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Real-time ventilation control based on a Bayesian estimation of occupancy

Building Simulation volume 14, pages 1487–1497 (2021)Cite this article

Abstract

Demand-controlled ventilation (DCV) is commonly implemented to provide variable amounts of outdoor air according to an internal ventilation demand. The objective of the present study is to investigate the applicability and the performance of occupancy-based DCV schemes in comparison with time-based and CO2-based DCV schemes. To do this, we apply the occupancy estimation method by the Bayes theorem to control the ventilation rate of an office building in real-time. We investigated six cases in total (two cases for each control scheme). Experiments were conducted in a small office room with controllable ventilation equipment and relevant sensors. The observed results indicated that the occupancy-based schemes relying on Bayes theorem could be applied successfully to perform continuous control of ventilation rates without causing recursive problems. Additionally, we discussed the time delays associated with the control procedure, including dispersion time, sensor-response time, and data processing time. Finally, we compared the performance of the proposed approach in six DCV cases in terms of a resultant indoor CO2 level and the total ventilation-air volume. We concluded that DCV control based on both occupancy and floor area provided the best conformity to the ASHRAE standard among the analyzed schemes.

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Abbreviations

A :

floor area (m2)

C :

CO2 concentration (ppm)

C max :

maximum set-point indoor CO2 concentration in Case 4 (ppm)

C min :

minimum set-point indoor CO2 concentration in Case 4 (ppm)

C R :

measured indoor CO2 concentration in Case 4 (ppm)

C SA :

CO2 supply outdoor concentration (ppm)

ṁ :

CO2 generation rate per person (g/(min·person))

n :

number of data points

N :

number of occupants (person)

N est :

estimated number of occupants (person)

N act :

actual number of occupants (person)

q A :

outdoor airflow rate required per unit area in Case 6 (L/(s·m2))

q N :

outdoor airflow rate required per person in Case 6 (L/(s·person))

q n :

outdoor airflow rate required per person in Case 5 (L/(s·person))

Q :

ventilation rate (L/s)

Q max :

maximum set-point ventilation rate in Case 4 (L/s)

Q min :

minimum set-point ventilation rate in Case 4 (L/s)

Q N :

people outdoor airflow rate (L/s)

Q A :

area outdoor airflow rate (L/s)

R 2 (T):

normalized cross-correlation

t :

time (min)

T :

time delay (min)

X :

variable proposal

\({\cal N}({\mu _i},\;\sigma _i^2)\) :

Gaussian distribution

μ i :

mean of variable prior distribution

μ ṁ :

mean of CO2 generation rate per person prior distribution (g/(min·person))

μ n :

mean of number of occupants prior distribution (person)

μ Q :

mean of ventilation rate prior distribution (L/s)

μ SA :

mean of CO2 supply outdoor concentration prior distribution (ppm)

σ 2i :

variant of variable prior distribution

σ i :

standard deviation of variable prior distribution

σ ṁ :

standard deviation of CO2 generation rate per person prior distribution (g/(min·person))

σ n :

standard deviation of number of occupants prior distribution (person)

σ Q :

standard deviation of ventilation rate prior distribution (L/s)

σ sa :

standard deviation of CO2 supply outdoor concentration prior distribution (ppm)

References

  1. Alam AG, Rahman H, Kim JK, et al. (2017). Uncertainties in neural network model based on carbon dioxide concentration for occupancy estimation. Journal of Mechanical Science and Technology, 31: 2573–2580.

    Article  Google Scholar 

  2. Andrews J, Kowsika M, Vakil A, et al. (2020). A motion induced passive infrared (PIR) sensor for stationary human occupancy detection. In: Proceedings of IEEE/ION Position, Location and Navigation Symposium (PLANS).

  3. ASHRAE (2004). ASHRAE Standard 90.1-2004. Building Performance Rating Method. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  4. ASHRAE (2007). ASHARE Standard 62.1-2007. Ventilation for Acceptable Indoor Air Quality. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  5. ASHRAE (2013). ASHARE Standard 62.1-2013. Ventilation for Acceptable Indoor Air Quality. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  6. ASTM (2012). ASTM Standard D 6245-2012. Standard Guide for Using Indoor Carbon Dioxide Concentrations to Evaluate Indoor Air Quality and Ventilation. West Conshohocken, PA, USA: American Society for Testing and Materials.

    Google Scholar 

  7. Bardsley JM (2012). MCMC-based image reconstruction with uncertainty quantification. SIAM Journal on Scientific Computing, 34: A1316–A1332.

    MathSciNet  Article  Google Scholar 

  8. Barnea DI, Silverman HF (1972). A class of algorithms for fast digital image registration. IEEE Transactions on Computers, C-21: 179–186.

    Article  Google Scholar 

  9. Bastin G (1999). Issues in modelling and control of mass balance systems. In: Aeyels D, Lamnabhi-Lagarrigue F, van der Schaft A (eds), Stability and Stabilization of Nonlinear Systems. Lecture Notes in Control and Information Sciences, vol 246. London: Springer.

    Chapter  Google Scholar 

  10. Calì 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.

    Article  Google Scholar 

  11. Dedesko S, Stephens B, Gilbert JA, et al. (2015). Methods to assess human occupancy and occupant activity in hospital patient rooms. Building and Environment, 90: 136–145.

    Article  Google Scholar 

  12. Dino IG, Kalfaoglu E, Sari AE, et al. (2019). Video content analysis-based detection of occupant presence for building energy modelling. In: Proceedings of ICT in Design, Construction and Management in Architecture, Engineering, Construction and Operations (AECO), Newcastle, UK.

  13. 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.

    Article  Google Scholar 

  14. Ebadat A, Bottegal G, Varagnolo D, et al. (2013). Estimation of building occupancy levels through environmental signals deconvolution. In: Proceeding of the 13th International Conference on Systems for Energy-Efficient Buildings.

  15. Erickson VL, Carreira-Perpiñán MÁ, Cerpa AE (2014). Occupancy modeling and prediction for building energy management. ACM Transactions on Sensor Networks, 10: 1–28.

    Article  Google Scholar 

  16. ISO (2008). ISO 3966. Measurement of Fluid Flow in Closed Conduits—Velocity Area Method Using Pitot Static Tubes. The International Organization for Standardization.

  17. Jiang C, Masood MK, Soh YC, et al. (2016). Indoor occupancy estimation from carbon dioxide concentration. Energy and Buildings, 131: 132–141.

    Article  Google Scholar 

  18. Labeodan T, Zeiler W, Boxem G, et al. (2015). Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation. Energy and Buildings, 93: 303–314.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. Liu D, Guan X, Du Y, et al. (2013). Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors. Measurement Science and Technology, 24: 074023.

    Article  Google Scholar 

  21. 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: 2499–2508.

    Article  Google Scholar 

  22. Meyn S, Surana A, Lin Y, et al. (2009). A sensor-utility-network method for estimation of occupancy in buildings. In: Proceedings of the 48h IEEE Conference on Decision and Control (CDC).

  23. Murphy J, Maldeis N (2009). Using time-of-day scheduling to save energy. ASHRAE Journal, 51(5): 42–48.

    Google Scholar 

  24. Nassif N (2012). A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems. Energy and Buildings, 45: 72–81.

    Article  Google Scholar 

  25. O’Neill ZD, Li Y, Cheng HC, et al. (2020). Energy savings and ventilation performance from CO2-based demand controlled ventilation: Simulation results from ASHRAE RP-1747 (ASHRAE RP-1747). Science and Technology for the Built Environment, 26(2): 257–281.

    Article  Google Scholar 

  26. Parsons P (2014). Determining infiltration rates and predicting building occupancy using CO2 concentration curves. Journal of Energy, 2014: 1–6.

    Article  Google Scholar 

  27. Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394–398.

    Article  Google Scholar 

  28. Persily A, de Jonge L (2017). Carbon dioxide generation rates for building occupants. Indoor Air, 27: 868–879.

    Article  Google Scholar 

  29. Rahman H, Han H (2016). Comparison of numerical algorithms to estimate number of occupants based on CO2 concentration. In: Proceedings of the SAREK 2016 Winter Annual Conference, The Society of Air-Conditioning and Refrigerating Engineers of Korea.

  30. Rahman H, Han H (2018). Bayesian estimation of occupancy distribution in a multi-room office building based on CO2 concentrations. Building Simulation, 11: 575–583.

    Article  Google Scholar 

  31. Schell MB, Turner SC, Shim RO (1998). Application of CO2-based demand-control ventilation using standard 62: optimizing energy use and ventilation, ASHRAE Transactions, 104(2): 1213–1225.

    Google Scholar 

  32. Schibuola L, Scarpa M, Tambani C (2018). CO2 based ventilation control in energy retrofit: An experimental assessment. Energy, 143: 606–614.

    Article  Google Scholar 

  33. Shin C, Han H (2015). Occupancy estimation in a subway station using Bayesian simulation based on carbon dioxide and particle concentrations. International Journal of Mechanical Systems Engineering, 1: IJMSE–106.

    Article  Google Scholar 

  34. Shriram S, Ramamurthy K (2019). Assessment of CO2-based demand controlled ventilation requirement for a flexible work environment with ductless split air conditioners. Science and Technology for the Built Environment, 25: 805–818.

    Article  Google Scholar 

  35. 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: 124–133.

    Article  Google Scholar 

  36. Taylor ST (2006). CO2-based DCV using 62.1-2004. ASHRAE Journal, 48(5): 67–75.

    Google Scholar 

  37. Wang S, Jin X (1998). CO2-based occupancy detection for on-line outdoor air flow control. Indoor and Built Environment, 7: 165–181.

    Article  Google Scholar 

  38. Wang S, Burnett J, Chong H (1999). Experimental validation of CO2-based occupancy detection for demand-controlled ventilation. Indoor and Built Environment, 8: 377–391.

    Article  Google Scholar 

  39. Wang J, Zabaras N (2004). A Bayesian inference approach to the inverse heat conduction problem. International Journal of Heat and Mass Transfer, 47: 3927–3941.

    Article  Google Scholar 

  40. Wang H, Wang C, Wang Y, et al. (2017). Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov chain Monte Carlo algorithm. Journal of Hydrology, 549: 476–483.

    Article  Google Scholar 

  41. Zhang R, Lam KP, Chiou Y, Dong B (2012). Information-theoretic environment features selection for occupancy detection in open office spaces. Building Simulation, 5: 179–188.

    Article  Google Scholar 

  42. Zhou J, Shi J (2011). A comprehensive multi-factor analysis on RFID localization capability. Advanced Engineering Informatics, 25: 32–40.

    MathSciNet  Article  Google Scholar 

  43. Zuraimi MS, Pantazaras A, Chaturvedi KA, et al. (2017). Predicting occupancy counts using physical and statistical CO2-based modeling methodologies. Building and Environment, 123: 517–528.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (2016R1D1A1B0-1009625) and Kookmin University.

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Affiliations

  1. Politeknik Negeri Jakarta, Jl. Prof. DR. G.A. Siwabessy, Depok, 16425, Jawa Barat, Indonesia

    Haolia Rahman

  2. Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul., 02707, R.O. Korea

    Hwataik Han

Authors
  1. Haolia Rahman
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  2. Hwataik Han
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Correspondence to Hwataik Han.

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Rahman, H., Han, H. Real-time ventilation control based on a Bayesian estimation of occupancy. Build. Simul. 14, 1487–1497 (2021). https://doi.org/10.1007/s12273-020-0746-7

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Keywords

  • occupancy estimation
  • Bayesian MCMC
  • carbon dioxide
  • ventilation control