Sensing System for Cooking Event Detection Designed to Control Indoor Air Quality

Giving consideration to cooking activity is important for sustainable housing. In contexts of limited ventilation, imposed by energy saving concerns, cooking causes deterioration of indoor air quality (IAQ) and occupants’ discomfort. This study presents a cooking event detection system that may support IAQ control to minimize the impact of cooking. The system consists of a multi-sensor device and a deep-learning neural network (DNN). The device monitors temperature (T), relative humidity (RH), suspended particulate matter (PM), CO2, the responses of sensors to volatile organic compounds (VOCs), and other gases (NO2, CO, CH2O) in the kitchen zone. The collected data are processed by the DNN. The detection system generates a response every 7 s, indicating either ’COOKING’ or ’NO COOKING’. Feature vector selection was based on classification performance and cost considerations. Cooking event misdetections generate unjustified IAQ control costs: economic ones (UEC), when the system detects a non-existent event, and environmental ones (UEN), when the system fails to detect an actual event. In this study, several well-performing detection systems were developed, with miss rates ranging from 5.1% to 20.5% and false detection rates ranging from 7.7% to 11.7%. The results show that gas sensor responses—particularly to VOCs—had greater utility for cooking event detection compared with T, RH, CO2, and PM. The cost analysis demonstrated that IAQ control supported by the developed cooking event detection systems could generate higher total unjustified environmental costs when the unit cost ratio UEN/UEC exceeded 1.25, or higher total unjustified economic costs when the unit cost ratio UEN/UEC was below 1.43. We believe this work will contribute to the development of novel automatic IAQ control systems supported by event detection.