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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Who Controls Energy in the Smart Home? A Multidisciplinary Taxonomy

Advances in technology have begun to open new opportunities for behavior-based and technical approaches to managing residential energy use and meet sustainability-related objectives. Visions of the future predict homes with smart technologies delivering enhanced comfort and cost savings to residents; utility-partners who can remotely optimize energy resources to meet grid needs; and occupants who play more active roles in the energy system supported by advanced information communication technologies. Each of these scenarios implies augmented control over home energy use, yet uncertainties remain regarding which ones will deliver the greatest grid benefits and services to customers in a given situation. These scenarios also raise broader questions regarding customer agency and the relationship between customers and third parties moving forward. While both the provision of information to spur behavior change and automated technologies theoretically enhance control over energy use in the built environment, these strategies are not often studied from an integrated perspective. Seeking to address this gap and develop a deeper understanding of the evolving paradigm of control over home energy use, this paper presents a taxonomy to evaluate perspectives from public policy (ex. demand-side management), technological innovation (automated controls), and user-agency (ex. the role of behavior change) on approaches to managing home energy use. We draw on theoretical and empirical evidence from across disciplines to detail the dimensions and implications of deploying programs that incorporate various levels of control and anticipate such a taxonomy will help holistically map out and evaluate tradeoffs between different approaches to demand-side management moving forward.

McIlvennie, Claire↗
DOE OSTI2022

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Scalability and Effectiveness of Smart Charge Management

The rise in electric vehicle (EV) adoption presents growing challenges for power grids, particularly from simultaneous residential charging, which can cause voltage fluctuations and increase feeder peak loads. Baltimore Gas and Electric (BGE), with support from the U.S. Department of Energy, initiated a pilot program to evaluate managed residential EV charging through Smart Charge Management (SCM). This study analyzes real-world charging behavior data from the pilot and feeder-level base loads from BGE to simulate residential charging scenarios through 2035 across the Washington, DC–Baltimore region. Grid impacts under unmanaged charging are compared to three SCM strategies: TOU-immediate, TOU-distributed, and Load Balancing. Results show that the magnitude of peak reduction is highly feeder-dependent. Some feeders achieve reductions of more than 40% at high enrollment levels, while others show improvements closer to 10–15%. This heterogeneity reflects differences in baseline feeder load shapes, EV penetration, and plug-in behavior across customers. Results also highlight trade-offs between shifting load away from peak periods and minimizing secondary demand peaks, offering practical insights for future utility program design.

Electric vehicle↗
DOE OSTI2022

Determinants of residential mobility: an adaptive retrospective survey method

This study introduces a survey instrument to collect retrospective life-course events, focusing on residential relocation, and it utilizes the survey to evaluate determinants of residential mobility. Here, the survey consists of seven modules collecting information about household structure and household demographics, latest residential relocation, current and previous home, employment, education, vehicle ownership, and travel behavior. The time window of the life-course calendar in the survey is customized according to the latest residential relocation as an anchor point to assist with memory recollection and balance the required input from participants. The survey is used to collect data from a sample of 514 respondents in Sydney, Australia, and another sample of 404 respondents in Chicago, Illinois, in the US. The Cox proportional hazard model is used to analyze residential mobility. The results show primary school commencement is a salient determinant of residential relocation, but its impact is significantly higher in Chicago compared to Sydney.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Personal and environmental predictors of polycyclic aromatic hydrocarbon exposure identified through repeated silicone wristband sampling

This study integrates quantitative data on personal exposure to polycyclic aromatic hydrocarbons (PAHs) in 162 silicone wristbands with demographics, behavioral information, and housing characteristics to explore contributions to residential exposure in a superfund-adjacent community over the course of a year. Forty-six residents completed questionnaires and wore silicone wristbands as personal passive samplers for seven consecutive days on up to four separate occasions in alternating months between November 2022 and June 2023. It was hypothesized that individual behaviors and housing characteristics are sources of dependence and correlation between personal PAH exposures. 50 PAHs were detected at least once, 17 of which were alkylated PAHs. Exposure to PAHs of similar molecular weight was often correlated, notably between naphthalenes (2-rings) and higher molecular weight PAHs (3 or more rings). Generalized linear mixed models identified flooring type, participant age, and sampling month as important predictors of increased PAH exposure, and flooring type, and use of wood stoves or heavy machinery as predictors of increased naphthalene exposure relative to higher molecular weight PAHs. Individual chemical models based on concentration data and detection frequencies corroborated these findings across multiple PAHs. We demonstrate that personal exposure is not static and the degree of variability in personal exposure is individual. Hence, identification of influential exposure factors through repeated measures of chemical exposure and characterization of variability in personal exposure as performed in this study, is important in the development of exposure mitigation strategies.

Bonner, Emily↗
DOE OSTI2026

Data-Driven Modeling of High-Resolution Residential Load Profiles Using Low-Resolution Smart Meter Measurements

Accurate and high-resolution residential load profiles are essential for power system modeling, demand response planning, and effective grid operation. As the energy sector moves towards a more actively managed distribution system, the ability to understand residential energy consumption at a minute-by-minute scale becomes increasingly critical. High-resolution load profiles provide key insights into demand patterns and user behavior, enabling grid operators to design more effective energy solutions; however, residential load measurements in the field are typically recorded at low resolutions, such as 15-60 minutes, which makes it hard to study the characteristics of different residential customers. This paper addresses these challenges by introducing a data-driven approach to generate realistic, high-resolution residential load profiles based on lowre-solution measurements and weather information. The proposed method retains the key features of the actual residential load measurements while offering appliance-level energy consumption details for each residential building. The results demonstrate the effectiveness of the proposed load profile generator, proving its capability to support utilities in optimizing residential energy management and ensuring a more reliable and resilient grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

COVID-19 pandemic ramifications on residential Smart homes energy use load profiles

The COVID-19 pandemic has significantly affected people’s behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Cluster analysis of occupancy schedules in residential buildings in the United States

The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.

42 ENGINEERING↗
DOE OSTI2025

Assessing the hygrothermal performance of bio-based materials in building wall systems

Building envelope systems are crucial in regulating thermal and moisture exchange between interior and exterior environments, accounting for approximately 28 % of building energy consumption in the United States with walls being the primary contributors. Improper selection of building envelope materials can lead to moisture-related issues, reduced resilience, and compromised durability. Hygrothermal performance assessment is a key factor in efficient building design. As such, improving the energy and hygrothermal performance of opaque wall materials, through careful assessment of material choices, is essential to enhancing building resilience, lowering energy costs, and improving occupant comfort. As the building industry seeks new strategies to reduce material energy intensity, bio-based materials emerge as a promising solution. However, their long-term hygrothermal performance in building envelope systems remains underexplored. To fill this gap, this study evaluates the hygrothermal behavior of 13 bio-based materials in residential wall systems across three U.S. climate zones. Laboratory experiments were performed to measure material properties such as density, thermal conductivity, moisture transmission, and sorption isotherms. These data were integrated into the WUFI® simulation tool to assess wall hygrothermal performance in Houston, Baltimore, and Chicago. A three-phase modeling approach was used: (1) baseline residential walls with oriented strand board (OSB) and gypsum board; (2) replacing OSB with bio-based materials; and (3) replacing drywall with bio-based materials. Results showed that the evaluated bio-based materials maintained acceptable moisture thresholds of ≤ 16 % across all climates, confirming their viability as an alternative for current sheathing materials. Furthermore, this study provides a foundation for future research and innovation in material science on the use of certain bio-based materials in high-performance, low energy use residential construction. Ultimately, providing critical data, offering a database of bio-based material properties, and supplying a simulation-based approach will help designers make informed decisions for future efficient building practices.

Bio-based materials↗
DOE OSTI2023

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Abstract Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The impact of simplified window and exhaust fan assumptions on indoor air quality in multifamily buildings

In residential buildings, the indoor air quality can be significantly affected by ventilation measures initiated by occupants, including the operation of windows and in-unit exhaust fans in kitchens and bathrooms. Many simulations simplify these factors by disregarding window opening behaviors and using fixed schedules for exhaust fan operation across all residential units. To estimate the impact of these simplifications in the U.S. context, this study used coupled CONTAM and EnergyPlus models to simulate airflow and contaminant transport in multifamily buildings. The coupled models parametrically varied climate zone, building airtightness, and mechanical ventilation system types. The study conducted a sensitivity analysis on two key occupant behaviors: (1) operating kitchen and bathroom exhausts on different schedules in individual dwelling units, and (2) scheduling open windows on ground and top floors. The simplified assumptions (i.e. uniform in-unit exhaust fan operation and window operation) had a minimal impact on inter-unit air flow and contaminant transport simulations across a broad range of building air leakage and mechanical ventilation system types. These findings suggest that for buildings with tight construction it is reasonable for most modelling and simulation efforts to ignore the effects of non-uniform exhaust fan operation and window opening.

Occupant behavior↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Urban Green Spaces, Greenness Exposure and Species Richness in Residential Environments and Relations with Physical Activity and BMI in Portuguese Adolescents

Environmental factors play an important role in obesity-related behaviors. Evidence indicates significant associations between weight and urban green spaces in adults, but it is not clear whether this relationship applies to adolescents. Therefore, our aim was to determine the associations between urban green spaces, greenness exposure and species richness in residential environments with physical activity and body mass index. Sixty-two adolescents between 12 and 18 years of age answered a self-administered questionnaire, providing information on height, weight, age, sex and home address. Data on socioeconomic deprivation were obtained from the European Index of Deprivation for Small Portuguese Areas. Physical activity levels were assessed using accelerometers. Urban green space counts and the normalized difference vegetation index values were measured using buffers along the roads with distances of 300, 500, 1000 and 1500 m from each participant’s residence. To quantify the species richness, the species richness index was used. Linear regression models were fitted to analyze whether urban green spaces, exposure to green spaces and species richness counts for each distance were associated with physical activity and self-reported body mass index. We did not find significant associations between the independent variables and the probability of overweight or obesity. The relationship between environmental variables, adolescents’ physical activity and body weight seems to be complex and further studies may contribute to better understanding of the topic.

Melo, Juliana (ORCID:0000000301275340)↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗