Exploring occupant detection model generalizability for residential buildings using supervised learning with IEQ sensors

Abstract

This study explores two modeling approaches for occupancy detection at room level for residential buildings in Denmark. The aim is to assess the performance and generalizability of occupant detection models using XGBoost method trained on a rich dataset comprising indoor environmental quality (IEQ) variables and occupancy ground truth. A global approach (all rooms) and a room-specific approach are considered. After a thorough feature- selection and importance analysis process, the occupancy detection models (ODMs) are trained and tested in a nested cross-validation schema. The time of the day, indoor CO2 concentration, and feature transforms related to short-term IEQ dynamics were found to be the most important features of the ODMs. Both the global and room-specific models show good occupancy prediction performance, especially for bedrooms. When tested for generalizability with an unseen dataset from a different residential building, the ODMs maintain very good performance for the bedroom but not for the office room. This discrepancy could be explained by significant differences in occupancy and ventilation patterns, and large air infiltration from adjacent rooms. Although currently limited in terms of generalizability, XGBoost-based ODMs using IEQ data have the potential to provide robust and scalable occupancy detection for occupant-centric control and occupant-aware building performance assessments. The IEQ dataset with occupancy ground truth collected for this study is made available in open access.