building (ORNLs Flexible Research Platform [FRP]), including baseline performance and faulty performance. A total of 10 different faults are tested for 49 different faulted and unfaulted scenarios with various fault intensity levels. Additional Contacts: Principal investigator: Matt Leach Matt.Leach@nrel.gov Experiments coordinator: Piljae Im imp1@ornl.gov Document preparation: Janghyun Kim Janghyun.Kim@nrel.gov

AFDD↗
DOE OSTI2023

Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A Simplified Methodology to Estimate Energy Savings in Commercial Buildings from Improvements in Airtightness

Air leakage through the envelope of commercial buildings in the United States accounts for approximately 6% of their energy use. Various simulation approaches have been proposed to estimate the impact of air leakage on building energy use. Although approaches that are based on detailed airflow modeling appear to be the most accurate to calculate infiltration heat transfer in simulation models, these approaches tend to require significant modeling expertise and effort. To make these energy savings estimates more readily available to building owners and designers, Oak Ridge National Laboratory, the National Institute of Standards and Technology, the Air Barrier Association of America, and the US Department of Energy (DOE) are developing a user-friendly online calculator that applies a detailed airflow modeling approach to examine energy savings due to airtightness in commercial buildings. The calculator, however, is limited to 52 US cities and a few cities in Canada and China. This paper describes the development of an alternative, simplified method to estimate energy savings from improved airtightness. The proposed method uses the same detailed approach for hourly infiltration calculations as the online calculator but it expands the ability to estimate energy savings to all US cities using hourly outdoor air temperature as the only input. The new simple regression model-based approach was developed and tested with DOE’s standalone retail prototype building model. Results from the new approach and the calculator show good agreement. In addition, a simple approach to estimate percent energy savings for retrofitted buildings was also developed; results were within 5% of the energy saving estimates from the online calculator.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Comprehensive Analysis of Energy and Daylighting Impact of Window Shading Systems and Control Strategies on Commercial Buildings in the United States

Commercial buildings consume approximately 1.9 EJ of energy in the United States, 50% of which is for heating, cooling, and lighting applications. It is estimated that windows contribute up to 34% of the energy used for heating and cooling. However, window retrofits are not often undertaken to increase energy efficiency because of the high cost and disruptive nature of window installation. Highly efficient window technologies would also need shading devices for glare prevention and visual comfort. An automated window shading system with an appropriate control strategy is a technology that can reduce energy demand, maintain occupant comfort, and enhance the aesthetics and privacy of the built environment. However, the benefits of the automated shades currently used by the shading industry are not well studied. The topic merits an analysis that will help building owners, designers and engineers, and utilities make informed decisions using knowledge of the impact of this technology on energy consumption, peak demand, daylighting, and occupant comfort. This study uses integrated daylight and whole-building energy simulation to evaluate the performance of various control strategies that the shading industry uses in commercial office buildings. The analysis was performed for three different vintages of medium office buildings at six different locations in United States. The results obtained show the control strategies enabled cooling energy savings of up to 40% using exterior shading, and lighting energy savings of up to 25%. The control strategies described can help building engineers and researchers explore different control methods used to control shading in actual buildings but rarely discussed in the literature. This information will give researchers the opportunity to investigate potential improvements in current technologies and their performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Quality Control Methods for Advanced Metering Infrastructure Data

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗