Abstract
This research presents a new occupancy schedule modeling system in a building information modeling (BIM) environment called stations-oriented indoor localization (SOIL). Occupant monitoring, data collection, and analysis are the main challenges when estimating occupancy patterns, especially in large-scale buildings. SOIL utilizes existing network infrastructure (Wi-Fi) and a clustering method to collect and sort occupancy data. These clusters are then mapped onto a BIM model to generate room-level occupancy schedules. In this research, the SOIL system was tested for 10 weekdays on 26 subjects and two operation scenarios in an educational building that included three office spaces and one open studio space. The results show that the SOIL system was able to detect indoor occupants' room-level locations with a 93% level of accuracy, and efficiently generate occupancy schedules in a BIM environment. The main contribution of this SOIL system is the development of a BIM-integrated occupant localization system using existing network infrastructure, a process that has great potential to save energy and assist in the efficient management of a building's renovation, construction, and operation.
•Development of a new building occupant localization system.•Use of an existing network infrastructure (Wi-Fi), clustering method, and BIM.•Elimination of dependence on the development of occupancy sensor technology.•Implementation of SOIL with 93% accuracy for room-level occupancy schedules.