Understanding the recurring patterns of occupants' energy-use behaviors at entry and departure events in office buildings

Publication date: 15 May 2018
Source:Building and Environment, Volume 136
Author(s): Hamed Nabizadeh Rafsanjani, Changbum Ryan Ahn, Kent M. Eskridge
Office-building occupants' behaviors during their arrivals and departures foreseeably have a large impact on a building's energy consumption since many occupants in control of appliances will turn their devices on or off at these entry and departure events. Consequently, occupants would have various types of energy-use patterns that coincide with their entry and departure events and that repeat over time. Despite the value that knowledge of such patterns would have on better tracking energy-use behaviors, these patterns have not been well explored with empirical data in the literature. Therefore, this paper studies occupants' energy-use behaviors in office buildings to identify and investigate energy-use patterns at entry and departure events. In particular, this research evaluates (1) the delay intervals that manifest between the occupants' entry/departure events and the beginning/end of the occupants' energy-consuming behaviors, and (2) changes in electricity consumption caused by occupants at entry/departure events to identify recurring—and thereby predictable—energy-use patterns associated with individual occupants. In the pursuit of this objective, the energy-use behaviors of 12 occupants in two office buildings were tracked during a four-month period. Results from statistical analyses performed on the collected data reveal that an occupant in an office building typically follows a consistent, recurring delay-interval pattern. In addition, the results show each occupant also follows a recurring pattern of power changes at entry/departure events. By identifying recurring, occupant-specific energy-use behavior patterns, this study significantly contributes to the current body of research and can be used to support research efforts into energy-load disaggregation.