MODELLING OF INDOOR AIR QUALITY
DOI:
https://doi.org/10.32782/mathematical-modelling/2026-9-1-17Keywords:
air quality, ventilation, energy efficiency, multi-agent systems, distributed control, building comfort, agent-based modelling, control systemsAbstract
The issue of ensuring adequate indoor air quality, as a significant factor influencing human cognitive functions, health and comfort, has been extensively covered in scientific research using various scientific approaches and methods. Increasing ventilation rates improves air quality and productivity, but is accompanied by higher energy consumption, necessitating a balance between energy efficiency and indoor environmental quality. Research confirms that, when calculating energy consumption and operating conditions for buildings, the impact of climatic and structural factors on filtration and ventilation systems must be taken into account. The paper demonstrates that traditional centralised approaches to optimal control are insufficiently effective under conditions of dynamic changes in indoor environment parameters and user behaviour. The feasibility of applying distributed control methods based on modern sensor networks and intelligent building automation systems is substantiated. A multi-agent distributed approach to the optimal control of multi-zone ventilation systems is proposed, which involves decomposing a complex optimisation problem into a series of local sub-problems. To solve these, a system of interacting agents corresponding to individual rooms and the supply and exhaust unit, as well as a central coordinating agent, is used. The approach takes into account air quality and energy consumption indicators by forming an integrated objective function for optimising ventilation air volumes. Based on agent-oriented modelling, the effectiveness of the proposed approach was investigated under various external conditions, with a comparison to baseline and centralised control. The modelling results confirmed improved control efficiency, reduced energy consumption whilst maintaining an adequate level of IAQ, as well as the system’s high scalability and flexibility. The results obtained indicate the prospects for using multi-agent approaches to optimize the functioning of modern engineering construction systems.
References
Chen J., Augenbroe G., Zeng Z., Song X. Regional difference and related cooling electricity savings of air pollutant affected natural ventilation in commercial buildings across the US. Building and Environment. 2020. Vol. 172. 106700. https://doi.org/10.1016/j.buildenv.2020.106700
Ben-David T., Waring M. S. Impact of natural versus mechanical ventilation on simulated indoor air quality and energy consumption in offices in fourteen U.S. cities. Building and Environment. 2016. Vol. 104. P. 320–336. https://doi.org/10.1016/j.buildenv.2016.05.007
Azimi P., Zhao D., Stephens B. Estimates of HVAC filtration efficiency for fine and ultrafine particles of outdoor origin. Atmospheric Environment. 2014. Vol. 98. P. 337–346. https://doi.org/10.1016/j.atmosenv.2014.09.007
Liu G., Xiao M., Zhang X., Gal C., Chen X., Liu L., Pan S., Wu J., Tang L., Clements-Croome D. A review of air filtration technologies for sustainable and healthy building ventilation. Sustainable Cities and Society. 2017. Vol. 32. P. 375–396. https://doi.org/10.1016/j.scs.2017.04.011
Metz T. Agent-Based Modeling (ABM). Neue Trends in Den Sozialwissenschaften: Innovative Techniken Für Qualitative Und Quantitative Forschung, edited by Sebastian Jäckle. 2017. P. 11–50. https://doi.org/10.1007/978-3-658-17189-6_2
Chen I-C., Cheng K-L. 2024. Agent-Based Modeling Framework for Predicting Regional Electricity Consumption Considering Occupant Behavior Shift and Exogenous Policy Impact. Energy and Buildings. 2024. Vol. 324. 114897. https://doi.org/10.1016/j.enbuild.2024.114897
Jule T., Müller B., Groeneveld J., Grimm V. Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward. Journal of Artificial Societies and Social Simulation. 2017. Vol. 20. https://doi.org/10.18564/jasss.3423
Li W., Koo C., Cha SH., Hong T., Oh J. A novel real-time method for HVAC system operation to improve indoor environmental quality in meeting rooms. Build Environ. 2018. Vol. 144. P. 365–85. https://doi.org/10.1016/j.buildenv.2018.08.046
Mazzetto, Silvia. Interdisciplinary Perspectives on Agent-Based Modeling in the Architecture, Engineering, and Construction Industry: A Comprehensive Review. Buildings. 2024. Vol. 14. № 11. https://doi.org/10.3390/buildings14113480
Kim J, Hong T, Lee M, Jeong K. Analyzing the real-time indoor environmental quality factors considering the influence of the building occupants’ behaviours and the ventilation. Build Environ. 2019. Vol. 156. P. 99–109. https://doi.org/10.1016/j.buildenv.2019.04.003
Li W., Wang S. A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use. Applied Energy. 2020. Vol. 275. 115371. https://doi.org/10.1016/j.apenergy.2020.115371
Stieler D., Schwinn T., Leder S., Maierhofer M., Kannenberg F., Menges A. Agent-Based Modeling and Simulation in Architecture. Automation in Construction. 2022. Vol. 141. 104426. https://doi.org/10.1016/j.autcon.2022.104426
Wang Z, Wang L, Dounis AI, Yang R. Multi-agent control system with information fusion based comfort model for smart buildings. Appl Energy. 2012. Vol. 99. P. 247–54. https://doi.org/10.1016/j.apenergy.2012.05.020
Yang R, Wang L. Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy Build. 2013. Vol. 56. P. 1–7. https://doi.org/10.1016/j.enbuild.2012.10.025
Das P., Chalabi Z., Jones B., Milner J., Shrubsole C., Davies M., et al. Multi-objective methods for determining optimal ventilation rates in dwellings. Build Environ. 2013. Vol. 66. P. 72–81. https://doi.org/10.1016/j.buildenv.2013.03.021
