Smart building, which delivers useful services to residents at lowest cost and maximum comfort, has gained increasing attention in recent years. A variety of emerging information technologies have been adopted in modern buildings, such as wireless sensor networks, internet of things, big data analytics, deep machine learning, etc. Most people agree that a smart building should be energy efficient, and consequently, much more affordable to building owners. Building operation accounts for major portion of energy consumption in the United States. HVAC (heating, ventilating, and air conditioning) equipment is a particularly expensive and energy consuming of building operation.
As a result, the concept of “demand-driven HVAC control” is currently a growing research topic for smart buildings. 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 building is improved significantly and the building operation becomes more intelligent.
The wireless sensor network was selected for this study, because it is tiny, cost effective, non-intrusive, easy to install and flexible to configure. In this work, we integrated CO2 and light senors 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.
Effectiveness of demand-driven HVAC control heavily depends on accurate occupancy information and measurement. Occupancy information has a big impact on dynamic optimization of HVAC operation parameters and set points. Numerous types of sensors have been used in literature in the past decade to detect occupancy information. In this section, we will briefly overview the advantages and drawbacks of these existing approaches.
SYSTEM ARCHITECTURE AND MEASUREMENTS
Figure 2 depicts the prototype implementation of the proposed system architecture. It is obvious that the CO2 and light sensors are connected with TI wireless sensor node through wires. The control computer receives the measurement results via 2.4GHz wireless channel. Note the central control computer does not have to be placed very close to the sensor node. According to prior measurement data (Huang, 2010), wireless transmission distance between this sensor node and central control computer can reach up to 16 meters.
In practice, the distance between light sensor and walking person is different. To investigate impacts of this phenomenon, we setup test instruments as shown in Figure 5. A LED lamp is used to mimic lighting source near door frame. The proposed hybrid sensor and a digital illuminance/Lux meter are vertically attached to a hardboard. When distance between the LED lamp and the hardboard is adjusted, ambient lighting condition changes as well as output voltage of the light sensor.
Smart building has great potential to increase comforts and quality of life, while significantly reducing energy usage and cost. Room occupancy is important information, which helps to realize energy-efficient demand-driven HVAC operation. Existing building occupancy detection or estimation methods can not meet all the requirements of low cost, high accuracy and privacy. Therefore, in this work, a hybrid CO2/light sensor is proposed for more accurate occupancy estimation. Our proposed solution is cost-effective, non-intrusive and suppresses temporal disturbances. The entire system has been assembled and tested experimentally in an office building. The measurement results validate the functionality and benefits.
Source: Southern Illinois University Carbondale
Authors: Chen Mao | Qian Huang