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Mechanical Ventilation Control Based on Estimated occupancy using a Carbon Dioxide Sensor

Kontrol Ventilasi Mekanis Berbasis pada Jumlah Estimasi Penghuni menggunakan Sensor Karbon Dioksida

*Haolia Rahman orcid scopus  -  Department of Mechanical Engineering, Jakarta State Polytechnic, Indonesia
Agus Sukandi  -  Department of Mechanical Engineering, Jakarta State Polytechnic, Indonesia
Nasruddin Nasruddin  -  Faculty of Engineering, Padang State University, Indonesia
Arnas Arnas  -  Faculty of Engineering, Padang State University, Indonesia
Remon Lapisa  -  Faculty of Engineering, Padang State University, Indonesia
Open Access Copyright (c) 2020 TEKNIK

Citation Format:
Ventilation is an important aspect to maintain good indoor air quality in a building. However, excessive ventilation causing high energy consumption of the HVAC system. The ASHRAE Standard provides a guideline to set the ventilation rate that depends on the occupants' number and space. Thus, quantification of the number of occupants is required to regulate the ventilation rate. In this study, the estimated number of occupants was estimated using a Bayesian MCMC method based on CO2 levels. The mass balance equation of the CO2 is used as a model for the calculation of Bayesian MCMC. The Bayesian method for estimating the occupants' number is tested in a 96,7 m3 office room equipped with a ventilation system. Thus the occupancy estimation and control of ventilation can be done in real-time. The test also includes conventional ventilation control based on CO2 levels directly without converting to the occupants' number. The ventilation rate based on the number of occupants at the present test chamber refers to ASHRAE 62.1. The test results show that ventilation controlled by the estimated number of occupants using the Bayesian method successfully conducted with ventilation rate per occupant closer to the ASHRAE 62.1 standard over conventional ventilation method
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Keywords: occupancy estimation; ventilation control; Bayesian MCMC; carbon dioxide, mass balance equation

Article Metrics:

  1. Alam, A. G., Rahman, H., Kim, J. K., & Han, H. (2017). Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation. Journal of Mechanical Science and Technology, 31(5), 2573– 2580
  2. American Society of Heating, Refrigerating and Air-Conditioning Engineers., & American National Standards Institute. (1989). Ventilation for acceptable indoor air quality: Standard 62-1989. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers
  3. American Society of Heating, Refrigerating and Air-Conditioning Engineers., Air-Conditioning and Refrigeration Institute., National Institute of Standards and Technology (U.S.), & U.S. Green Building Council. (2007). 62.1 user's manual: ANSI/ASHRAE Standard 62.1-2007 : ventilation for acceptable indoor air quality. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers
  4. Cali, D., Matthes, P., Huchtemann, K., Streblow, R., & Müller, D. (2015). CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings. Building and Environment, 86, 39– 49
  5. Camelia, A. (2011). Sick Building Syndrome and Indoor Air Quality. Jurnal Ilmu Kesehatan Masyarakat, 2(2): 79– 84
  6. Duarte, C., Van Den Wymelenberg, K., & Rieger, C. (2013). Revealing occupancy patterns in an office building through the use of occupancy sensor data. Energy and buildings, 67, 587– 595
  7. International Organization for Standardization. (2008). ISO 3966: Measurement of fluid flow in closed conduits - Velocity area method using Pitot static tubes. Geneva: ISO
  8. Li, N., Calis, G., & Becerik-Gerber, B. (2012). Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations. Automation in construction, 24, 89-99
  9. Lu, T., Lü, X., & Viljanen, M. (2011). A novel and dynamic demand-controlled ventilation strategy for CO2 control and energy saving in buildings. Energy and buildings, 43(9), 2499– 2508
  10. Nasruddin, Audi, M. R., Ilham, H., Putra, J. D., & Ananda, Y. O. (2018). Analysis of Heat and Mass Transfer on Fungi Growth Inside of Building Walls and Increasing of Energy Consumption, Case Study: Jakarta and Medan Weather Data. Prosiding SNTTM XVII, 295–99
  11. Persily, A., & de Jonge, L. (2017). Carbon dioxide generation rates for building occupants. Indoor air, 27(5), 868-879
  12. Rahman, H., & Han, H. (2017a). Estimation of occupancy in a naturally ventilated room using Bayesian method based on CO2 concentration. International Journal of Mechanical Systems Engineering. 3,123
  13. Rahman, H., & Han, H. (2017b). Bayesian estimation of occupancy distribution in a multi-room office building based on CO2 concentrations. Building Simulation, 11, 575–583
  14. Rahman, H., & Han, H. (2017c). Bayesian Approach for Occupancy Estimation Based on CO2 Concentration in a Room of Unknown Ventilation Rate. 14th Korean society for indoor environment (KOSSIE) conference. Gwangju, South Korea
  15. Schell, M. B., S. C. Turner, and R. O. Shim. (1998). Application of CO2-Based Demand-Controlled Ventilation Using ASHRAE Standard 62: Optimizing Energy Use and Ventilation. ASHRAE Transactions, 104(2):1213–25
  16. Schibuola, L., Scarpa, M., & Tambani, C. (2018). CO2 based ventilation control in energy retrofit: An experimental assessment. Energy, 143, 606– 614
  17. Sun, Z., Wang, S., & Ma, Z. (2011). In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building. Building and Environment, 46(1), 124– 133
  18. Yang, S., Hans, A., Zhao, W., & Luo, X. (2020). Indoor Localization and Human Activity Tracking with Multiple Kinect Sensors. Smart Assisted Living, 23– 42

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