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

Mitra, Debrudra; Chu, Yiyi; Cetin, Kristen

doi:10.1016/j.enbuild.2021.110791

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

Journal Article · Tue Feb 02 00:00:00 EST 2021 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2021.110791· OSTI ID:1848385

Mitra, Debrudra ^[1]; Chu, Yiyi ^[2]; Cetin, Kristen ^[2]

Michigan State Univ., East Lansing, MI (United States). Dept. of Civil and Environmental Engineering; Michigan State Univ., East Lansing, MI (United States)
Michigan State Univ., East Lansing, MI (United States). Dept. of Civil and Environmental Engineering

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.

View Accepted Manuscript (DOE)

Research Organization:: Michigan State Univ., East Lansing, MI (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0001288

OSTI ID:: 1848385

Alternate ID(s):: OSTI ID: 1924160

Journal Information:: Energy and Buildings, Journal Name: Energy and Buildings Journal Issue: C Vol. 236; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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