Demand controlled ventilation
Demand controlled ventilation (DCV) is a feedback control method to maintain indoor air quality that automatically adjusts the ventilation rate provided to a space in response to changes in conditions such as occupant number or indoor pollutant concentration. The control strategy is mainly intended to reduce the energy use by heating, cooling, and ventilation systems compared to buildings that use open-loop controls with constant ventilation rates.
Common reference standards for ventilation:
- ISO ICS 91.140.30: Ventilation and air-conditioning systems
- ASHRAE 62.1 & 62.2: The standards for Ventilation and Indoor Air Quality
Examples of estimating occupancy
- Timed schedules
- Motion sensors (various technologies including: Audible sound, inaudible sound, infrared)
- Gas detection (CO2) 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.
- Positive control gates
- Ticket sales
- Security equipment data share (including people counting video software)
- Inference from other system sensors/equipment, like smart meters
Carbon dioxide sensing
Carbon dioxide sensors monitor carbon dioxide levels in a space by strategic placement. The placement of the sensors should be able to provide an accurate representation of the space, usually placed in a return duct or on the wall. As the sensor reads the increasing amount of carbon dioxide levels in a space, the ventilation increases to dilute the levels. When the space is unoccupied, the sensor reads normal levels, and continues to supply the unoccupying rate for airflow. The amount of air supplied is determined by the building owner standards, along with the designer and ASHRAE Standard 62.1.
- "Demand Control Ventilation Benefits for Your Building" (PDF). KMC Controls. 2013.
- Mysen, Mads; Berntsen, Sveinung; Nafstad, Per; Schild, Peter G. (December 2005). "Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools". Energy and Buildings. 37 (12): 1234–1240. doi:10.1016/j.enbuild.2005.01.003.
- Jin, Ming; Bekiaris-Liberis, Nikolaos; Weekly, Kevin; Spanos, Costas J.; Bayen, Alexandre M. (April 2018). "Occupancy Detection via Environmental Sensing". IEEE Transactions on Automation Science and Engineering. 15 (2): 443–455. doi:10.1109/tase.2016.2619720.
- University of California, Merced. "Occupancy Measurement, Modeling and Prediction for Energy Efficient Buildings". Retrieved 26 March 2013.
- Lawrence Berkeley National Laboratory. "Carbon Dioxide Measurement & People Counting for Demand Controlled Ventilation". Retrieved 26 March 2013.
- Jin, Ming; Jia, Ruoxi; Spanos, Costas J. (1 November 2017). "Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence". IEEE Transactions on Mobile Computing. 16 (11): 3264–3277. arXiv:1407.4395. doi:10.1109/tmc.2017.2684806.
- Lin, X.; Lau, J. (2016). "Applying demand-controlled ventilation" (PDF). ASHRAE Journal. 58 (1): 30–32, 34, 36. ProQuest 1755482305.