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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Procedures for Calculating Residential Dehumidification Loads

Residential building codes and voluntary labeling programs are continually increasing the energy efficiency requirements of residential buildings. Improving a building's thermal enclosure and installing energy-efficient appliances and lighting can result in significant reductions in sensible cooling loads leading to smaller air conditioners and shorter cooling seasons. However due to fresh air ventilation requirements and internal gains, latent cooling loads are not reduced by the same proportion. Thus, it's becoming more challenging for conventional cooling equipment to control indoor humidity at part-load cooling conditions and using conventional cooling equipment in a non-conventional building poses the potential risk of high indoor humidity. The objective of this project was to investigate the impact the chosen design condition has on the calculated part-load cooling moisture load, and compare calculated moisture loads and the required dehumidification capacity to whole-building simulations. Procedures for sizing whole-house supplemental dehumidification equipment have yet to be formalized; however minor modifications to current Air-Conditioner Contractors of America (ACCA) Manual J load calculation procedures are appropriate for calculating residential part-load cooling moisture loads. Though ASHRAE 1% DP design conditions are commonly used to determine the dehumidification requirements for commercial buildings, an appropriate DP design condition for residential buildings has not been investigated. Two methods for sizing supplemental dehumidification equipment were developed and tested. The first method closely followed Manual J cooling load calculations; whereas the second method made more conservative assumptions impacting both sensible and latent loads.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

The Price-Concentration Relationship in Early Residential Solar Third-Party Markets

The market for residential solar photovoltaic (PV) systems in the United States has experienced tremendous growth over the past decade, with installed capacity more than doubling between 2014 and 2016 alone (SEIA, 2016). As the residential market continues to grow, it prompts new questions about the nature of competition between solar installers and how this competition, or lack thereof, affects the prices consumers are paying. It is often assumed that more competition leads to lower prices, but this is not universally true. For example, some studies have shown that factors such as brand loyalty could lead to a negative relationship between concentration and price in imperfectly competitive markets (Borenstein, 1985; Holmes, 1989). As such, the relationship between prices and market concentration is an open empirical question since theory could predict either a positive or negative relationship. Determining a relationship between prices and market concentration is challenging for several reasons. Most significantly, prices and market structure are simultaneously determined by each other -- the amount of competition a seller faces influences the price they can command, and prices determine a seller's market share. Previous studies have examined recent PV pricing trends over time and between markets (Davidson et al., 2015a; Davidson and Margolis 2015b; Nemet et al., 2016; Gillingham et al., 2014; Barbose and Darghouth 2015). While these studies of solar PV pricing are able to determine correlations between prices and market factors, they have not satisfactorily proven causation. Thus, to the best of our knowledge, there is little work to date that focuses on identifying the causal relationship between market structure and the prices paid by consumers. We use a unique dataset on third-party owned contract terms for the residential solar PV market in the San Diego Gas and Electricity service territory to better understand this relationship. Surprisingly, we find that firms charged higher prices in more competitive markets in our sample. The finding is robust across multiple definitions of firm concentration. There are at least two potential explanations for our findings. First, firms could be conducting entry deterrence strategies. It is possible that firms are acting in a non-competitive way and setting prices lower than they would be otherwise. Setting low prices that are below potential competitors' marginal costs could deter entrants and ensure a larger market share. Second, there could be a group of dominant firms (with a competitive fringe), and the dominant firms may occasionally engage in price wars. If this is true, prices should be lower in more concentrated markets during the price wars (Salinger, 1990). As the rooftop PV market continues to grow, market structure will remain a relevant policy issue in consideration of the potential for rooftop solar to contribute to de-carbonization efforts or other policy objectives. This paper adds to a growing emphasis on understanding supply-side factors in scaling up solar markets in the residential sector. Generally, solar markets have become more competitive over the same time period that solar technology costs decreased. While solar system hard costs have come down, our research suggests that total costs are more nuanced in early solar system TPO markets. Policymakers should consider these findings when designing markets, and have the data needed to make informed decisions.

14 SOLAR ENERGY↗
DOE OSTI2018

Income Trends of Residential PV Adopters: An analysis of household-level income estimates [PowerPoint presentation]

The residential photovoltaic (PV) market has expanded rapidly over the past decade, but questions exist about how equitably that growth has occurred across income groups. Prior studies have investigated this question but are often limited by narrow geographic study regions, now-dated analysis timeframes, or coarse estimates of PV-adopter incomes. At the same time, a spate of new programs and initiatives, as well as innovations in business models and product design, have emerged in recent years with the aim of making solar more accessible and affordable to broader segments of the population. Yet, many of those efforts are proceeding without robust underlying information about the income characteristics of recent residential PV adopters. This work aims to establish basic factual information about income trends among U.S. residential solar adopters, with some emphasis on low- and moderate-income (LMI) households. The analysis is unique in its relatively extensive coverage of the U.S. solar market, relying on Berkeley Lab’s Tracking the Sun dataset, which contains project-level data for the vast majority of all residential PV systems in the country (a subset of which are ultimately included in the analysis sample). This analysis is also unique in its use of household-level income estimates that provide a more-precise characterization of PV-adopter incomes than in most prior studies.

14 SOLAR ENERGY↗
DOE OSTI2018

West Virginia Residential Codes Study

The U.S. Department of Energy (DOE) Building Energy Codes Program launched a series of research studies in 2014 across several U.S. states to investigate typical energy efficiency practices in residential buildings and identify opportunities to reduce household utility bills. A primary goal of the study was to help document baseline practices in new single-family construction, target areas for improvement relative to the state energy code, and quantify related savings potential. This information was intended to assist states in measuring energy compliance with their codes and to identify areas of focus for future education and training initiatives. Following the original baseline studies, project teams in each state conducted a series of education, training and outreach activities aimed at improving compliance with the state residential energy code. A second baseline study was then implemented in several states with the objective of determining whether the investment in education, training and outreach programs had produced a significant and measurable change in statewide residential energy use. Through the program, DOE planned to establish a sufficient data set to represent baseline construction trends across states and detect significant changes in energy use resulting from education and training activities. The three basic phases employed were as follows: I. Pre-Study: A baseline study to identify the energy use in typical single-family residential buildings in a given state and opportunities for improving energy efficiency II. Education & Training: Education, training and outreach activities targeting issues identified through the baseline study III. Post-Study : A second study to identify the change in energy use following the education and training activities This report outlines the application of the study in the state of West Virginia with a focus on the experiences of the project team across the multi-phased approach. Data gathered through phase I has been submitted to Pacific Northwest National Laboratory (PNNL) for analysis. Findings available to date are published as PNNL technical reports on the DOE Building Energy Codes Program website with final analysis pending publication later in 2018.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Residential Energy Code Compliance in The Southeast: Georgia and Arkansas

The U.S. Department of Energy (DOE) Building Energy Codes Program launched a series of research studies in 2014 across several U.S. states to investigate typical energy efficiency practices in residential buildings and identify opportunities to reduce household utility bills. A primary goal of the study was to help document baseline practices in new single-family construction, target areas for improvement relative to the state energy code, and quantify related savings potential. This information was intended to assist states in measuring energy compliance with their codes and to identify areas of focus for future education and training initiatives. Following the original baseline studies, project teams in each state conducted a series of education, training and outreach activities aimed at improving compliance with the state residential energy code. A second baseline study was then implemented in each state with the objective of determining whether the investment in education, training, and outreach programs had produced a significant and measurable change in statewide residential energy use. Through the program, DOE planned to establish a sufficient data set to represent baseline construction trends across states and detect significant changes in energy use resulting from education and training activities. The three basic phases employed in the study were as follows: I. Pre-Study : A baseline study to identify the energy use in typical single-family residential buildings in a given state and opportunities for improving energy efficiency II. Education & Training : Education, training, and outreach activities targeting issues identified through the baseline study III. Post-Study : A second study to identify the change in energy use following the education and training activities This report outlines the application of the study in the states of Arkansas, Georgia, Tennessee, and Virginia with a focus on the experiences of the project team across the “Pre-Study” and “Education and Training” phases. Data gathered through Phase I has been submitted to Pacific Northwest National Laboratory (PNNL) for analysis. Findings available to date are published as PNNL technical reports on the DOE Building Energy Codes Program website with Post-Study analysis pending publication in 2019.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Considerations for Fire Service Response to Residential Battery Energy Storage System Incidents

Renewable energy sources (e.g., rooftop photovoltaics, wind turbines) are capable of addressing our grid-level energy challenges by reducing environmental impacts, increasing resiliency, and increasing the supply of input energy. However, renewable energy sources alone are not a complete solution. The generation of power from major renewable energy sources fluctuates with the weather, creating significant challenges in matching electrical power generation to electrical power consumption. The value of renewable power generation sources is multiplied when paired with battery energy storage systems (ESS), which can store excess power generated from renewable energy sources when it is not consumed and deliver that power later when demand exceeds base power generation. Residential ESS units are frequently installed to support renewable energy initiatives by storing energy from intermittent sources such as photovoltaic panels. Residential ESS units also offer an alternative to gas-powered generators or other backup power means. Lithium-ion batteries are the most common residential ESS technology due to their affordability and energy density. Though lithium-ion systems come with benefits, there is also a risk of thermal runaway within this technology which can result in flammable gas release, fire, and explosion. As the installations of these products increase, the frequency of response to fire incidents involving these products will increase. In response to this new and evolving hazard, UL Solutions (ULS) and UL Fire Safety Research Institute (FSRI) have partnered with the International Association of Fire Fighters (IAFF) to conduct a series of large-scale tests sponsored by the US Department of Energy to characterize the challenges for fire fighters responding to fires involving residential energy storage systems. The project focuses on developing size-up and tactical considerations to support the fire service in navigating the evolving modern fireground.

25 ENERGY STORAGE↗
DOE OSTI2020

Residential Natural Gas Demand Response Potential during Extreme Cold Events in Electricity-Gas Coupled Energy Systems

In regions where natural gas is used for both power generation and heating buildings, extreme cold weather events can place the electrical system under enormous stress and challenge the ability to meet residential heating and electric demands. Residential demand response has long been used in the power sector to curtail summer electric load, but these types of programs in general have not seen adoption in the natural gas sector during winter months. Natural gas demand response (NG-DR) has garnered interest given recent extreme cold weather events in the United States; however, the magnitude of savings and potential impacts—to occupants and energy markets—are not well understood. We present a case-study analysis of the technical potential for residential natural gas demand response in the northeast United States that utilizes diverse whole-building energy simulations and high-performance computing. Our results show that NG-DR applied to residential heating systems during extreme cold-weather conditions could reduce natural gas demand by 1–29% based on conservative and aggressive strategies, respectively. This indicates a potential to improve the resilience of gas and electric systems during stressful events, which we examine by estimating the impact on energy costs and electricity generation from natural gas. We also explore relationships between hourly indoor temperatures, demand response, and building envelope efficiency.

03 NATURAL GAS↗
DOE OSTI2024

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗
DOE OSTI2017

Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Analysis of Residential Time-of-Use Utility Rate Structures and Economic Implications for Thermal Energy Storage

Thermal energy storage (TES) is an increasingly popular tool to level out the daily electrical demand and add stability to the electrical grid as more intermittent renewable energy sources are installed. TES systems can locally decouple high thermal loads from the operation of a heat pump or reduce the electrical energy demand of the heat pump by providing a more favorable temperature gradient. In addition, many policy makers and utility providers have introduced time-of-use (TOU) rate schedules for residential customers to better reflect the price of electricity generation and demand for specific times. TOU rate schedules price grid-provided electricity differently throughout the day depending on the region’s climate, time of year, and electrical production. Large differences between on-peak and off-peak electrical prices may create an economic advantage for a residential customer to install a TES system. In this work, the economic and energy savings are calculated for a modeled 2,400 square foot residential building with water/ice-based TES using a TOU rate structure. The weather data is from Fresno County, CA, ASHRAE climate zone 3B, and a representative residential TOU utility rate structure from Pacific Gas and Electric (PG&E) was used. The results showed that total energy consumption could be reduced by 14.5% with an 87.5% reduction in on-peak energy usage when the TES is installed. The cost of operating this system for space cooling was reduced by nearly 20% using the sample utility rate plan.

Sultan, Sara↗
DOE OSTI2024

Towards Non-Intrusive Real-Time Monitoring of Behind the Meter Residential Distributed Energy Resources

The growing adoption of residential distributed energy resources (DERs) introduces more uncertain variability in power grid operation. More importantly, the residential DERs operate behind customers’ energy meters, and therefore, the utility cannot “directly” monitor them. Prior approaches to enable visibility into behind-the-meter (BTM) DERs either depend on estimations or require intrusive instrumentation on the customer side. To address the critical need for direct real-time monitoring of BTM DERs, in this paper, we propose a novel approach for utility-side direct real-time monitoring of residential BTM DERs. We utilize high-frequency (> 10kHz) conducted electromagnetic interference (EMI) from residential DERs’ grid-tied inverters to monitor their power generation. We discuss the working principle of our approach and present supporting results using three of-the-shelf grid-tied inverters.

14 SOLAR ENERGY↗
DOE OSTI2014

Measured Performance of Residential Dehumidifiers Under Cyclic Operation

Residential construction practices are progressing toward higher levels of energy efficiency. A proven strategy to save energy is to simultaneously increase building insulation levels and reduce outdoor air infiltration. Tight homes require intentional mechanical ventilation to ensure healthy indoor air. Overall, this strategy results in a shift in the mix of latent and sensible space conditioning loads, requiring proportionally more moisture to be removed compared to standard homes. There is currently not sufficient information available at a wide enough range of operating points to design dehumidification systems for high performance homes in hot-humid climates. The only industry information available on dehumidifier moisture removal and energy consumption are performance ratings conducted at a single test condition, which does not provide a full representation of dehumidifier operation under real-world conditions. Winkler et al. (2011) developed steady state performance maps to predict dehumidifier performance at a variety of indoor conditions. However, installed heating, ventilating, and air-conditioning (HVAC) equipment rarely operates at steady state. Part load performance testing of residential dehumidifiers is not mandated by current test standards. Therefore, we tested the part load performance of four residential dehumidifiers in the National Renewable Energy Laboratory's (NREL) Advanced HVAC Systems Laboratory . The part load efficiency of each dehumidifier was measured under 13 cycling scenarios, and combined with NREL field data to develop part load fraction (PLF) performance curves under realistic cycling scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Measured Performance of Residential Dehumidifiers Under Cyclic Operation

Residential construction practices are progressing toward higher levels of energy efficiency. A proven strategy to save energy is to simultaneously increase building insulation levels and reduce outdoor air infiltration. Overall, this strategy results in a shift in the mix of latent and sensible space conditioning loads, requiring proportionally more moisture to be removed compared to standard homes. In this project, researchers at the National Renewable Energy Laboratory (NREL) developed steady state performance maps to predict dehumidifier performance at a variety of indoor conditions. However, installed heating, ventilating, and air-conditioning (HVAC) equipment rarely operates at steady state. Part load performance testing of residential dehumidifiers is not mandated by current test standards. Therefore, the team tested the part load performance of four residential dehumidifiers in NREL’s Advanced HVAC Systems Laboratory . The part load efficiency of each dehumidifier was measured under 13 cycling scenarios, and combined with NREL field data to develop part load fraction (PLF) performance curves under realistic cycling scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

We report ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility's distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood's load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes' energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community's demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Analysis of heating load diversity in German residential districts and implications for the application in district heating systems

In recent years, the application of district heating systems for the heat supply of residential districts has been increasing in Germany. Central supply systems can be very efficient due to diverse energy demand profiles which may lead to reduced installed equipment capacity. Load diversity in buildings has been investigated in former studies, especially for the electricity demand. However, little is known about the influence of single building characteristics (such as building envelope or hot water demand) on the overall heating peak load of a residential district. For measuring the diversity, the peak load ratio (PLR) index is used to represent the percentage reduction of peak load of a district system from a simple sum of individual peak loads of buildings. A total of 144 residential building load profiles have been created with the dynamic building simulation software IDA ICE for a theoretical analysis in which the PLR reaches 15%. Within this study, certain district features are identified which lead to higher diversity. Furthermore, these results are used in a district heating simulation model which confronts the possible advantage of reduced installed capacity with the practical disadvantage of heat distribution losses. Likewise, the influence of load density and the districts building structure can be analyzed. This study shows that especially in districts with high load density, which consist of newly constructed buildings with low supply temperature and high influence of the hot water demand, the advantages of load diversity can be exploited.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Automated fault detection of residential air-conditioning systems using thermostat drive cycles

Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.

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↗