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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Black carbon emissions from biomass and coal in rural China

Residential solid fuel combustion makes a major contribution to black carbon (BC) emissions in China. A new estimation of BC emissions from rural solid biomass and coal consumption has been derived from field survey data. The following new contributions are made: (1) emission factors are collected and reviewed; (2) household energy data are collected from field survey data and from the literature; (3) a new extrapolation method is developed to extend the field survey data to other locations; (4) the ownership and usage of two stove types are estimated and considered in the emission calculations; and (5) uncertainties associated with the estimation results are quantified. It is shown that rural households with higher income will consume less biomass but more coal. Agricultural acreage and temperature also significantly influence the amount of solid fuel consumed in rural areas. It is estimated that 640±245 Gg BC/y were emitted to the atmosphere due to residential solid fuel consumption in rural China in 2014. Emissions of BC from straw, wood, and coal contributed 42±13%, 36±15%, and 22±10% of the total, respectively. As a result, we show that effective BC mitigation (a reduction of 47%) could be obtained through widespread introduction of improved stoves in rural households

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate

Urban buildings account for up to 75% of total energy use in the United States (U.S.). Understanding urban building energy use is important for developing feasible options to mitigate energy use and greenhouse gas emissions. In this study, an improved bottom-up building energy use model, named City Building Energy Use Model (City-BEUM), was developed to estimate building energy use for all buildings in Polk County, Iowa. First, 28 commercial and 6 residential building prototypes were designed by combing Assessor's parcel data and building footprint data. Then, the EnergyPlus in the CityBEUM was calibrated for all building prototypes using the U.S. Energy Information Administration's survey data, monthly utility meter data, and actual weather data. Finally, spatial and temporal variations of building energy use in the study area were estimated using the CityBEUM. Results indicate that the spatial variation of building energy use in the study area can be captured using the CityBEUM. With the monthly-calibrated model, the temporal pattern of urban building energy use can be well represented. The comparison of building energy use using the Typical Meteorological Year and actual weather data demonstrates the importance of using actual weather data in building energy modeling for an improved temporal representation.

CityBEUM, Urban building energy use, High spatiote↗
DOE OSTI2019

Sensitivity of occupant comfort models to humidity and their effect on cooling energy use

Residential building energy consumption in the United States has decreased steadily during the last several decades largely because of advances in building codes as well as voluntary efficiency and labeling programs, which have resulted in the reduction in building sensible loads. Building latent loads have not decreased by the same amount, and field and analytical research studies have concluded that high-efficiency, low-load homes often have elevated indoor humidity levels, possibly leading to occupant discomfort. There are discrepancies in the literature on occupant comfort as it relates to indoor humidity and recommended upper humidity limits. The choice of humidity limit could potentially change results and conclusions on the energy impacts of a new air conditioner or dehumidifier technology, changing ventilation rates, or an improved building envelope. In this study, we use building simulations to explore the impact of humidity control on cooling energy use in efficient residential buildings for different humidity limits. We look at limits based on humidity ratio (or dew point temperature), wet-bulb temperature, relative humidity, and constant slope lines based on the popular comfort models of both Fanger and Gagge. We quantify the additional energy use required to control humidity below these limits. We look at the sensitivity of the results to different temperature and humidity set points, occupant internal gains, moisture buffering levels, evaporator airflow rates, and dehumidifier energy factors. High-efficiency home predicted energy savings are more sensitive to the assumed humidity limit and cooling and dehumidification set points than the other parameters investigated in this paper.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A simulation-based assessment of technologies to reduce heat emissions from buildings

Heat emissions from buildings are part of anthropogenic heat leading to urban overheating. This paper aims to assess how technologies (i.e., energy conservation measures - ECMs), used to decrease energy use, may also reduce heat emissions from buildings. This study employs the physics-based engine EnergyPlus to simulate the main components of heat emissions from buildings to ambient air: envelope, zone, and systems. Hourly simulations are run for IECC single- and multi-family reference models with three representative climates: Miami, Baltimore, and Chicago. The results show that the performance of ECMs varies among weather, seasons, and residential typologies. Particularly, some ECMs (i.e., cool coatings, heat pumps, additional insulation, energy-awareness occupants) show a strong decrease in heat emissions, yet they are not always correlated with proportional decreases in energy use. When all ECMs are combined, the reductions are larger on heat emissions (89%) than on site energy (65%) from the base cases. During summer in Miami, the combination of ECMs shows a decrease in heat emissions from the building surface component of 80% during daytime, 92% for the HVAC component and a counterbalanced increase in the zone component of 88%, bringing to a daily decrease in total heat emissions. The main contributions of this study are quantifying how typical ECMs influence residential building heat emissions using EnergyPlus simulations and informing urban planners and stakeholders on prioritizing measures for mitigating urban overheating problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Evaluation of energy savings potential of variable refrigerant flow (VRF) from variable air volume (VAV) in the U.S. climate locations

Variable refrigerant flow (VRF) systems are known for their high energy performance and thus can improve energy efficiency both in residential and commercial buildings. The energy savings potential of this system has been demonstrated in several studies by comparing the system performance with conventional HVAC systems such as rooftop variable air volume systems (RTU-VAV) and central chiller and boiler systems. This paper evaluates the performance of VRF and RTU-VAV systems in a simulation environment using widely-accepted whole building energy modeling software, EnergyPlus. A medium office prototype building model, developed by the U.S. Department of Energy (DOE), is used to assess the performance of VRF and RTU-VAV systems. Each system is placed in 16 different locations, representing all U.S. climate zones, to evaluate the performance variations. Both models are compliant with the minimum energy code requirements prescribed in ASHRAE standard 90.1-2010 — energy standard for buildings except low-rise residential buildings. Finally, a comparison study between the simulation results of VRF and RTU-VAV models is made to demonstrate energy savings potential of VRF systems. The simulation results show that the VRF systems would save around 15–42% and 18–33% for HVAC site and source energy uses compared to the RTU-VAV systems. In addition, calculated results for annual HVAC cost savings point out that hot and mild climates show higher percentage cost savings for the VRF systems than cold climates mainly due to the differences in electricity and gas use for heating sources.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Health impact assessment of air pollution using a dynamic exposure profile: Implications for exposure and health impact estimates

In both ambient air pollution epidemiology and health impact assessment an accurate assessment of the population exposure is crucial. Although considerable advances have been made in assessing human exposure outdoors, the assessments often do not consider the impact of individual travel behavior on such exposures. Population-based exposures to NO{sub 2} and O{sub 3} using only home addresses were compared with models that integrate all time-activity patterns-including time in commute-for Flanders and Brussels. The exposure estimates were used to estimate the air pollution impact on years of life lost due to respiratory mortality. Health impact of NO{sub 2} using an exposure that integrates time-activity information was on average 1.2% higher than when assuming that people are always at their home address. For ozone the overall estimated health impact was 0.8% lower. Local differences could be much larger, with estimates that differ up to 12% from the exposure using residential addresses only. Depending on age and gender, deviations from the population average were seen. Our results showed modest differences on a regional level. At the local level, however, time-activity patterns indicated larger differences in exposure and health impact estimates, mainly for people living in more rural areas. These results suggest that for local analyses the dynamic approach can contribute to an improved assessment of the health impact of various types of pollution and to the understanding of exposure differences between population groups. - Highlights: Black-Right-Pointing-Pointer Exposure to ambient air pollution was assessed integrating population mobility. Black-Right-Pointing-Pointer This dynamic exposure was integrated into a health impact assessment. Black-Right-Pointing-Pointer Differences between the dynamic and residential exposure were quantified. Black-Right-Pointing-Pointer Modest differences in health impact were found at a regional level. Black-Right-Pointing-Pointer At municipal level larger differences were found, influenced by gender and age.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Unanticipated potential cancer risk near metal recycling facilities

Metal recycling is an important growing industry. Prior to this study, area sources consisting of metal recycling facilities fell in a category of limited regulatory scrutiny because of assumed low levels of annual emissions. Initiating with community complaints of nuisance from smoke, dust and odor, the Houston Department of Health and Human Services (HDHHS) began a monitoring program outside metal recycler facilities and found metal particulates in outdoor ambient air at levels which could pose a carcinogenic human health risk. In a study of five similar metal recycler facilities which used a torch cutting process, air downwind and outside the facility was sampled for eight hours between 6 and 10 times each over 18 months using a mobile laboratory. Ten background locations were also sampled. Iron, manganese, copper, chromium, nickel, lead, cobalt, cadmium and mercury were detected downwind of the metal recyclers at frequencies ranging from 100% of the time for iron to 2% of the time for mercury. Of these metals, chromium, nickel, lead, cobalt, cadmium and mercury were not detected in any sample in the background. Two pairs of samples were analyzed for total chromium and hexavalent chromium to establish a ratio of the fraction of hexavalent chromium in total chromium. This fraction was used to estimate hexavalent chromium at all locations. The carcinogenic risk posed to a residential receptor from metal particulate matter concentrations in the ambient air attributed to the metal recyclers was estimated from each of the five facilities in an effort to rank the importance of this source and inform the need for further investigation. The total risk from these area sources ranged from an increased cancer risk of 1 in 1,000,000 to 6 in 10,000 using the 95th upper confidence limit of the mean of the carcinogenic metal particulate matter concentration, assuming the point of the exposure is the sample location for a residential receptor after accounting for wind direction and the number of shifts that could operate a year. Further study is warranted to better understand the metal air pollution levels in the community and if necessary, to evaluate the feasibility of emission controls and identify operational improvements and best management practices for this industry. This research adds two new aspects to the literature: identification of types and magnitude of metal particulate matter air pollutants associated with a previously unrecognized area source, metal recyclers and their potential risk to health. -- Highlights: • Air monitoring study in response to community complaints found metal contamination. • Metal recyclers found to potentially pose cancer from metal particulates • Chromium, nickel, cobalt and cadmium samples were detected in five metal recyclers. • These metals were not detected in background air samples. • Estimated increased cancer risk ranges from 1 in 1,000,000 to 8 in 10,000.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A program-level management system for the life cycle environmental and economic assessment of complex building projects

Climate change has become one of the most significant environmental issues, of which about 40% come from the building sector. In particular, complex building projects with various functions have increased, which should be managed from a program-level perspective. Therefore, this study aimed to develop a program-level management system for the life-cycle environmental and economic assessment of complex building projects. The developed system consists of three parts: (i) input part: database server and input data; (ii) analysis part: life cycle assessment and life cycle cost; and (iii) result part: microscopic analysis and macroscopic analysis. To analyze the applicability of the developed system, this study selected ‘U’ University, a complex building project consisting of research facility and residential facility. Through value engineering with experts, a total of 137 design alternatives were established. Based on these alternatives, the macroscopic analysis results were as follows: (i) at the program-level, the life-cycle environmental and economic cost in ‘U’ University were reduced by 6.22% and 2.11%, respectively; (ii) at the project-level, the life-cycle environmental and economic cost in research facility were reduced 6.01% and 1.87%, respectively; and those in residential facility, 12.01% and 3.83%, respective; and (iii) for the mechanical work at the work-type-level, the initial cost was increased 2.9%; but the operation and maintenance phase was reduced by 20.0%. As a result, the developed system can allow the facility managers to establish the operation and maintenance strategies for the environmental and economic aspects from a program-level perspective. - Highlights: • A program-level management system for complex building projects was developed. • Life-cycle environmental and economic assessment can be conducted using the system. • The design alternatives can be analyzed from the microscopic perspective. • The system can be used to establish the optimal O&M strategy at the program-level. • It can be applied to any other country or sector in the global environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Developing micro-level urban ecosystem indicators for sustainability assessment

Sustainability assessment is increasingly being viewed as an important tool to aid in the shift towards sustainable urban ecosystems. An urban ecosystem is a dynamic system and requires regular monitoring and assessment through a set of relevant indicators. An indicator is a parameter which provides information about the state of the environment by producing a quantitative value. Indicator-based sustainability assessment needs to be considered on all spatial scales to provide efficient information of urban ecosystem sustainability. The detailed data is necessary to assess environmental change in urban ecosystems at local scale and easily transfer this information to the national and global scales. This paper proposes a set of key micro-level urban ecosystem indicators for monitoring the sustainability of residential developments. The proposed indicator framework measures the sustainability performance of urban ecosystem in 3 main categories including: natural environment, built environment, and socio-economic environment which are made up of 9 sub-categories, consisting of 23 indicators. This paper also describes theoretical foundations for the selection of each indicator with reference to the literature [Turkish] Highlights: • As the impacts of environmental problems have multi-scale characteristics, sustainability assessment needs to be considered on all scales. • The detailed data is necessary to assess local environmental change in urban ecosystems to provide insights into the national and global scales. • This paper proposes a set of key micro-level urban ecosystem indicators for monitoring the sustainability of residential developments. • This paper also describes theoretical foundations for the selection of each indicator with reference to the literature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗
DOE OSTI2019

Incorporating machine learning with building network analysis to predict multi-building energy use

Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Evaluation of hybrid evaporative-vapor compression air conditioners for different global climates

Currently, a majority of global residential air-conditioning requirements are met using cooling systems based on the vapor compression cycle (VCC). To meet the future demand for space cooling and to reduce the corresponding environmental impact, there is a need for alternative cooling systems which require less resources and exhibit a higher coefficient of performance (COP). One of proposed alternatives is called the hybrid evaporative-vapor compression (HEVC) cycle, which utilizes adiabatic latent cooling in combination with the VCC. In this paper, we conducted a feasibility analysis of a HEVC system by comparing its performance to that of VCC for various global climates. To accomplish this, we developed and validated models to simulate the performance of residential VCC and HEVC systems. ASHRAE weather data and design conditions were then used to compare the performance of the two systems. In general, HEVC systems are best suited for hot arid climates with energy reduction greater than 20%. Conversely, humid climates are not suitable for HEVC adoption due to degraded performance of evaporative coolers in these climates. In addition to comparing the energy savings from HEVC systems, it is also critical to analyze their water consumption. Generally, climates that benefit the most from HEVC technology tend to also experience water scarcity issues. Thus, both the energy savings and water consumption of HEVC systems must be analyzed to guide the discussion on their adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Scenarios of Building Energy Demand for China with a Detailed Regional Representation

Building energy consumption currently accounts for 28% of China’s total energy use and is expected to continue to grow induced by floorspace expansion, income growth, and population change. Fuel sources and building services are also evolving over time as well as across regions and building types. To understand sectoral and regional difference in building energy use and how socioeconomic, physical, and technological development influence the evolution of the Chinese building sector, this study developed a building energy use model for China downscaled into four climate regions under an integrated assessment framework. Three building types (rural residential, urban residential, and commercial) were modeled specifically in each climate region. Our study finds that the Cold and Hot Summer Cold Winter regions lead in total building energy use. The impact of climate change on heating energy use is more significant than that of cooling energy use in most climate regions. Both rural and urban households will experience fuel switch from fossil fuel to cleaner fuels. Commercial buildings will experience rapid growth in electrification and energy intensity. Improved understanding of Chinese buildings with climate change highlighted in this study will help policy makers develop targeted policies and prioritize building energy efficiency measures.

building energy use↗
DOE OSTI2021

Ultra-clean condensing gas furnace enabled with acidic gas reduction

Natural gas furnaces are the most common space heating equipment in the U.S. residential and commercial building markets. However, current residential natural gas condensing furnaces generate substantial acidic condensate as well as significant emissions of sulfur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and methane (CH 4 ) contributing to environmental degradation of air, water, and soil. This paper describes a novel solution to reduce the environmental impact of natural gas condensing furnaces based on the technology of monolithic acidic gas reduction (AGR) catalyst for SOx trapping, NOx redox to nitrogen, and oxidation of formic acid, CO, HC, and CH 4 . This technology offers a new condensing natural gas furnace with both ultra-clean flue gas and neutral condensate. Here, a prototype of the condensing gas furnace with the AGR component is demonstrated to have condensate with pH = 7, NOx emissions of 1–2 ng/J, and an annual fuel utilization efficiency (AFUE) of 96%. The AGR component and the AGR-enabled furnace were tested for long-term reliability and durability, as well as for SOx storage and regeneration activity. In addition, this paper provides new data on measurements of the specific acidic gas content in natural gas condensing furnaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗