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Optimal decision making in ventilation control

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  • Kusiak, Andrew
  • Li, Mingyang

Abstract

In this paper, a two-mode ventilation control of a single facility is formulated as a scheduling model over multiple time horizons. Using the CO2 concentration as the major indoor air quality index and expected room occupancy schedule, optimal solutions leading to reduced CO2 concentration and energy costs are obtained by solving the multi-objective optimization model formulated in the paper. A modified evolutionary strategy algorithm is used to solve the model at different time horizons. The optimized ventilation schedules result in energy savings and maintain an acceptable level of indoor CO2 concentration.

Suggested Citation

  • Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:11:p:1835-1845
    DOI: 10.1016/j.energy.2009.07.039
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    References listed on IDEAS

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    Cited by:

    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    3. Panagiotis Korkidis & Anastasios Dounis & Panagiotis Kofinas, 2021. "Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings," Energies, MDPI, vol. 14(16), pages 1-33, August.
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    7. Schibuola, Luigi & Scarpa, Massimiliano & Tambani, Chiara, 2018. "CO2 based ventilation control in energy retrofit: An experimental assessment," Energy, Elsevier, vol. 143(C), pages 606-614.
    8. Turanjanin, Valentina & Vučićević, Biljana & Jovanović, Marina & Mirkov, Nikola & Lazović, Ivan, 2014. "Indoor CO2 measurements in Serbian schools and ventilation rate calculation," Energy, Elsevier, vol. 77(C), pages 290-296.
    9. R. Shiller I. & Р. Шиллер Дж., 2018. "Нарративная Экономика И Нейроэкономика // Narrative Economics And Neuroeconomics," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(1), pages 64-91.
    10. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    11. Schmid, Eva & Knopf, Brigitte, 2012. "Ambitious mitigation scenarios for Germany: A participatory approach," Energy Policy, Elsevier, vol. 51(C), pages 662-672.
    12. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    13. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "Modeling, air balancing and optimal pressure set-point selection for the ventilation system with minimized energy consumption," Applied Energy, Elsevier, vol. 236(C), pages 574-589.

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