Smart building, which delivers useful services to residents at the lowest cost and maximum comfort, has gained increasing attention. HVAC equipment is a particularly expensive and energy consuming of building operation. A variety of emerging information technologies has been adopted. As a result, the concept of “demand-driven HVAC control” is a hot research topic. In this work, we investigated the issue of building occupancy estimation by using a wireless CO2 sensor network. The concentration level of indoor CO2 is a good indicator of the number of room occupants while protecting the personal privacy of building residents. Once indoor CO2 level is observed, HVAC equipment is aware of the number of room occupants. HVAC equipment can adjust its operation parameters to fit demands of these occupants. Thus, the desired quality of service is guaranteed with minimum energy dissipation. Excessive running of HVAC fans or pumps will be eliminated to conserve energy. Hence, the energy efficiency of smart buildings is improved significantly. The wireless sensor network was selected because it is tiny, cost effective, non-intrusive, easy to install and configure. We integrated CO2 and light sensors with a wireless sensor platform from Texas Instruments. Compare with existing occupancy detection methods, our proposed hybrid scheme achieves higher accuracy while keeping low cost and non-intrusiveness. Experimental results in an office environment show full functionality and validate benefits. This study paves the way for future research, where a wireless CO2 sensor network is connected with HVAC systems to realize fine-grained, energy efficient smart building.
Huang, Qian and Mao, Chen
"Occupancy Estimation in Smart Building Using Hybrid CO2/Light Wireless Sensor Network,"
Journal of Applied Sciences and Arts: Vol. 1
, Article 5.
Available at: https://opensiuc.lib.siu.edu/jasa/vol1/iss2/5