Demand-driven heating, ventilation, and air conditioning (HVAC) operations have become very attractive in energy-efficient smart buildings. Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. So far, existing building occupancy detection methods have their disadvantages, and cannot fully meet the expected performance. To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify the number of occupants and their locations in buildings. The proposed study can detect the quantity, distance, and angle of indoor human users, which is essential for controlling air-conditioners to adjust the direction and speed of air blow. Compared with the state of the art, the proposed method successfully fulfills the function of building occupant counting, which cannot be realized when using PIR, sound, and carbon dioxide sensors. Our method also achieves higher accuracy in detecting moving or stationary human bodies and can filter out false detections (such as animal pets or moving curtains) that are existed in previous solutions. The proposed idea has been implemented and collaboratively tested with air conditioners in an office environment. The experimental results verify the validity and benefits of our proposed idea.
Development of CNN-based visual recognition air conditioner for smart buildings
