Skip to main content
NCBI home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2022 Sep 30;16(2):205–223. doi: 10.1007/s12273-022-0936-6

Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season

Xuezheng Wang 1, Bing Dong 1,✉, Jianshun Jensen Zhang 1
PMCID: PMC9523641  PMID: 36196082

Abstract

Since the coronavirus disease 2019, the extended time indoors makes people more concerned about indoor air quality, while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating, ventilation, and air-conditioning systems. In this study, to represent the dynamics of indoor temperature and air quality, a coupled grey-box model is developed. The model is identified and validated using a data-driven approach and real-time measured data of a campus office. To manage building energy usage and indoor air quality, a model predictive control strategy is proposed and developed. The simulation study demonstrated 18.92% energy saving while maintaining good indoor air quality at the testing site. Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones. The results showed 20%–40% energy saving in general while maintaining a predetermined indoor air quality setpoint. Although the infection risk is increased due to the reduced ventilation rate, it is still less than the suggested threshold (2%) in general.

Electronic Supplementary Material (ESM)

The Appendix is available in the online version of this article at 10.1007/s12273-022-0936-6.

Keywords: model predictive control, indoor air quality, infection risk, energy-saving, large-scale simulation

Electronic Supplementary Material

12273_2022_936_MOESM1_ESM.pdf (2MB, pdf)

Appendix to: Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season

Acknowledgements

This research was jointly sponsored by Honeywell International Inc. and Syracuse University.

References

  1. Alkalamouni H, Hitti E, Zaraket H. Adopting fresh air ventilation may reduce the risk of airborne transmission of SARS-CoV-2 in COVID-19 unit. Journal of Infection. 2021;83:e4–e5. doi: 10.1016/j.jinf.2021.08.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. ASHRAE . ASHRAE Standard 62.1-2016: Ventilation for Acceptable Indoor Air Quality. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air Conditioning Engineers; 2016. [Google Scholar]
  3. Azuma K, Kagi N, Yanagi U, et al. Effects of low-level inhalation exposure to carbon dioxide in indoor environments: A short review on human health and psychomotor performance. Environment International. 2018;121:51–56. doi: 10.1016/j.envint.2018.08.059. [DOI] [PubMed] [Google Scholar]
  4. Bazant MZ, Kodio O, Cohen AE, et al. Monitoring carbon dioxide to quantify the risk of indoor airborne transmission of COVID-19. Flow. 2021;1:E10. doi: 10.1017/flo.2021.10. [DOI] [Google Scholar]
  5. Cao G, Awbi H, Yao R, et al. A review of the performance of different ventilation and airflow distribution systems in buildings. Building and Environment. 2014;73:171–186. doi: 10.1016/j.buildenv.2013.12.009. [DOI] [Google Scholar]
  6. Dai H, Zhao B. Association of the infection probability of COVID-19 with ventilation rates in confined spaces. Building Simulation. 2020;13:1321–1327. doi: 10.1007/s12273-020-0703-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. de Coninck R, Helsen L. Practical implementation and evaluation of model predictive control for an office building in Brussels. Energy and Buildings. 2016;111:290–298. doi: 10.1016/j.enbuild.2015.11.014. [DOI] [Google Scholar]
  8. DeForest N, Shehabi A, Selkowitz S, et al. A comparative energy analysis of three electrochromic glazing technologies in commercial and residential buildings. Applied Energy. 2017;192:95–109. doi: 10.1016/j.apenergy.2017.02.007. [DOI] [Google Scholar]
  9. del Mar Castilla M, Alvarez JD, Normey-Rico JE, et al. (2013). A multivariable nonlinear MPC control strategy for thermal comfort and indoor-air quality. In: Proceedings of the 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), Vienna, Austria.
  10. Dong Y, Zhu L, Li S, et al. Optimal design of building openings to reduce the risk of indoor respiratory epidemic infections. Building Simulation. 2022;15:871–884. doi: 10.1007/s12273-021-0842-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Drgoňa J, Picard D, Helsen L. Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration. Journal of Process Control. 2020;88:63–77. doi: 10.1016/j.jprocont.2020.02.007. [DOI] [Google Scholar]
  12. Du B, Tandoc MC, Mack ML, et al. Indoor CO2 concentrations and cognitive function: A critical review. Indoor Air. 2020;30:1067–1082. doi: 10.1111/ina.12706. [DOI] [PubMed] [Google Scholar]
  13. Finck C, Li R, Zeiler W. Economic model predictive control for demand flexibility of a residential building. Energy. 2019;176:365–379. doi: 10.1016/j.energy.2019.03.171. [DOI] [Google Scholar]
  14. Fiorentini M, Wall J, Ma Z, et al. Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage. Applied Energy. 2017;187:465–479. doi: 10.1016/j.apenergy.2016.11.041. [DOI] [Google Scholar]
  15. Ghaddar D, Itani M, Ghaddar N, et al. Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces. Building Simulation. 2021;14:1757–1771. doi: 10.1007/s12273-021-0783-x. [DOI] [Google Scholar]
  16. Hidalgo-Leon R, Litardo J, Urquizo J, et al. (2019). Some factors involved in the improvement of building energy consumption: A brief review. In: Proceedings of 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM), Guayaquil, Ecuador.
  17. Hilliard T, Swan L, Qin Z. Experimental implementation of whole building MPC with zone based thermal comfort adjustments. Building and Environment. 2017;125:326–338. doi: 10.1016/j.buildenv.2017.09.003. [DOI] [Google Scholar]
  18. Huang H, Chen L, Hu E. A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. Building and Environment. 2015;89:203–216. doi: 10.1016/j.buildenv.2015.01.037. [DOI] [Google Scholar]
  19. Jin Y, Yan D, Zhang X, et al. A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development. Building Simulation. 2021;14:219–235. doi: 10.1007/s12273-020-0638-x. [DOI] [Google Scholar]
  20. Joe J, Karava P. A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings. Applied Energy. 2019;245:65–77. doi: 10.1016/j.apenergy.2019.03.209. [DOI] [Google Scholar]
  21. Johnson DL, Lynch RA, Floyd EL, et al. Indoor air quality in classrooms: Environmental measures and effective ventilation rate modeling in urban elementary schools. Building and Environment. 2018;136:185–197. doi: 10.1016/j.buildenv.2018.03.040. [DOI] [Google Scholar]
  22. Khakimova A, Kusatayeva A, Shamshimova A, et al. Optimal energy management of a small-size building via hybrid model predictive control. Energy and Buildings. 2017;140:1–8. doi: 10.1016/j.enbuild.2017.01.045. [DOI] [Google Scholar]
  23. Lewis D. Mounting evidence suggests coronavirus is airborne—But health advice has not caught up. Nature. 2020;583:510–513. doi: 10.1038/d41586-020-02058-1. [DOI] [PubMed] [Google Scholar]
  24. Li S, Xu Y, Cai J, et al. Integrated environment-occupant-pathogen information modeling to assess and communicate room-level outbreak risks of infectious diseases. Building and Environment. 2021;187:107394. doi: 10.1016/j.buildenv.2020.107394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li Y, Qian H, Hang J, et al. Probable airborne transmission of SARS-CoV-2 in a poorly ventilated restaurant. Building and Environment. 2021;196:107788. doi: 10.1016/j.buildenv.2021.107788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liu H, Lee S, Kim M, et al. Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system. Energy and Buildings. 2013;66:553–561. doi: 10.1016/j.enbuild.2013.07.066. [DOI] [Google Scholar]
  27. Lu X, Pang Z, Fu Y, et al. The nexus of the indoor CO2 concentration and ventilation demands underlying CO2-based demand-controlled ventilation in commercial buildings: A critical review. Building and Environment. 2022;218:109116. doi: 10.1016/j.buildenv.2022.109116. [DOI] [Google Scholar]
  28. Luo K, Lei Z, Hai Z, et al. Transmission of SARS-CoV-2 in public transportation vehicles: a case study in Hunan Province, China. Open Forum Infectious Diseases. 2020;7(10):ofaa430. doi: 10.1093/ofid/ofaa430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. MacArulla M, Casals M, Carnevali M, et al. Modelling indoor air carbon dioxide concentration using grey-box models. Building and Environment. 2017;117:146–153. doi: 10.1016/j.buildenv.2017.02.022. [DOI] [Google Scholar]
  30. Megahed NA, Ghoneim EM. Indoor Air Quality: Rethinking rules of building design strategies in post-pandemic architecture. Environmental Research. 2021;193:110471. doi: 10.1016/j.envres.2020.110471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mei J, Xia X. Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system. Applied Energy. 2017;195:439–452. doi: 10.1016/j.apenergy.2017.03.076. [DOI] [Google Scholar]
  32. Mei X, Zeng C, Gong G. Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model. Building Simulation. 2022;15:1243–1258. doi: 10.1007/s12273-021-0855-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Morawska L, Milton DK. It is time to address airborne transmission of coronavirus disease 2019 (COVID-19) Clinical Infectious Diseases. 2020;71:2311–2313. doi: 10.1093/cid/ciaa939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. O’Neill Z, Li Y, Williams K. HVAC control loop performance assessment: A critical review (1587-RP) Science and Technology for the Built Environment. 2017;23:619–636. doi: 10.1080/23744731.2016.1239466. [DOI] [Google Scholar]
  35. Pantazaras A, Lee SE, Santamouris M, et al. Predicting the CO2 levels in buildings using deterministic and identified models. Energy and Buildings. 2016;127:774–785. doi: 10.1016/j.enbuild.2016.06.029. [DOI] [Google Scholar]
  36. Park S, Kim Y, Yi S, et al. Coronavirus disease outbreak in call center, South Korea. Emerging Infectious Diseases. 2020;26:1666–1670. doi: 10.3201/eid2608.201274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Qiu Y, Wang Y, Tang Y. Investigation of indoor air quality in six office buildings in Chengdu, China based on field measurements. Building Simulation. 2020;13:1009–1020. doi: 10.1007/s12273-020-0663-9. [DOI] [Google Scholar]
  38. Riley EC, Murphy G, Riley RL. Airborne spread of measles in a suburban elementary school. American Journal of Epidemiology. 1978;107:421–432. doi: 10.1093/oxfordjournals.aje.a112560. [DOI] [PubMed] [Google Scholar]
  39. Risbeck MJ, Bazant MZ, Jiang Z, et al. Modeling and multiobjective optimization of indoor airborne disease transmission risk and associated energy consumption for building HVAC systems. Energy and Buildings. 2021;253:111497. doi: 10.1016/j.enbuild.2021.111497. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Schoen LJ. Guidance for Building Operations During the COVID-19 Pandemic. ASHRAE Journal. 2020;2020(5):72–74. [Google Scholar]
  41. Setti L, Passarini F, de Gennaro G, et al. Airborne transmission route of COVID-19: Why 2 meters/6 feet of inter-personal distance could not be enough. International Journal of Environmental Research and Public Health. 2020;17:2932. doi: 10.3390/ijerph17082932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shen J, Kong M, Dong B, et al. Airborne transmission of SARS-CoV-2 in indoor environments: a comprehensive review. Science and Technology for the Built Environment. 2021;27:1331–1367. doi: 10.1080/23744731.2021.1977693. [DOI] [Google Scholar]
  43. Sun C, Zhai Z. The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustainable Cities and Society. 2020;62:102390. doi: 10.1016/j.scs.2020.102390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Tang R, Fan C, Zeng F, et al. Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression. Building Simulation. 2022;15:317–331. doi: 10.1007/s12273-021-0811-x. [DOI] [Google Scholar]
  45. Tran VV, Park D, Lee YC. Indoor air pollution, related human diseases, and recent trends in the control and improvement of indoor air quality. International Journal of Environmental Research and Public Health. 2020;17:2927. doi: 10.3390/ijerph17082927. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. US CDC (2021). Improving Ventilation in Your Home. US Centers for Disease Control and Prevention (CDC). Available at https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/improving-ventilation-home.html
  47. US EPA (2022). Ventilation and Coronavirus (COVID-19). US Environmental Protection Agency. Available at https://www.epa.gov/coronavirus/ventilation-and-coronavirus-covid-19
  48. Wells WF. Airborne Contagion and Air Hygiene: An Ecological Study of Droplet Infections. Cambridge, USA: Harvard University Press; 1955. [Google Scholar]
  49. Yan D, Zhou X, An J, et al. DeST 3.0: A new-generation building performance simulation platform. Building Simulation. 2022;15:1849–1868. doi: 10.1007/s12273-022-0909-9. [DOI] [Google Scholar]
  50. Yang X. Multi-objective optimization. In: Yang X, editor. Nature-Inspired Optimization Algorithms. London: Elsevier; 2016. [Google Scholar]
  51. Yang S, Wan MP, Chen W, et al. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Applied Energy. 2020;271:115147. doi: 10.1016/j.apenergy.2020.115147. [DOI] [Google Scholar]
  52. Zhang JJS. Combined heat, air, moisture, and pollutants transport in building environmental systems. JSME International Journal Series B. 2005;48:182–190. doi: 10.1299/jsmeb.48.182. [DOI] [Google Scholar]
  53. Zhang K, Kummert M. Evaluating the impact of thermostat control strategies on the energy flexibility of residential buildings for space heating. Building Simulation. 2021;14:1439–1452. doi: 10.1007/s12273-020-0751-x. [DOI] [Google Scholar]
  54. Zheng W, Hu J, Wang Z, et al. COVID-19 impact on operation and energy consumption of heating, ventilation and air-conditioning (HVAC) systems. Advances in Applied Energy. 2021;3:100040. doi: 10.1016/j.adapen.2021.100040. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12273_2022_936_MOESM1_ESM.pdf (2MB, pdf)

Appendix to: Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season

Articles from Building Simulation are provided here courtesy of Nature Publishing Group

RESOURCES