Demand controlled ventilation

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One of the biggest challenges facing the designers of heating, ventilation, air conditioning (HVAC) systems is providing the correct level of heating/cooling/lighting for the actual occupancy.[1] Demand-controlled ventilation (DCV, also known as Demand Control Ventilation) aims to provide a solution to this problem. The theory behind this concept has been around for over 20 years. But not having a cost effective, simple, and reliable sensor has always been a barrier to its full implementation.[2] A single method can be used or a combination.[3]

Examples

Current examples of systems used are:

Schedules

Schedules have been used in HVAC systems for decades to predict building occupancy. Office buildings, for example, are generally only occupied weekdays. During nights, weekends, and holidays, reducing ventilation, heating, and cooling down to their bare minimums saves considerable energy costs over continuously running them at full capacity.

However, schedules must be properly configured to account for holidays and adjust for Daylight Saving Time. On a zone level (e.g., an individual office), furthermore, schedules alone cannot compensate for employee vacations, sick days, business trips, or other disruptions to normal routines.[4]

Motion sensors

A study on Norwegian schools found that using IR motion sensors to control DCV systems reduced energy consumption by 49% when compared with a standard CAV system.[5][6]

Motion sensors verify whether or not occupants really are in office spaces during the predicted scheduled time. If no motion is detected within a set time, action is taken, such as changing the zone's setpoint to reduce the energy usage.

In a more sophisticated implementation of motion sensing (adaptive occupancy), a building automation system uses motion sensor detections to self-program a schedule over time, but the motion sensors still daily verify the self-taught schedule.

Motion sensors can only determine whether-or-not at least one person is in a particular space, but the ventilation needs of one person are very different from the needs of numerous people.[7]

CO2 sensors

CO2 sensors are popularly used to measure occupancy level.[8][9] In a survey on Norwegian schools, using CO2 sensors for DCV was found to reduce energy consumption by 62% when compared with a constant air volume (CAV) ventilation system.[10]

Some room thermostats integrate schedules, motion sensing, and CO2 sensing (as well as standard temperature and humidity sensing) to optimize energy savings and indoor air quality.[11] Measuring CO2 is much more difficult than measuring temperature or humidity, and CO2 sensors are more complicated than temperature or humidity sensors. Implementation of CO2 sensors had obstacles:

  • Early sensors were often overly sensitive to temperature and humidity.[12]
  • Accuracy of good quality sensors may be +/- 75ppm and poor ones as low as +/- 150ppm. A typical room will have a CO2 set point of around 375 - 1000ppm, this means potentially an error ranging from 5 – 40% [13][14]) before other operational errors are added.[15]
  • CO2 sensors may need to be calibrated one or more times per year.[16] This may also involve accessing, un-mounting, calibrating using test gases, remounting the sensor, which could take days for the whole system. Potentially factory calibration may be necessary involving the removal of sensors for a number of days.[17] In spaces that are unoccupied at regular intervals, however, some types of sensors can routinely self-calibrate and maintain accuracy (without human intervention) during the life of the sensor.[18] During unoccupied times in typical office buildings, for example, the CO2 levels inside drop to that of outside air. Such sensors can track these low settings over time and recalibrate themselves based on the reference levels.

DCV systems must be properly designed. In improperly designed systems, CO2 sensing can result in chronic underventilation. Even at very low levels of building occupancy, a minimum ventilation rate must be maintained to eliminate building pollutants created by the site, building, equipment, and furnishings (e.g., VOCs, radon, ozone). Also, CO2 sensors mounted only in return ducts leading from multiple spaces will not properly measure spaces with excessive levels of CO2 (e.g., a crowded meeting room) when "averaged" with other spaces with low occupancy (e.g., a private office).[19]

Sound

Studies have been conducted concerning the feasibility of using acoustic sensing and processing to control building automation.[20] In October 2011, a project began,[21] partially funded by the European Commission under grant no. 284628 Sounds for Energy-efficient Buildings (S4EeB).[22][23] It is investigating the feasibility of DCV through monitoring sound in buildings to determine the occupancy level.[24]

Video

Studies have been conducted concerning the feasibility of "people counting" via inexpensive video cameras (e.g., web cams or low resolution thermal imagers) and software to compute occupancy levels of rooms.[25][26]

References

  1. ^ Dwyer, T. (2009). CPD - September 09: Sensing the Need for Demand Controlled Ventilation | Chartered Institution of Building Services Engineers - CIBSE Journal. Retrieved October 4, 2012, from http://www.cibsejournal.com/cpd/2009-09/
  2. ^ Stipe, M. (2003). Demand-Controlled Ventilation: A Design Guide. Oregon Office of Energy.
  3. ^ Pavlovas, V. (2004). Demand Controlled Ventilation. Energy and Buildings, 36(10), 1029–1034. Retrieved October 9, 2012,
  4. ^ KMC Controls. (2013). Demand Control Ventilation Benefits for Your Building. Retrieved 25 March 2013, from http://www.kmccontrols.com/docs/DCV_Benefits_White_Paper_KMC_RevB.pdf
  5. ^ Mysen, M., Berntsen, S., Nafstad, P. & Schild, P. G. (2005). Occupancy Density and Benefits of Demand-controlled Ventilation in Norwegian Primary Schools. Energy and Buildings, 37(12), 1234–1240. Retrieved October 9, 2012,
  6. ^ SINTEF. "reDuCeVentilation: Reduced energy use in Educational buildings with robust Demand Controlled Ventilation". Retrieved 26 March 2013.
  7. ^ KMC Controls. (2013). Demand Control Ventilation Benefits for Your Building. Retrieved 25 March 2013, from http://www.kmccontrols.com/docs/DCV_Benefits_White_Paper_KMC_RevB.pdf
  8. ^ Federal Energy Management Program. (2004). Demand-controlled Ventilation Using CO2 Sensors. Federal Energy management Program. Retrieved from http://www1.eere.energy.gov/femp/pdfs/fta_co2.pdf
  9. ^ American Society of Heating, Refrigerating and Air-Conditioning Engineers, The American Institute of Architects, Illuminating Engineering Society of North America, U.S. Green Building council & U.S. Department of Energy. (2011). Advanced Energy Design Guide for K-12 School Buildings.
  10. ^ Mysen, M., Berntsen, S., Nafstad, P. & Schild, P. G. (2005). Occupancy Density and Benefits of Demand-controlled Ventilation in Norwegian Primary Schools. Energy and Buildings, 37(12), 1234–1240. Retrieved October 9, 2012.
  11. ^ KMC Controls. "Like a Breath of Fresh Air: Using CO2 Sensors with Demand Control Ventilation". Retrieved 25 March 2013.
  12. ^ Dwyer, T. (2009). CPD - September 09: Sensing the Need for Demand Controlled Ventilation | Chartered Institution of Building Services Engineers - CIBSE Journal. Retrieved October 4, 2012, from http://www.cibsejournal.com/cpd/2009-09/
  13. ^ Aircuity. (2007). A Review of the Unique Requirements for a Facility Monitoring System. Retrieved from http://www.aircuity.com/wp-content/uploads/7d-Unique-Requirements-for-a-Facility-Monitoring-System.pdf
  14. ^ Mysen, M., Berntsen, S., Nafstad, P. & Schild, P. G. (2005). Occupancy Density and Benefits of Demand-controlled Ventilation in Norwegian Primary Schools. Energy and Buildings, 37(12), 1234–1240. Retrieved October 9, 2012,
  15. ^ Damiano, L. (2003). Issues Concerning the Difficulties in Applying DCV with CO2 Sensors. Retrieved from http://www.energy.ca.gov/title24/2005standards/archive/rulemaking/documents/public_comments/10-14-03_EBTron_DCV&CO2%20.PDF
  16. ^ CETCI. (2012). Critical Environment Technologies Canada Inc. - Gas Detection Experts - FAQ - Fixed Gas Detection Systems. Retrieved October 9, 2012, from http://www.critical-environment.com/support/technical-library/faq-fixed-gas-detection-systems.html
  17. ^ Aircuity. (2007). A Review of the Unique Requirements for a Facility Monitoring System. Retrieved from http://www.aircuity.com/wp-content/uploads/7d-Unique-Requirements-for-a-Facility-Monitoring-System.pdf
  18. ^ Telaire. "Application Note: Telaire's ABCLogic Self Calibration Feature". Retrieved 1 April 2013.
  19. ^ Mumma, S. A. (2002). Demand Controlled Ventilation. American Society of Heating. Retrieved October 9, 2012, from http://www.hekta.org/~hp04-72/Dokumenter/IAQ7.pdf
  20. ^ S4ECoB Sounds for Energy Control of Buildings. "Acoustic sensing, computing and processing". Retrieved 1 April 2013.{{cite web}}: CS1 maint: numeric names: authors list (link)
  21. ^ Vukovic. (2012). Sounds for Energy Efficient Buildings Smartcode Workshop. Retrieved from https://www.fp7-smartcode.eu/system/files/ct_publication/1415_Barona.pdf
  22. ^ Vukovic. (2012). Sounds for Energy Efficient Buildings Smartcode Workshop. Retrieved from https://www.fp7-smartcode.eu/system/files/ct_publication/1415_Barona.pdf
  23. ^ Muficata. (2011a). S4Eeb.eu - 284628- Sounds for Energy-efficient Buildings FP7-ICT7 PPP. Retrieved October 9, 2012, from http://s4eeb.eu/
  24. ^ S4Eeb. (2011). The Project at a Glance — S4EeB. Retrieved March 28, 2013, from http://s4eeb.eu/content/s4eeb#attachments
  25. ^ University of California, Merced. "Occupancy Measurement, Modeling and Prediction for Energy Efficient Buildings". Retrieved 26 March 2013.
  26. ^ Lawrence Berkeley National Laboratory. "Carbon Dioxide Measurement & People Counting for Demand Controlled Ventilation". Retrieved 26 March 2013.
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