Abstract
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.
Highlights
According to the U.S Energy Information Administration (EIA, 2019), building energy consumption accounts for 20% of global energy usage, and a large part of electricity in buildings is used for heating, ventilation, and air conditioning (HVAC)
We explore the design of next-generation convolutional neural networks (CNN)-based visual recognition air conditioner
The average estimation errors for distances and angles are 17% and 10%, respectively. These results indicate that the use of the YOLO neural network in the Rockchip RK3399 computing platform is an appropriate choice for building occupancy detection and positioning applications
Summary
According to the U.S Energy Information Administration (EIA, 2019), building energy consumption accounts for 20% of global energy usage, and a large part of electricity in buildings is used for heating, ventilation, and air conditioning (HVAC). In order to take into account the impact of the number of occupants and activities, the researchers of Lim et al (2016) presented an online HVAC-aware occupancy scheduling scheme Their experimental study showed that HVAC operation can save up to 12% of energy. As a basic research area in the field of computer vision, object recognition tries to identify what objects exist in images, and report the position and orientation of these objects in the images. These objects may be human bodies, faces, cars, or animals, etc. Special features for heads, arms, legs, torso, skin color, and arm distance can be used for human body recognition
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