Non-invasive occupancy estimation and space utilization in smart buildings: Leveraging machine learning with PIR sensors and booking data

Azad Shokrollahi, Fredrik Karlsson, Reza Malekian, Jan A. Persson, Arezoo Sarkheyli-Hägele,
Non-invasive occupancy estimation and space utilization in smart buildings: Leveraging machine learning with PIR sensors and booking data,
Internet of Things,
Volume 34,
2025,
101777,
ISSN 2542-6605,
https://doi.org/10.1016/j.iot.2025.101777.
(https://www.sciencedirect.com/science/article/pii/S2542660525002914)
Abstract: Occupancy estimation in smart buildings is essential for optimizing resource usage and enhancing operational efficiency. Existing estimation methods predominantly rely on cameras or advanced sensor fusion techniques, which, while accurate, are often expensive, invasive, and raise privacy concerns. Additionally, these approaches frequently require extra hardware, increasing installation complexity and operational costs. A significant gap in the literature lies in the limited use of existing smart building infrastructure, such as detection systems and booking data, for people counting. This study addresses these limitations by exclusively utilizing two binary PIR sensors (in-door and in-room) and booking data. Since PIR sensors and booking systems are already integrated into most smart building infrastructures, leveraging these existing resources helps reduce costs and simplifies implementation. The primary goal is to estimate the number of people between each in-door sensor trigger using machine learning models by incorporating people counting levels and time thresholds. Among the evaluated machine learning algorithms, the Extra Trees Classifier delivered strong performance, achieving 68.5% accuracy when the estimated occupancy differed from the actual count by at most one person, and 81.56% with a tolerance of two. These results are based on periods when the room was occupied. When both occupied and unoccupied periods were included, the accuracy was 96.10% for ±1 tolerance. Moreover, incorporating booking data enhanced people counting accuracy by 4%. The study also explores the method’s ability to identify underutilization and overutilization by comparing estimated occupancy with booking records and seating capacity, thereby supporting enhanced space management in smart buildings.
Keywords: Smart buildings; Machine learning; Occupancy estimation; People counting; PIR sensors; Booking information; Space utilization