Jump to content

Demand controlled ventilation

Add links
From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by IsoFocus (talk | contribs) at 10:37, 18 March 2015 (It is clear the equipment vendors go a hold of this article - much commercialism removed). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Demand Controlled Ventilation (DCV) is automatic adjustment of ventilation equipment to meet occupant demand. DCV is a control method used to modulate the volume of fresh air taken into a building or other occupied space by mechanical air conditioning equipment. Sensors or time schedules are used to estimate ventilation need and automated control loops are used to meet a set point determined by a design engineer. The design engineer references a codified standard when determining ventilation set points.

There is a significant energy saving potential in rigorous outdoor air control.[1]Cite error: A <ref> tag is missing the closing </ref> (see the help page).

  • Gas detection (CO2)</ref> 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.[2]
  • positive control gates
  • ticketing sales
  • Security equipment data share (including people counting video software)[3][4]
  • Inference from other system sensors/equipment

Sound

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.

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]Cite error: A <ref> tag is missing the closing </ref> (see the help page). 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.[9]
  • 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% [10][11]) before other operational errors are added.[12]
  • CO2 sensors may need to be calibrated one or more times per year.[13] 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.[14] 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.[15] 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).[16]

Sound

Studies have been conducted concerning the feasibility of using acoustic sensing and processing to control building automation.[17] In October 2011, a project began,[18] partially funded by the European Commission under grant no. 284628 Sounds for Energy-efficient Buildings (S4EeB).[19][20] It is investigating the feasibility of DCV through monitoring sound in buildings to determine the occupancy level.[21]


References

  1. ^ 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
  2. ^ 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.
  3. ^ University of California, Merced. "Occupancy Measurement, Modeling and Prediction for Energy Efficient Buildings". Retrieved 26 March 2013.
  4. ^ Lawrence Berkeley National Laboratory. "Carbon Dioxide Measurement & People Counting for Demand Controlled Ventilation". Retrieved 26 March 2013.
  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. ^ 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/
  10. ^ 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
  11. ^ 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,
  12. ^ 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
  13. ^ 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
  14. ^ 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
  15. ^ Telaire. "Application Note: Telaire's ABCLogic Self Calibration Feature". Retrieved 1 April 2013.
  16. ^ 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
  17. ^ 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)
  18. ^ Vukovic. (2012). Sounds for Energy Efficient Buildings Smartcode Workshop. Retrieved from https://www.fp7-smartcode.eu/system/files/ct_publication/1415_Barona.pdf
  19. ^ Vukovic. (2012). Sounds for Energy Efficient Buildings Smartcode Workshop. Retrieved from https://www.fp7-smartcode.eu/system/files/ct_publication/1415_Barona.pdf
  20. ^ Muficata. (2011a). S4Eeb.eu - 284628- Sounds for Energy-efficient Buildings FP7-ICT7 PPP. Retrieved October 9, 2012, from http://s4eeb.eu/
  21. ^ S4Eeb. (2011). The Project at a Glance — S4EeB. Retrieved March 28, 2013, from http://s4eeb.eu/content/s4eeb#attachments
Retrieved from "https://en.wikipedia.org/w/index.php?title=Demand_controlled_ventilation&oldid=651909618"