AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

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

Xuezheng Wang, Bing Dong( ), Jianshun (Jensen) Zhang
Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY 13244, USA
Show Author Information

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.

Keywords

energy-savingmodel predictive controlindoor air qualityinfection risklarge-scale simulation

Electronic Supplementary Material

Download File(s)
bs-16-2-205_ESM1.pdf (2 MB)

References

 

Alkalamouni H, Hitti E, Zaraket H (2021). Adopting fresh air ventilation may reduce the risk of airborne transmission of SARS-CoV-2 in COVID-19 unit. Journal of Infection, 83: e4–e5.
Google Scholar
 
ASHRAE (2016). ASHRAE Standard 62.1-2016: Ventilation for Acceptable Indoor Air Quality. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air Conditioning Engineers.
 

Azuma K, Kagi N, Yanagi U, et al. (2018). Effects of low-level inhalation exposure to carbon dioxide in indoor environments: A short review on human health and psychomotor performance. Environment International, 121: 51–56.
Crossref Google Scholar
 

Bazant MZ, Kodio O, Cohen AE, et al. (2021). Monitoring carbon dioxide to quantify the risk of indoor airborne transmission of COVID-19. Flow, 1: E10.
Crossref Google Scholar
 

Cao G, Awbi H, Yao R, et al. (2014). A review of the performance of different ventilation and airflow distribution systems in buildings. Building and Environment, 73: 171–186.
Google Scholar
 

Dai H, Zhao B (2020). Association of the infection probability of COVID-19 with ventilation rates in confined spaces. Building Simulation, 13: 1321–1327.
Crossref Google Scholar
 

de Coninck R, Helsen L (2016). Practical implementation and evaluation of model predictive control for an office building in Brussels. Energy and Buildings, 111: 290–298.
Crossref Google Scholar
 

DeForest N, Shehabi A, Selkowitz S, et al. (2017). A comparative energy analysis of three electrochromic glazing technologies in commercial and residential buildings. Applied Energy, 192: 95–109.
Crossref Google Scholar
 
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.
 

Dong Y, Zhu L, Li S, et al. (2022). Optimal design of building openings to reduce the risk of indoor respiratory epidemic infections. Building Simulation, 15: 871–884.
Crossref Google Scholar
 

Drgoňa J, Picard D, Helsen L (2020). Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration. Journal of Process Control, 88: 63–77.
Crossref Google Scholar
 

Du B, Tandoc MC, Mack ML, et al. (2020). Indoor CO2 concentrations and cognitive function: A critical review. Indoor Air, 30: 1067–1082.
Crossref Google Scholar
 

Finck C, Li R, Zeiler W (2019). Economic model predictive control for demand flexibility of a residential building. Energy, 176: 365–379.
Crossref Google Scholar
 

Fiorentini M, Wall J, Ma Z, et al. (2017). Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage. Applied Energy, 187: 465–479.
Crossref Google Scholar
 

Ghaddar D, Itani M, Ghaddar N, et al. (2021). Model-based adaptive controller for personalized ventilation and thermal comfort in naturally ventilated spaces. Building Simulation, 14: 1757–1771.
Crossref Google Scholar
 
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.
 

Hilliard T, Swan L, Qin Z (2017). Experimental implementation of whole building MPC with zone based thermal comfort adjustments. Building and Environment, 125: 326–338.
Crossref Google Scholar
 

Huang H, Chen L, Hu E (2015). A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. Building and Environment, 89: 203–216.
Crossref Google Scholar
 

Jin Y, Yan D, Zhang X, et al. (2021). A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development. Building Simulation, 14: 219–235.
Crossref Google Scholar
 

Joe J, Karava P (2019). A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings. Applied Energy, 245: 65–77.
Crossref Google Scholar
 

Johnson DL, Lynch RA, Floyd EL, et al. (2018). Indoor air quality in classrooms: Environmental measures and effective ventilation rate modeling in urban elementary schools. Building and Environment, 136: 185–197.
Crossref Google Scholar
 

Khakimova A, Kusatayeva A, Shamshimova A, et al. (2017). Optimal energy management of a small-size building via hybrid model predictive control. Energy and Buildings, 140: 1–8.
Crossref Google Scholar
 

Lewis D (2020). Mounting evidence suggests coronavirus is airborne—But health advice has not caught up. Nature, 583: 510–513.
Crossref Google Scholar
 

Li S, Xu Y, Cai J, et al. (2021a). Integrated environment-occupant-pathogen information modeling to assess and communicate room-level outbreak risks of infectious diseases. Building and Environment, 187: 107394.
Crossref Google Scholar
 

Li Y, Qian H, Hang J, et al. (2021b). Probable airborne transmission of SARS-CoV-2 in a poorly ventilated restaurant. Building and Environment, 196: 107788.
Crossref Google Scholar
 

Liu H, Lee S, Kim M, et al. (2013). Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system. Energy and Buildings, 66: 553–561.
Crossref Google Scholar
 

Lu X, Pang Z, Fu Y, et al. (2022). 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, 218: 109116.
Crossref Google Scholar
 

Luo K, Lei Z, Hai Z, et al. (2020). Transmission of SARS-CoV-2 in public transportation vehicles: a case study in Hunan Province, China. Open Forum Infectious Diseases, 7(10): ofaa430.
Crossref Google Scholar
 

MacArulla M, Casals M, Carnevali M, et al. (2017). Modelling indoor air carbon dioxide concentration using grey-box models. Building and Environment, 117: 146–153.
Crossref Google Scholar
 

Megahed NA, Ghoneim EM (2021). Indoor Air Quality: Rethinking rules of building design strategies in post-pandemic architecture. Environmental Research, 193: 110471.
Crossref Google Scholar
 

Mei J, Xia X (2017). Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system. Applied Energy, 195: 439–452.
Crossref Google Scholar
 

Mei X, Zeng C, Gong G (2022). Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model. Building Simulation, 15: 1243–1258.
Crossref Google Scholar
 

Morawska L, Milton DK (2020). It is time to address airborne transmission of coronavirus disease 2019 (COVID-19). Clinical Infectious Diseases, 71: 2311–2313.
Google Scholar
 

O'Neill Z, Li Y, Williams K (2017). HVAC control loop performance assessment: A critical review (1587-RP). Science and Technology for the Built Environment, 23: 619–636.
Crossref Google Scholar
 

Pantazaras A, Lee SE, Santamouris M, et al. (2016). Predicting the CO2 levels in buildings using deterministic and identified models. Energy and Buildings, 127: 774–785.
Crossref Google Scholar
 

Park S, Kim Y, Yi S, et al. (2020). Coronavirus disease outbreak in call center, South Korea. Emerging Infectious Diseases, 26: 1666–1670.
Crossref Google Scholar
 

Qiu Y, Wang Y, Tang Y (2020). Investigation of indoor air quality in six office buildings in Chengdu, China based on field measurements. Building Simulation, 13: 1009–1020.
Crossref Google Scholar
 

Riley EC, Murphy G, Riley RL (1978). Airborne spread of measles in a suburban elementary school. American Journal of Epidemiology, 107: 421–432.
Crossref Google Scholar
 

Risbeck MJ, Bazant MZ, Jiang Z, et al. (2021). Modeling and multiobjective optimization of indoor airborne disease transmission risk and associated energy consumption for building HVAC systems. Energy and Buildings, 253: 111497.
Crossref Google Scholar
 

Schoen LJ (2020). Guidance for Building Operations During the COVID-19 Pandemic. ASHRAE Journal, 2020(5): 72–74.
Google Scholar
 

Setti L, Passarini F, de Gennaro G, et al. (2020). 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, 17: 2932.
Crossref Google Scholar
 

Shen J, Kong M, Dong B, et al. (2021). Airborne transmission of SARS-CoV-2 in indoor environments: a comprehensive review. Science and Technology for the Built Environment, 27: 1331–1367.
Crossref Google Scholar
 

Sun C, Zhai Z (2020). The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustainable Cities and Society, 62: 102390.
Crossref Google Scholar
 

Tang R, Fan C, Zeng F, et al. (2022). Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression. Building Simulation, 15: 317–331.
Crossref Google Scholar
 

Tran VV, Park D, Lee YC (2020). 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, 17: 2927.
Crossref Google Scholar
 
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
 
US EPA (2022). Ventilation and Coronavirus (COVID-19). US Environmental Protection Agency. Available at https://www.epa.gov/coronavirus/ventilation-and-coronavirus-covid-19
 

Wells WF (1955). Airborne Contagion and Air Hygiene: An Ecological Study of Droplet Infections. Cambridge, USA: Harvard University Press.
 

Yan D, Zhou X, An J, et al. (2022). DeST 3.0: A new-generation building performance simulation platform. Building Simulation, 15: 1849–1868.
Crossref Google Scholar
 
Yang X (2016). Multi-objective optimization. In: Yang X (ed), Nature-Inspired Optimization Algorithms. London: Elsevier.
 

Yang S, Wan MP, Chen W, et al. (2020). Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Applied Energy, 271: 115147.
Crossref Google Scholar
 

Zhang JJS (2005). Combined heat, air, moisture, and pollutants transport in building environmental systems. JSME International Journal Series B, 48: 182–190.
Crossref Google Scholar
 

Zhang K, Kummert M (2021). Evaluating the impact of thermostat control strategies on the energy flexibility of residential buildings for space heating. Building Simulation, 14: 1439–1452.
Crossref Google Scholar
 

Zheng W, Hu J, Wang Z, et al. (2021). COVID-19 impact on operation and energy consumption of heating, ventilation and air-conditioning (HVAC) systems. Advances in Applied Energy, 3: 100040.
Crossref Google Scholar
Building Simulation
Volume 16 Issue 2,
February 2023
Pages 205-223
DOI: 10.1007/s12273-022-0936-6
Cite this article:
Wang X, Dong B, Zhang J(. Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season. Building Simulation, 2023, 16(2): 205-223. https://doi.org/10.1007/s12273-022-0936-6

480

Views

7

Crossref

7

Web of Science

8

Scopus

0

CSCD

Altmetrics
Received: 12 June 2022
Revised: 16 August 2022
Accepted: 25 August 2022
Published: 30 September 2022
© Tsinghua University Press 2022
Return