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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Chapter 17: Residential Behavior Evaluation Protocol. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures, September 2011 - August 2020

This document has been updated in August 2020. This document was developed for the U.S. Department of Energy Uniform Methods Project (UMP). The UMP provides model protocols for determining energy and demand savings that result from specific energy-efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The protocols are updated on an as-needed basis. The UMP protocols can be used by utilities, program administrators, public utility commissions,evaluators, and other stakeholders for both program planning and evaluation. To learn more about the UMP, visit the website, https://energy.gov/eere/about-us/ump-home, or download the UMP introduction document at http://www.nrel.gov/docs/fy17osti/68557.pdf.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Aggregate residential demand flexibility behavior: A novel assessment framework

Residential demand flexibility (DF) could save the U.S. electric grid up to 10 GW of peak demand while supporting increased amounts of renewable generation. However, less than 40% of the estimated DF peak reduction capacity is currently realized, and less than 8% of American households are enrolled. These low participation rates are combined with high rates of "overriding" a DF event and eventual opt-outs among enrolled customers. There is still not a comprehensive understanding of the drivers of DF participation and occupant interaction with DF program signals. We, therefore, present a novel survey processing framework to assess occupant DF-relevant behavior from the American Time Use Survey (ATUS). Using the framework, we summarize both the extensive and intensive behavior of more than 200,000 ATUS respondents (2003-2018 data) and provide insights on the DF-relevant behavior of residential occupants, which is generally overlooked in the literature. Here we use the framework to identify the activity priorities of residential occupants in the United States during different DF-relevant periods (critical peak, peak, and off-peak). These preferred activities capture overlooked routine behaviors that could be barriers to DF participation, if ignored, and might explain the high levels of overrides often exhibited by participants of demand response.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Data-driven occupant-behavior analytics for residential buildings

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

The impact of behavioral and geographic heterogeneity on residential-sector carbon abatement costs

Analyses of monetary and emissions savings from residential efficiency upgrades usually neglect behavioral differences between consumers. Variations in behavior are frequently large: 13% of the U.S. population sets thermostats for cooling at 20 °C (68 °F) or lower and 15% at 25.5 °C (78 °F) or higher. Efficiency analyses should account for behavioral heterogeneity as well as variations in climate and housing characteristics. We model energy and economic savings of efficiency upgrades for U.S. single-family detached houses, accounting for differences in thermostat settings, climate zone, fuel prices, and home characteristics. We consider five efficiency interventions: wall insulation, attic insulation, air sealing, high-efficiency furnaces, and high-efficiency air conditioners. Energy and economic savings vary widely by consumer; the average net present value for wall insulation is $3,020 United States Dollar (USD), but considering heterogeneity, it has a 10th percentile value of -$231 and 90th percentile value of $5,000. Neglecting heterogeneity, technology upgrades can be prioritized from lower to higher carbon abatement costs: air conditioners, wall insulation, furnace, air sealing, and attic insulation. Accounting for heterogeneity reorders this prioritization, and consumer-specific conditions affect mitigation costs such that technologies are mixed in with one another and are no longer arranged one after another. These results indicate that interventions to improve efficiency should consider differences between consumers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Assessing residential PM 2.5 concentrations and infiltration factors with high spatiotemporal resolution using crowdsourced sensors

Building conditions, outdoor climate, and human behavior influence residential concentrations of fine particulate matter (PM 2.5 ). To study PM 2.5 spatiotemporal variability in residences, we acquired paired indoor and outdoor PM 2.5 measurements at 3,977 residences across the United States totaling >10,000 monitor-years of time-resolved data (10-min resolution) from the PurpleAir network. Time-series analysis and statistical modeling apportioned residential PM 2.5 concentrations to outdoor sources (median residential contribution = 52% of total, coefficient of variation = 69%), episodic indoor emission events such as cooking (28%, CV = 210%) and persistent indoor sources (20%, CV = 112%). Residences in the temperate marine climate zone experienced higher infiltration factors, consistent with expectations for more time with open windows in milder climates. Likewise, for all climate zones, infiltration factors were highest in summer and lowest in winter, decreasing by approximately half in most climate zones. Large outdoor–indoor temperature differences were associated with lower infiltration factors, suggesting particle losses from active filtration occurred during heating and cooling. Absolute contributions from both outdoor and indoor sources increased during wildfire events. Infiltration factors decreased during periods of high outdoor PM 2.5 , such as during wildfires, reducing potential exposures from outdoor-origin particles but increasing potential exposures to indoor-origin particles. Time-of-day analysis reveals that episodic emission events are most frequent during mealtimes as well as on holidays (Thanksgiving and Christmas), indicating that cooking-related activities are a strong episodic emission source of indoor PM 2.5 in monitored residences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An experimental study of the behavior of a high efficiency residential heat pump in cooling mode with common installation faults imposed

Faults are believed to be common in split system air-conditioner and heat pump systems, due to operating degradation or installation problems. Common faults include improper refrigerant charge, reduced evaporator airflow, liquid line restrictions, and the presence of non-condensable gas. Laboratory tests were used to quantify fault impacts on performance of a heat pump system, and impacts on indicator variables such as refrigerant temperatures and pressures. This paper describes a large set of laboratory tests implemented on a high efficiency heat pump operating in cooling mode. This system uses R-410A refrigerant and has a rotary compressor, TXV, two accumulators, and a compensator. A small number of previous experimental studies have been done previously to study the effects of some operating faults, but none has examined a modern system with these components. The tests were conducted with a range of fault intensities and driving conditions. The results are compared to previous researchers’ experimental results. The system’s performance was found to be quite robust in the presence of faults. The TXV, accumulators, and compensator significantly reduce sensitivity to refrigerant charge and liquid line restriction faults, and other faults to a lesser extent.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Factors Affecting Override Behavior during Demand Flexibility from High-Resolution Smart Thermostat Data

In this study, we delineate key weather and demographic predictors of override behavior in residential buildings during connected thermostats demand response events. Anticipating and reducing overrides is critical to demand flexibility (DF) program success. We use high- dimensional fixed effects linear regression techniques on a large ecobee dataset for about 5,000 enrolled households in the United States. We identify critical weather (indoor and outdoor temperature), building (housing type), and occupant (previous DF overrides and previous DF event exposure) factors influencing the override patterns of customers during thermostat demand response. We also differentiate DF events by season to understand the different indoor and outdoor conditions that might influence seasonal override rates. We found significant differences in override rates between building types, with single-family semi-detached homes generally having the highest overrides. Having a history of overrides was additionally a critical factor in predicting occupant response to future DF events. Understanding these override differences is necessary for rural electric cooperatives and emerging DF programs without access to large DR historical data. Overall, we provide critical information on technical and local demographic characteristics that may be correlated to building type to influence strategies to reduce overrides and improve the adoption of DF technologies.

connected thermostats (CTs)↗
DOE OSTI2021

Dishwashers in the Residential Sector: A Survey of Product Characteristics, Usage, and Consumer Preferences

Data on consumer purchasing decisions, usage, and behaviors relating to residential appliances help to inform technical and economic analyses related to the energy and water used by those appliances, including dishwashers. Existing publicly available data for dishwashers include two regularly conducted national surveys that describe dishwasher ownership and usage. The United States (US) Department of Energy’s (DOE) Energy Information Administration’s Residential Energy Consumption Survey (RECS) records the presence of a dishwasher in the home, the numbers of times per week the dishwasher is operated, and the dishwasher age, along with household demographic characteristics. The US Census Bureau’s American Housing Survey (AHS) also records the presence of a dishwasher in the home along with household demographic characteristics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Demand Response Analysis for Different Residential Personas in a Comfort-Driven Behavioral Context

Low demand response (DR) participation and high program drop-out rates continue to impede DR goals that could save up to $13 billion in annual grid expansion and electricity demand costs. Yet, the literature lacks a thorough understanding of how different residential customer segments enrolled in DR programs respond to utility signals in view of occupant comfort considerations. The objective of this study is to gain a clear understanding of the effects of four different customer personas on residential DR. Given current data limitations, this work developed an array of hypothetical personas with varied priorities, activity levels, and comfort thresholds based on demographic variables that have been found in previous studies to influence energy consumption. A BEopt DR model for a reference residential single-family building located in Colorado was built to isolate the effect of differences in buildings or climate. The results provide useful evidence on how persona-comfort differences lead to significant deviations in DR goals (especially peak demand reduction), ranging from 0.1% to 20%. This work presents a novel framework representing comfort preferences in DR models. The data generated, albeit synthetic, and the results could inform DR program design considerations of how different people respond to different comfort priorities.

BEopt↗
DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Consumer Purchasing Behavior and Usage of Lighting in the Residential Sector

We present the results of a 2019 online survey of 1,800 adults in the United States who recently purchased a light bulb to understand how consumers make decisions about purchasing light bulbs and how light bulbs are used in the residential sector. From our survey, we found that purchases of LED bulbs made up the largest percentage of sales by lighting technology and respondents generally had positive impressions of LEDs. We estimated the average daily hours of use for bulbs being purchased depending on bulb shape. We also provide an estimate for the lumen distribution of purchased light bulbs and the distribution of installation locations by bulb shape. We found that incandescent bulb purchasers were primarily replacing a failed incandescent bulb and these purchasers were less likely to have favorable impressions of LEDs. However, we found that the majority of participants replacing an incandescent bulb purchased either a CFL or LED. Using an adaptive conjoint analysis technique, we found that the five most important factors to consumers when choosing a light bulb are purchase price, bulb lifetime, energy cost savings relative to an incandescent bulb, light appearance, and energy savings relative to an incandescent bulb. The results of this study provide an in-depth look at the lighting market in 2019 and may provide a basis for developing models of the residential lighting market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Hygrothermal Performance of Bio-Based Materials in Residential Building Wall Envelope Systems

In the United States, building envelope systems contribute approximately 28% of building energy consumption, with walls being a primary factor. As the construction industry explores new approaches to lower the energy intensity needed to manufacture materials and, in turn, reduce energy consumption, bio-based materials are gaining attention as a promising alternative. While prior studies have demonstrated that bio-based materials can decrease material energy intensity, their long-term hygrothermal behavior within a whole residential wall system is not yet fully understood. This study aims to evaluate the potential of replacing Oriented Strand Board (OSB) and drywall in residential wall systems with bio-based alternatives. The research focuses on two bio-based materials, assessing their hygrothermal performance under Chicago climate condition (Climate Zone 5). To achieve that, a series of laboratory experiments were conducted to measure key properties of the bio-based materials such as thickness, dimensions, density, thermal conductivity (as a function of temperature and humidity), moisture-dependent permeance and water vapor permeability (WVP), and sorption isotherms. These properties were then used in WUFI® simulations to predict the moisture durability of a standard residential wall system in Climate Zone 5. Results show that substituting OSB and drywall with bio-based materials can achieve acceptable moisture durability, effectively mitigating risks of mold growth and structural damage over time.

Palani, Hevar [ORNL] (ORCID:0000000220211994)↗
DOE OSTI2022

‪A Novel Methodology for Longitudinal Studies of Home Thermal Comfort Perception and Behavior

Human-building interactions significantly influence building energy consumption and affect peak energy demand. For example, heating and cooling contribute 46% of daily peak residential energy demand. Grid-interactive efficient buildings (GEBs) can potentially increase energy-demand flexibility and accelerate the adoption of renewables. However, traditional demand response (DR) programs focused on shedding peak loads disregard the human-building interactions leading to occupant thermal frustration. Specifically, they do not model occupants’ ability to override thermostat controls, nor how indoor environmental conditions and sociocultural factors affect the timing and magnitude of overrides. Studies have found 20% of occupants override thermostat setpoints during DR events longer than 6 hours yet lack detail about the motivation that might guide more successful efforts. Balancing energy-demand flexibility with occupant thermal comfort requires understanding dynamic occupant behavior, the underlying psychophysiological drivers, in the context of homes. This paper presents methods for scalable longitudinal studies of residential occupant behavior dynamics to inform the development of psychophysiological occupant-centric building models. Smart sensors were installed to measure local environmental conditions in 20 homes in two regions of the United States. Just-in-time ecological momentary assessments (EMAs) provided qualitative data on occupant thermal comfort and local environmental conditions not captured in Internet-of-Things (IoT) based studies or existing datasets. Participant interviews provided insight into environmental attitudes, mental models, and the role of economics in comfort and behavior, which in turn may affect thermostat interactions. Based on these data, interventions in the next phase of the study will collect data and monitor occupant behavior during simulated DR events.

Kane, Michael↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2024

Analyzing Residential Charging Demand for Light-Duty Electric Vehicles in Colorado

The past decade has witnessed a remarkable surge in adoption of electric vehicles (EVs). The momentum is expected to continue with strong support from governments and industry. Rapid EV adoption will add significant electricity demand, making it critical to plan for and manage EV charging to avoid causing additional stress and non-negligible risks to the already-aging power grid. To help power grid operators understand the impacts of residential EV charging and identify risk factors, this study presents a data-driven charging demand analysis for light-duty vehicles. This study considers two real-world grid service regions in Colorado and merges multiple data sources and state-of-the-art tools that characterize EV adoption projections, vehicle travel patterns, seasonal variations, residential charging accessibility, ambient temperature impact, EV charging behaviors, grid utility customers, vehicle registration, and household-level EV charging demand distribution. We characterize potential residential charging demand in 2030 for two regions within the state of Colorado: Boulder and Aurora regions. We project that EVs will be 26% of the light-duty vehicle population in Boulder and 16% in Aurora areas. Charging demand is characterized for ten power grid feeders (five for each study region). Across the ten feeders, peak total EV charging powers during wintertime range from less than 1 MW to more than 4 MW.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Analyzing Residential Charging Demand for Light-Duty Electric Vehicles in Colorado: Preprint

The past decade has witnessed the rapid adoption of electric vehicles (EVs). The momentum is expected to continue with strong support from the government and industry. Rapid EV adoption brings significant charging demand to the power grid, causing additional stress and non-negligible risks to the already-aging power grid. To help power grid operators understand the impacts of EV home charging on the grid and identify risk factors, this study presents a data-driven residential charging demand analysis for light-duty vehicles. This study considers two real-world grid service regions in Colorado and merges multiple data sources and state-of-the-art tools that characterize EV adoption projections, vehicle travel patterns, seasonal variations, residential charging accessibility, ambient temperature impact, EV charging behaviors, grid utility customers, vehicle registration, and household-level EV charging demand distribution. We characterize potential residential charging demand in 2030 for two regions within the state of Colorado: Boulder and Aurora. We project that EVs will be 26% of the light-duty vehicle population in Boulder and 16% in Aurora. Charging demand is characterized for ten power grid feeders (five for each study region). Across the ten feeders, peak total EV charging powers during wintertime range from less than 1 MW to more than 4 MW.

ADVANCED PROPULSION SYSTEMS↗