Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “household”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types

This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Children, Income, and the Impact of Home Delivery on Household Shopping Trips

Expanding e-commerce and delivery benefit consumers through increased flexibility and convenience. However, there is a potential impact on vehicle miles traveled (VMT) by delivery and personal vehicles, and the resulting energy consumption, air quality, and congestion. Delivery trips could replace personal vehicle trips, but, if not a replacement, could add to (or supplement) shopping-related VMT for a given household. Here, we examined the benefits of e-commerce to consumers and the impact on personal shopping trips, and how these differed across item types, household child status, and income. We found that high-income households and households with children cared relatively more about time saving from deliveries. We found that, on average, deliveries substituted for 12% of vehicle shopping trips, but supplemented personal shopping trips for 9% of purchase deliveries. Underlying these averages were two main types of households: those for whom all deliveries substitute for trips (between 55% and 70% of households) and those for whom all deliveries supplement trips (between 20% and 35% of households). There was significant heterogeneity across households with and without children and with high or low incomes with respect to the use of delivery. Although time savings were more likely to motivate higher-income households and households with children to use delivery, this did not translate through to these households substituting for more of their trips; deliveries of prepared meals for both these categories of household were relatively more likely (15% for households with children, and 12% for higher-income households) to supplement, and not substitute for, personal trips.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

2005 Tahoe Regional Household Travel Survey

The 2005 Tahoe Regional Household Travel Survey is a comprehensive survey of the travel patterns of households in the Tahoe Basin region of California and Nevada. The survey was conducted for the Tahoe Regional Planning Agency over a four-month period, from June through September 2005. This collection contains two sets of travel data: 1) a survey of 1,220 permanent households within the Tahoe Basin region and 2) a subset survey of 125 seasonal households within the same area. The permanent resident dataset contains information about 2,732 household members, 2,471 vehicles, and 11,048 trips made during a 24-hour period. The seasonal resident subset features 726 trip observations and information about 229 household members and 185 vehicles. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles and 2) document all travel for each household member, regardless of age, over an assigned 24-hour period. During the recruitment phase, respondents were asked to provide detailed household information, including permanent or seasonal resident status, number of household occupants, type of dwelling unit, number of vehicles available to household members, as well as the year, body type, and fuel type of each vehicle. Survey participants received mailed travel logs to aid in documenting travel information for the 24-hour assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of passengers, departure and arrival times, activities at each trip destination, ride fare, and parking costs. Demographic information for this study includes age, gender, income, education, and employment status.

1Hz data↗
DOE OSTI2026

Investigating the Determinants of Household Capabilities Burden During Power Outages: The Case of Winter Storm Uri

Existing research primarily uses census data to identify the vulnerability of communities to hazards. These vulnerability indices provide aggregated data and are not hazard-specific nor well-validated with post-event data. In contrast, our study uses household survey data (n=1065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, and the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden' and ‘heavier burden' households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have larger households with more young children, fewer adults over 65, lower household incomes, been impacted by the COVID-19 pandemic, and more family characteristics that made life harder. We also fit a logistic regression model to assess the role of outage, household, and community characteristics in predicting differences in capabilities burden. Our results offer insights into enumerating the consequences of utility service disruptions on households, which can inform more targeted and equitable resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Household Transportation Energy Affordability by Region and Socioeconomic Factors

Transportation fuel is an important component of household budgets, as 3.3% of total household expenditures are for vehicle fuel nationwide and over 50% of annual household expenditures on energy are for transportation. These average values vary geographically, and higher energy cost burdens are faced by households with lower incomes. Defining transportation energy affordability burden as the percentage of annual household income spent on vehicle fuel, this study aims to quantify affordability as a function of household characteristics and geography. Here, through analysis at the census tract level, this study (i) projects annual household vehicle miles traveled (VMT) based on demographic factors using machine-learning techniques, (ii) estimates local differences in vehicle fuel economy and fuel price, and (iii) quantifies resulting transportation energy affordability by census tract. This study found that the average burden by tract varies from 0.15% to 8%. The variation in affordability can be largely explained by income level and vehicle fuel efficiency. Suburban and rural households spend more on transportation energy compared with urban households because of the usage of less fuel-efficient vehicle technologies and higher annual VMT. Lower-income groups have a wide distribution of the percentage of income spent on transportation energy, 1.2% to 8%, whereas the range for the highest income group ($125,000+) is from 0.15% to 3.9%. This detailed transportation energy affordability analysis provides a better understanding of regional variations in household travel behavior, helps to determine where fuel-efficient vehicle technologies are more likely to be used, and improves estimates of vehicle ownership costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Why is it still too warm or cold in my house? Examining the relationships between energy efficient capital and household energy insecurity

Here, this paper examines the relationships between energy efficient (EE) capital technology and household energy insecurity in the United States. The theoretical model of these relationships employs household production theory to capture the demand for and production of household energy services, and a stochastic production frontier approach to describe how having access to and the usage intensity of EE capital technology could help alleviate inefficiency in the production of household energy services. A working hypothesis formulated from the theoretical model posits that having EE capital technology in the home will reduce the level of household energy insecurity experienced. The extent of energy insecurity experienced is inferred from an energy insecurity index value assigned to each household, generated via the application of a dichotomous Rasch model to questions contained in the 2015 Residential Energy Consumption Survey. Noting the potential simultaneous relationship that exists between a household having access to and the usage intensity of EE capital technology and the experience of being energy insecure, an instrumental variables approach was employed to estimate a series of ordered logit models. Results suggest access to EE capital technology in the form of Energy Star® appliances, Energy Star® windows, or a SMART thermostat does not reduce the probability of experiencing a greater level of energy insecurity. Nor does the usage intensity of EE capital. Thus, policy instruments designed to alleviate household energy insecurity may need to go beyond simply helping households obtain EE capital technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Tracking the PACE of household energy usage: Energy usage impacts of projects financed through Property Assessed Clean Energy programs in California

This report examines the household-level energy impacts of residential property assessed clean energy (R-PACE) projects using normalized metered energy consumption methods. Our analysis covers projects that occurred through an R-PACE program between 2009 and 2017 and includes more than 25,000 electricity meters and 15,000 gas meters. We employ a comparison group, drawn from other R-PACE households with similar locational and usage characteristics whose projects were implemented at different times, to control for some non-project and non-weather factors that may impact energy use. We find that projects consisting of energy efficiency technologies save, on average, about 3% of household electricity usage and 3.5% of household gas usage. R-PACE financing, however, can be used to install central heating or air conditioning equipment for the first time. These projects would be expected to increase energy consumption. Since our data do not directly indicate which projects are new installations, we develop a simple algorithm for identifying them. Removing these inferred installation projects yields average savings of about 5% for electricity and 6% for gas for those households that remain in the sample. Given the mild California climate and the results of another study of similar California projects using similar methods, these results are in line with expectations. Solar PV projects yield large reductions in grid electricity use, averaging 69% of household consumption. We estimate that, collectively, all R-PACE projects installed in California through 2019 would generate annual reductions in grid-tied electricity consumption of 506 GWh (mostly due to solar PV) and gas consumption reductions of 2 million therms in a normal weather year. These impacts are equivalent to the electricity consumption of about 74,000 California households (including both efficiency and PV generation) and the gas consumption of about 4700 California households.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Dataset for: Rooftop solar and energy storage programs can remediate energy-limiting behaviors of energy insecure households

Energy insecurity affects most low-income households in the United States. Energy insecurity, which is characterized by a household’s inability to afford their energy needs, often leads to risky choices, causing other forms of insecurity including food and health. Although there are government programs designed to provide relief to low-income households that face energy insecurity, eligibility for these programs is usually determined by household income, and those with incomes close to the threshold face uncertainty or may be left out. In many cases, these households turn to energy-limiting behaviors as a strategy to lower their electric utility bills. This paper explores the relationship between energy insecurity and energy-limiting behaviors to investigate alternative solutions that target the households that may fall out of available energy assistance programs. We explore the role of battery energy storage systems and rooftop solar photovoltaics in improving energy affordability. The results show that residential rooftop solar and behind-the-meter energy storage can offset two-thirds of the bill savings that households attain through energy-limiting behavior. Household renewable energy systems could complement existing energy assistance programs to provide long-term bill relief, enabling occupants to live in their homes with comfort and dignity. This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit.

Kerby, Jessica [Pacific Northwest National Laborat↗
DOE OSTI2024

The Triple Effect: Unraveling the Joint Impact of Electric Vehicles, Solar Panels, and Work-From-Home Patterns on Household Electricity Costs

Solutions for deep decarbonization need to be sustainable as well as affordable to garner widespread adoption. The past two decades have seen the introduction of new technologies and paradigms such as electric vehicles (EVs), solar photovoltaics (PVs), and increased work-from-home (WFH), which impact the overall energy consumption and cost of a household in different ways. However, much attention has not been paid to explore the joint impact these technologies have on a household's energy (electricity cost) burden. Leveraging Residential Energy Consumption Survey (RECS), this study presents an integrated model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. RECS data revealed that, compared to the baseline (i.e., households that did not own any of these technologies), households that own EVs, PVs, and engage in WFH observed a 13.5% decline in their electricity bills, despite a 25% increase in electricity consumption. The model results indicated that bundled adoption of EVs-PVs reduces electricity costs more significantly for a household, compared to the increased costs imposed by more household members staying at home. This holistic assessment presents an opportunity for decision makers to contextualize the changing energy costs and design effective strategies to foster a sustainable and affordable future. Policies such as bundled incentivization to motivate co-adoption, education about energy-management strategies, and add-on subsidies to adopt energy-efficient appliances, will not only accelerate decarbonization but increase household financial savings.

ADVANCED PROPULSION SYSTEMS,SOLAR ENERGY↗
DOE OSTI2024

Triple Effect Economics: How Do Electric Vehicles, Solar Photovoltaics, and Work-from-Home Transform Household Electricity Cost?

Solutions for deep decarbonization need to be affordable as well as sustainable to garner widespread adoption. The past two decades have seen the introduction of new technologies and paradigms such as electric vehicles (EVs), solar photovoltaics (PVs), and increased work-from-home (WFH), which impact a household's overall energy consumption and cost in different ways. However, much attention has not been paid to explore the joint impact these technologies have on a household's electricity cost burden. Leveraging the 2020 Residential Energy Consumption Survey (RECS), this study presents an integrated model to unravel the extent to which the bundled adoption of EV-PV and stay-at-home decisions impact the total electricity cost of households. National level RECS data revealed that, compared to the baseline (i.e., households that did not own any of these technologies), households that own EVs, PVs, and engage in WFH observed a 13.5% decline in their electricity bills, despite a 25% increase in electricity consumption. The model results indicated that bundled adoption of EVs-PVs reduces electricity costs more significantly for a household, compared to the increased costs imposed by more household members staying at home. This holistic assessment presents an opportunity for decision makers to contextualize the changing energy costs and design effective strategies to foster a sustainable and cost saving mechanism. Policies such as bundled incentivization to motivate co-adoption, education about energy-management strategies, and add-on subsidies to adopt energy-efficient appliances, will not only accelerate decarbonization but increase household financial savings.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

The Debate on Vehicle Triad: Examining the Utilization Patterns of Gasoline, Hybrid, and Electric Vehicles in Households

Light-duty vehicles contribute to approximately half of the transport sector's emissions, a sector challenging to decarbonize owing to the autocentric nature of many American cities. The U.S. Department of Transportation's blueprint outlines three key decarbonization strategies: increasing convenience, improving efficiency, and transitioning to zero-emission vehicles. Among these, the adoption of Electric Vehicles (EVs) offers a promising opportunity to reduce emissions from light-duty vehicles, which are predominantly gasoline-powered. Although substantial literature identifies and addresses factors influencing EV adoption, understanding their utilization within households who own vehicles with mixed fuel types is crucial for accurately assessing their environmental benefits. This paper addresses two key questions: (a) Are EVs utilized more, equally, or less than gasoline vehicles (GVs) and (b) What factors influence mileage utilization patterns in households with multiple vehicle fuel types, accounting for substitution and complementary effects? Analysis of 2022 National Household Travel Survey (NHTS) data indicates that EVs are utilized more than GVs within households that own multiple mixed fuel fleets. Findings reveal that households with a higher number of younger individuals and multiple workers use EVs more extensively, and those with older adults and larger households with multiple vehicles tend to use GVs more. Rural households have higher use of hybrid vehicles (HVs). The study identifies a substitution effect between EVs and GVs, while complementary relationship between HVs and EVs, suggesting nuances in vehicle utilization patterns. This nuanced understanding of vehicle utilization patterns informs the development of targeted policies and infrastructure investments to optimize household transportation efficiency.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

1989 Seattle Household Travel Survey Wave 1

The Puget Sound Transportation Panel was initiated in the fall of 1989. The Seattle Household Travel Survey Wave 1, conducted in 1989, was the first wave in a ten-part longitudinal panel survey initiated by the Puget Sound Regional Council to assess the travel patterns of households in the Puget Sound region of Washington state. The panel intended to serve three basic objectives: 1) to be a metropolitan “current population survey” to track changes in employment, work characteristics, household composition, and vehicle availability; 2) to monitor changes in travel behavior and responses to changes in the transportation environment; and 3) to examine changes in attitudes and values as they affect mode choice and travel behavior. This collection contains the first set of panel data for households in King, Kitsap, Pierce, and Snohomish counties. A total of 1,687 households successfully completed a phone survey and returned completed travel diaries for household members age 15 and older. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs in a two-day trip diary. Demographic information for this study includes age, gender, education, employment status, and household income.

1Hz data↗
DOE OSTI2026

1992 Seattle Household Travel Survey Wave 3

The Seattle Household Travel Survey Wave 3, conducted in 1992, was the third wave in a ten-part longitudinal panel survey initiated by the Puget Sound Regional Council to assess the travel patterns of households in the Puget Sound region of Washington state. This collection contains the third set of panel data for approximately 1,700 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20% of households needed to be replaced for each survey wave. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles; 2) document all travel for each household member, aged 15 years or older, for an assigned two-day period; and 3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the two-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

1Hz data↗
DOE OSTI2026

1996 Seattle Household Travel Survey Wave 6

The Seattle Household Travel Survey Wave 6, conducted in the second and third quarters of 1996, was the sixth wave in a ten-part longitudinal panel survey of the travel patterns of households in the Puget Sound region of Washington state. The survey series was initiated in 1989 by the Puget Sound Regional Council. This collection contains the sixth set of panel data for approximately 2,000 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20% of households needed to be replaced for each survey wave. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles; 2) document all travel for each household member, aged 15 years or older, for an assigned two-day period; and 3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the two-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of vehicle passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

1Hz data↗
DOE OSTI2026

2000 Seattle Household Travel Survey Wave 9

The Seattle Household Travel Survey Wave 9, conducted in 2000, was the ninth wave in a ten-part longitudinal panel survey of the travel patterns of households in the Puget Sound region of Washington state. The survey series was initiated in 1989 by the Puget Sound Regional Council. This collection contains the ninth set of panel data for approximately 2,000 households in King, Kitsap, Pierce, and Snohomish counties. Due to various sources of attrition, approximately 20% of households needed to be replaced for each survey wave. The survey relied on the willingness of study area residents to 1) provide demographic information about their household, its members, and its vehicles; 2) document all travel for each household member, aged 15 years or older, for an assigned two-day period; and 3) agree to participate in additional survey waves. After an initial telephone screening, survey participants received mailed travel diaries to aid in documenting travel information for the two-day assessment period. Respondents were instructed to record their mode of transportation, trip purpose, number of passengers, departure and arrival times, ride fare, and parking costs. Demographic information for this study includes age, gender, education, employment status, and household income.

1Hz data↗
DOE OSTI2026

Substitution or Shared Utilization? Intrahousehold Vehicle Use in Mixed-Powertrain Households

While previous research has focused heavily on understanding the factors deriving alternative fuel vehicle adoption rates, there remains a significant gap in understanding how households distribute mileage across different powertrains. This study utilizes data from the 2022 Next Generation National Household Travel Survey to investigate vehicle miles traveled within a sample of 150 plug-in electric vehicle (PEV)-owning households (in which at least one battery electric vehicle is present), characterizing how different powertrains are integrated into daily mobility. Leveraging a Seemingly Unrelated Regression (SUR) framework the study jointly models the utilization of PEVs, hybrid electric vehicles (HEV), and internal combustion engine vehicles (ICEVs) while accounting for household-level substitution effects. The results provide evidence of an asymmetric substitution effect. In households with mixed-powertrain configurations, the ICEV captures a substantially higher share of household miles (compared with the PEV), acting as a utility sponge. Conversely, the model identifies specific socioeconomic and geographic cohorts that prioritize PEV as the primary household workhorse, indicating a systematic sorting effect. Although the sample size limits broader generalizability, these findings suggest that PEVs are used for frequent, specific routine-intensive roles, whereas the ICEV remains a specialized utility vehicle. These insights highlight distinct intrahousehold vehicle use behaviors that are often obscured by aggregate fleetwide statistics.

25 ENERGY STORAGE↗
DOE OSTI2025

2012 California Household Travel Survey Supplement

# 2012 California Household Travel Survey Supplement The 2012 California Household Travel Survey Supplement focused on gathering specific travel information from residents for the development of next-generation, activity-based models. Called the "Augment Survey," it supplemented the [2010–2012 California Household Travel Survey](https://www.nrel.gov/transportation/secure-transportation-data/tsdc-california-travel-survey). ## Data Collection Agency The Southern California Association of Governments (SCAG) hired Abt-SRBI, Inc. to conduct the survey. ## Methodology Travel data were collected from households via in-vehicle (625 vehicles) and wearable (244 participants) global positioning system (GPS) devices. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Study records include 473 households. ## Transportation Data The SCAG data set contains data from 473 households that participated in one or more areas of study. Of these, 141 completed the wearable GPS portion of the study and 332 completed the vehicle GPS portion. There was no overlap between households participating in the two study areas (wearable and vehicle GPS). For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/caltrans_scag_data_dictionary.pdf?sfvrsn=6ec36d7a_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗