Stochastic residential occupancy schedules based on the American Time-Use Survey

Abstract

The energy performance of residential buildings is closely correlated with occupants’ behavior and their schedules. Moreover, the energy consumption of heating, ventilation, and air conditioning (HVAC) systems, which are by far the biggest contributor to energy consumption in residential buildings, is controlled by the presence or absence of occupants in a building. Thus, accurate occupancy presence and activity profiles are important to determine actual energy demands and corresponding control schedules for residential buildings. Conventionally, building energy simulation tools typically use a single generic and static occupancy profile to represent a building’s occupancy schedule, regardless of day type or household size. However, literature in the field suggests that there is significant potential for improvement to allow for more flexibility and accuracy in the calculation of occupancy. The objective of this study is thus to develop a stochastic building occupancy model and propose it as a realistic replacement for the conventional generic static schedules. This three-state stochastic occupancy model is based on the 2019 American Time-Use Survey (ATUS) and considers the differences among weekdays, Saturdays, and Sundays. In this model, survey respondents are clustered based on the number of residents in their household and a first-order inhomogeneous Markov chain technique is used to generate occupancy presence schedules. The models’ results are then validated against the original ATUS data in terms of state probabilities, state durations, and number of state changes throughout the course of a day.