Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator

Jung, Wooyoung; Wang, Zhe; Hong, Tianzhen; Jazizadeh, Farrokh

doi:10.1016/j.buildenv.2023.110628

Smart thermostat data-driven U.S. residential occupancy schedules and development of a U.S. residential occupancy schedule simulator

Journal Article · Tue Jul 18 00:00:00 EDT 2023 · Building and Environment

DOI:https://doi.org/10.1016/j.buildenv.2023.110628· OSTI ID:2294049

^[1]; ^[2]; ^[3]; Jazizadeh, Farrokh ^[4]

Univ. of Arizona, Tucson, AZ (United States)
Hong Kong University of Science and Technology (HKUST) (Hong Kong); HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute (China)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. In conclusion, the derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2294049

Journal Information:: Building and Environment, Journal Name: Building and Environment Vol. 243; ISSN 0360-1323

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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