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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 most common indoor pollutants monitored in DCV systems are carbon dioxide and humidity. [1] This control strategy is mainly intended to reduce the energy used by heating, ventilation, and air conditioning (HVAC) systems compared to those of buildings that use open-loop controls with constant ventilation rates.

When to use DCV

Standard HVAC system design uses fixed airflow rates to calculate the outdoor air (OA) required in a space. These airflow rates are determined by mechanical code and vary based on expected occupancy and space use. This process of supplying fixed airflow to a space ensures that sufficient OA is present in that space when it is occupied. However, such spaces are not always fully occupied; in these cases, energy is wasted as the HVAC system processes more OA than is necessary for the space occupants. Demand control ventilation is an attractive alternative to standard design in these situations because DCV systems only supply the outdoor airflow necessary to serve the occupants in a space. Therefore, the above-described energy is not wasted in this system type.  

DCV application in different system types

DCV is primarily used in variable-air-volume (VAV) systems. In DCV VAV systems, airflow to a zone is modulated to control the temperature and outdoor airflow to the space. Using the pollutant levels measured in a zone, the system’s controller sets the zone’s minimum airflow requirement to dilute the pollutant concentration. [2] Such a control sequence is supported by a pollutant sensor (e.g. carbon dioxide sensor), a variable frequency drive (VFD) on the fan supplying the zone, individual VAV boxes with reheat serving each space in the zone, and airflow measuring stations. [2]  

Research has been conducted on the application of DCV in constant-air-volume (CAV) systems. Although CAV systems cannot modulate airflow, researchers have experimented with running CAV system equipment intermittently to reduce energy consumption. [1] In this proposed system, the HVAC equipment is to run continuously when the space is occupied, then cycle on and off to maintain indoor air quality during inoccupancy. [1]

Carbon dioxide sensing

Carbon dioxide levels measured in a space are commonly used to control DCV systems because CO2 level is generally proportional to the level of bioeffluents, or occupant generated pollutants, in a space. [2] 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. [3] 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 unoccupied airflow rate. This rate is determined by the building owner standards, along with the designer and ASHRAE Standard 62.1. [4]

Codes & standards

Common reference codes and standards for ventilation:

  • IMC Chapter 4: 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

  • Ticket sales
  • Timed schedules
  • Positive control gates
  • Motion sensors (various technologies including: Audible sound, inaudible sound, infrared) [5]
  • 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. [6] [7]
  • Security equipment data share (including people counting video software) [8] [9]
  • Inference from other system sensors/equipment, like smart meters [10]

See also

References

  1. ^ a b c Zhang, Sheng; Ai, Zhengtao; Lin, Zhang (23 April 2021). "Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving". Applied Energy. 293: 116954. doi: 10.1016/j.apenergy.2021.116954. ISSN  0306-2619.
  2. ^ a b c O'Neill, Zheng D.; Li, Yanfei; Cheng, Hwakong C.; Zhou, Xiaohui; Taylor, Steven T. (30 April 2019). "Energy savings and ventilation performance from CO 2 -based demand controlled ventilation: Simulation results from ASHRAE RP-1747 (ASHRAE RP-1747)". Science and Technology for the Built Environment. 26 (2): 257–281. doi: 10.1080/23744731.2019.1620575. ISSN  2374-4731.
  3. ^ "What is a Ductless Air Conditioner and How Does it Work?". 2023-09-28. Retrieved 2024-03-23.
  4. ^ Lin, X.; Lau, J. (2016). "Applying demand-controlled ventilation" (PDF). ASHRAE Journal. 58 (1): 30–32, 34, 36. ProQuest  1755482305.
  5. ^ "Demand Control Ventilation Benefits for Your Building" (PDF). KMC Controls. 2013.
  6. ^ 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.
  7. ^ 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. S2CID  4600376.
  8. ^ University of California, Merced. "Occupancy Measurement, Modeling and Prediction for Energy Efficient Buildings". Archived from the original on 3 December 2012. Retrieved 26 March 2013.
  9. ^ Lawrence Berkeley National Laboratory. "Carbon Dioxide Measurement & People Counting for Demand Controlled Ventilation". Archived from the original on 17 May 2013. Retrieved 26 March 2013.
  10. ^ 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. S2CID  1997078.

External links

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