Cross-Room CO2-Based Presence Detection for Occupancy Profiling

Control systems for building services, such as heating and cooling, often rely on fixed timing schemes. While such approaches are convenient, they make strong assumptions about room usage, often leading to inadequate comfort and energy efficiency. This study addresses this limitation by presenting two contributions aimed at automating the configuration of building control systems. The first contribution involves the development of a presence detection model based on CO2 data, which is easy to measure and non-privacy intrusive. Unlike existing literature, which typically focuses on single-room applications, this work introduces a dataset and machine learning methodology demonstrating the generalizability of a presence detection model across various real-world rooms, even among different building types. Sliding window normalization of the sensor data is the key to achieve this unsupervised cross-room adaptability. As second contribution, we propose an occupancy profiling technique that relies on the predicted presence information. This approach facilitates the automated configuration of building control systems by using historical presence probabilities to anticipate future occupancy. In contrast to fixed timing schemes, these occupancy profiles dynamically adapt over time, accommodating changes in occupant behavior. As such, this work improves the configuration of building control systems, leading to a more comfortable and energy-efficient environment.