Occupant-Centric Control of Split Air Conditioners, Personal Desktop Fans and Lights Based on Wi-Fi Probe Technology

Although research on occupancy detection has been extensive, most studies have focused on improving detection accuracy, while the application of occupancy models to device control remains limited. This paper presented an occupant-centric control (OCC) of split air conditioners, personal desktop fans, and lights based on the Wi-Fi probe technology, Internet of Things (IoT), and time-of-use (TOU) electricity rates. Firstly, a machine-learning model based on Wi-Fi signals for occupancy detection was developed. The occupancy detection model integrated convolutional neural networks, gradient boosting classifier, and random forest (CNN-GBC-RF) models to identify four types of occupant statuses: arrival, leave, stay, and outside. Subsequently, the occupancy status and TOU rates information were integrated with IoT-enabled devices to dynamically control the space split air conditioners, personal desktop fans, and personal lights. This study implemented two experimental scenarios: a baseline scenario with fixed device operations and an OCC scenario. The OCC scenario dynamically adjusted device operation based on real-time occupancy status, TOU electricity rates, desktop illuminances, and dry-bulb air temperatures. Finally, a 16-day experiment was conducted in a multi-occupant office room to evaluate both occupancy detection model performance and the effectiveness of the proposed OCC-based scenario. Subjective questionnaires were collected to evaluate the thermal comfort under different scenarios. The results showed that the CNN-GBC-RF model had an overall detection accuracy of 97.0%. Additionally, the OCC-based scenario achieved a relative reduction of 39.9%, a relative reduction of 41.6% compared to the baseline scenario. The thermal comfort of the occupants under the OCC scenario was close to the baseline scenario. The results indicated that the proposed OCC-based control scenario contributed to improving energy efficiency while maintaining occupant thermal comfort.