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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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Case study of a DOE 2021 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 34 with PV, with 1,872 square feet, ENERGY STAR appliances and lighting, high-efficiency HVAC, along with 1.9-kW of photovoltaic panels.
Case study of a DOE 2021 Housing Innovation Award winning custom home in a marine climate that got a HERS -13 with PV, with 1,804 square feet, highly efficient construction; high-efficiency ductless minisplit heat pumps, appliances, and lighting; triple-pane windows; and 9.9 kW of solar panels on each of the monoplane roofs.
Case study of a DOE 2021 Housing Innovation Award winning custom home in a cold climate that got a HERS 5 with PV, with 3,934 square feet, ENERGY STAR appliances and windows, LED lighting, and a heat pump water heater.
Case study of a DOE 2021 Housing Innovation Award winning custom home in a cold climate that got a HERS 33 without PV, with 1,936 square feet, gable roof with asphalt shingles, attic insulated with 14 inches (R-49) of blown cellulose, and triple-pane windows.
Case study showing project and performance data and energy-efficiency measures of a winning entry in the Housing Innovation Awards of DOE's Building Technologies Office.
Case study showing project and performance data and energy-efficiency measures of a winning entry in the Housing Innovation Awards of DOE's Building Technologies Office.
Case study showing project and performance data and energy-efficiency measures of a winning entry in the Housing Innovation Awards of DOE's Building Technologies Office.
Case study showing project and performance data and energy-efficiency measures of a winning entry in the Housing Innovation Awards of DOE's Building Technologies Office.
Case study showing project and performance data and energy-efficiency measures of a winning entry in the Housing Innovation Awards of DOE's Building Technologies Office.
ComStock™ is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.
ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.
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This dataset provides 30 m resolution spatial layers of building fraction and mean building height for the Los Angeles metropolitan region for 2010 and 2020. Derived from the Model America version 2 building dataset, it represents the distribution of total building footprint area and average building height across the metropolitan region, offering a consistent geospatial resource for urban analysis. The dataset is intended to support applications in urban climate studies, land-use assessment, city-scale modeling, and planning by enabling comparison of urban form characteristics over time. Additional dataset details including data processing details are provided in the Readme document (README_LA_BF_MeanHeight_2010_2020 1.txt).
This work explores the Urban Heat Island (UHI) effects in Maricopa County, Arizona, employing a simulation-based approach that combines large-scale building energy modeling with advanced spatial analysis. Utilizing the Automatic Building Energy Modeling (AutoBEM) software suite, we simulated the energy consumption for approximately 1.35 million buildings based on the Model America version 1.0 (MAv1) dataset. Our methodology incorporated spatial analysis at multiple scales, including individual buildings, clusters of zones determined by K-means clustering, and geographical level evaluation based on Zip codes. The results revealed significant variations in energy consumption and heat emissions across different building types and urban zones. High-emission hotspots identified through clustering pointed to areas most contributing to the UHI effects. Zip code-based area analysis further contextualized these findings, offering an urban context-based perspective on emission distribution and informing potential urban energy policies for mitigating UHI effects.
In October and November 2020, two dangerous back-to-back hurricanes, Eta and Iota, made landfall in Central America. The SERVIR program - a joint effort of NASA and the U.S. Agency for International Development, and whose motto is “connecting space to village” was able to leverage two tools originally developed for use in other regions for predicting and assessing the flood impacts of the hurricanes. The GEOGLoWS Streamflow Prediction tool - originally implemented in the Hindu Kush Himalayan region - was used for predicting potential flooding ahead of landfall by Eta and Iota. In conjunction, the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) framework - originally developed along with SERVIR-Mekong - was used for post-event flood mapping, leveraging its ability to map floods in cloud-covered areas using synthetic aperture radar (SAR) imagery from the Copernicus program. Both tools were used in support of disaster coordination efforts being led by the Central American Regional Disaster Prevention Center (CEPREDENAC), in conjunction with its sister agency, the Regional Water Resources Committee (CRRH). The support provided to regional entities - and to their stakeholder national governments - served as an example of rapid generation of Earth observation products for disaster response. Feedback on those products was also provided, especially in terms of the implications of (i) calibration of predicted river volumes, and (ii) the latency of the input Earth observation imagery and attempts to map the floods’ maximum extents. An upcoming NASA DEVELOP project will also seek to strengthen the capability of CEPREDENAC and CRRH to apply HYDRAFloods for future extreme events. The application of the tools also provides a useful case study on capacity building, in terms of how Earth observation data and models can be replicated across regions.
This study evaluates the long-term effectiveness of energy conservation measures (ECMs) on building energy consumption using AutoBEM, a scalable modeling framework driven by the high-resolution Model America dataset. We simulated 18,951 buildings in Flagstaff, Arizona under four climate scenarios using Future Typical Meteorological Year (fTMY) weather files for six time periods spanning from 1980 to 2099. Six ECMs were analyzed across electricity and gas usage, including HVAC fuel-switching, insulation upgrades, and infiltration control. While some measures, such as reducing space infiltration by percentage, showed minimal or even negative impact on total energy savings at the aggregate level, they proved highly effective for specific building types. Conversely, HVAC electrification offers high gas reduction but shifts demand to electricity, highlighting critical trade-offs under different climate trajectories. Building-type-specific analysis under SSP5-RCP8.5 (2080–2099) revealed significant variation in ECM performance, underscoring the need for targeted retrofit strategies. This study demonstrates the power of combining fTMY projections with large-scale simulations to inform data-driven retrofit planning.
Over time, miscellaneous electric loads (MELs) are expected to increase both in magnitude and share of residential and commercial building energy consumption. This trend is most apparent in North America, but it is also occurring in Japan and Europe. However, the contribution of MELs to building energy use is not currently well understood, both because the products in this category are transforming rapidly and the definition and classification of MELs is ambiguous. This study estimated the national energy consumption of 36 MELs using best-available data and found them to comprise 12% of delivered electricity to the U.S. residential and commercial building sectors. If 26 of these MELs were replaced with the most energy-efficient product models available on the market, their energy consumption could be halved to 6% of delivered electricity. National energy models will better account for building energy consumption by incorporating the MELs data collected and analyzed for this study, leading to improved policy decisions.
A wide variety of remote sensing instruments have been utilized to attempt to detect archaeological features under volcanic ash in Central America. Some techniques have not been successful, such as seismic refraction, for reasons that are not difficult to understand. Others have been very successful and provide optimism for archaeologists witnessing the destruction of unburied sites throughout Central America. The sudden burial of buildings, gardens, and footpaths by volcanic ash can preserve them extremely well providing a rich data base for understanding human life and culture at certain points in time.