INTRODUCTION
Thermostats allow customized indoor climate control within buildings and specific rooms. The first programmable thermostat debuted in 1906, produced by Honeywell [1], and the concept soon caught on as a method to provide increased comfort and energy savings. Recently, reactive thermostats were developed to incorporate real-time data on occupancy detection. HVAC systems often operate based on maximum room capacity to avoid ensure that the room will always be ready for comfortable occupation. Efficiency can be increased by basing HVAC output on actual occupancy [2]. 39% of US domestic building energy is used by temperature control of unoccupied areas [3]. One study done in 2010 showed 10 to 15% savings in HVAC by utilizing reliable occupancy data [4]. Occupancy detection does not yet provide data with the level of reliability from this study, but ongoing research is being conducted to improve reliability.
Occupancy detection uses various sensors to determine if humans are present in the rooms with HVAC operation and reduce or altogether stop system output in unoccupied rooms. This method has the potential to reduce energy consumption, but actual consumption during some testing scenarios increased due to an inability of HVAC systems to quickly and efficiently respond to occupancy increases, showing that further progress is needed [5].
One way to counteract the negative aspects of reactive thermostats is to combine them with programmable thermostats to produce smart thermostats [5]. Smart thermostats allow HVAC systems to have a deep setback temperatures [Footnote 1] with temperature adjustments in a compressed time frame which result in greater energy savings than longer setback periods [5]. The reactive aspect allows the HVAC system to respond to occupancy changes before air temperature fluctuations can be detected.
One way to counteract the negative aspects of reactive thermostats is to combine them with programmable thermostats to produce smart thermostats [5]. Smart thermostats allow HVAC systems to have a deep setback temperatures [Footnote 1] with temperature adjustments in a compressed time frame which result in greater energy savings than longer setback periods [5]. The reactive aspect allows the HVAC system to respond to occupancy changes before air temperature fluctuations can be detected.
Reactive thermostats, and therefore also smart thermostats, can incorporate occupancy detection through predictive models or through real time data collection. Data can be collected with a variety of sensors. The most common types are Pyroelectric Infrared (PIR), ultrasonic, photoelectric sensors, and thermal imaging. A comparison of the sensor types is located on the page titled Sensor Technology.
Predictive models use the number people expected to be in a room have been generated and implemented for small scale testing for buildings with high occupancy, such as office buildings [2]. Real time data collection is currently used in many thermostats designed for homes and other buildings with low occupancy. This type of smart thermostat normally uses PIR sensors to detect occupancy near the thermostat to determine if the room is occupied or not on a binary option basis. If the room becomes occupied, the HVAC system increases its output to reach the desired temperature from the setback temperature [6]. Occupancy detection allows systems to have different target temperatures for occupied and unoccupied rooms, unlike only programmable thermostats [7]. PIR sensors are a well-established technology in major producers such as Honeywell, Viconics, and Entergize.
Smart thermostats are an established technology proven to improve HVAC efficiency. Businesses have higher energy consumption to operate HVAC systems, so potential benefits drastically increase. Thermostats that effectively incorporate occupancy detection for large environments do not yet exist on the market, however, they are the subject of many research projects. The most promising method of occupancy detection uses PIR sensors in a similar, but much more complex, manner to that of home-use smart thermostats. Smart thermostats using occupancy detection have the possibility to become a source of increased HVAC efficiency for businesses if the technology and programming are fully developed.
Predictive models use the number people expected to be in a room have been generated and implemented for small scale testing for buildings with high occupancy, such as office buildings [2]. Real time data collection is currently used in many thermostats designed for homes and other buildings with low occupancy. This type of smart thermostat normally uses PIR sensors to detect occupancy near the thermostat to determine if the room is occupied or not on a binary option basis. If the room becomes occupied, the HVAC system increases its output to reach the desired temperature from the setback temperature [6]. Occupancy detection allows systems to have different target temperatures for occupied and unoccupied rooms, unlike only programmable thermostats [7]. PIR sensors are a well-established technology in major producers such as Honeywell, Viconics, and Entergize.
Smart thermostats are an established technology proven to improve HVAC efficiency. Businesses have higher energy consumption to operate HVAC systems, so potential benefits drastically increase. Thermostats that effectively incorporate occupancy detection for large environments do not yet exist on the market, however, they are the subject of many research projects. The most promising method of occupancy detection uses PIR sensors in a similar, but much more complex, manner to that of home-use smart thermostats. Smart thermostats using occupancy detection have the possibility to become a source of increased HVAC efficiency for businesses if the technology and programming are fully developed.
Finally, the inclusion of opportunistic forms of data can supplement the above technology to increase the accuracy of occupancy detection. Utilizing the multitudes of data already present in most workplaces fine tunes the system and allows for estimations to be made in large environments otherwise untested and unaffected by the benefits of occupancy detection.
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Footnotes:
[1] Setback Temperature is the baseline temperature for an unoccupied room. Deep setbacks have a larger difference between occupied and unoccupied target temperatures than shallow setbacks.
[1] Setback Temperature is the baseline temperature for an unoccupied room. Deep setbacks have a larger difference between occupied and unoccupied target temperatures than shallow setbacks.
Sources:
[1] "100 Years of Programmable Thermostats," [online], December 8, 2016, National Trade Supply Company, LLC, website: http://www.prothermostats.com/history.php.
[2] Erickson, V. L., and Cerpa, A. E., "Occupancy based demand response HVAC control strategy," presented at the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland (November 2, 2010).
[3] Meyers, Richard. J., et al, "Scoping the potential of monitoring and control technologies to reduce energy use in homes," Energy and Buildings, 42 (5), pp. 563-569 (2010).
[4] Agarwal, R. G., et al, "Occupancy-driven energy management for smart building automation," presented at the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland (November 2, 2010)
[5] Lu, J., et al., "The Smart Thermostat: Using Occupancy Sensors to save Energy in Homes," presented at the 8th ACM Conference on Embedded Networked Sensor Systems, Zurich, Switzerland (November 3, 2010).
[6] Huppi, B., et al, "System and method for integrating sensors in thermostats," US Patent 8727611 B2, issued May 20, 2014.
[7] Disser, J., "Method and apparatus for reducing energy consumption in heating, ventilating, and air conditioning of unoccupied building zones," US Patent 20020134849 A1, issued September 26, 2002.
[1] "100 Years of Programmable Thermostats," [online], December 8, 2016, National Trade Supply Company, LLC, website: http://www.prothermostats.com/history.php.
[2] Erickson, V. L., and Cerpa, A. E., "Occupancy based demand response HVAC control strategy," presented at the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland (November 2, 2010).
[3] Meyers, Richard. J., et al, "Scoping the potential of monitoring and control technologies to reduce energy use in homes," Energy and Buildings, 42 (5), pp. 563-569 (2010).
[4] Agarwal, R. G., et al, "Occupancy-driven energy management for smart building automation," presented at the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland (November 2, 2010)
[5] Lu, J., et al., "The Smart Thermostat: Using Occupancy Sensors to save Energy in Homes," presented at the 8th ACM Conference on Embedded Networked Sensor Systems, Zurich, Switzerland (November 3, 2010).
[6] Huppi, B., et al, "System and method for integrating sensors in thermostats," US Patent 8727611 B2, issued May 20, 2014.
[7] Disser, J., "Method and apparatus for reducing energy consumption in heating, ventilating, and air conditioning of unoccupied building zones," US Patent 20020134849 A1, issued September 26, 2002.