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Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

Author

Listed:
  • Nam-Chul Seong

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Jee-Heon Kim

    (Eco-System Research Center, Gachon University, Seongnam 13120, Korea)

  • Wonchang Choi

    (Department of Architectural Engineering, Gachon University, Seongnam 13120, Korea)

Abstract

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.

Suggested Citation

  • Nam-Chul Seong & Jee-Heon Kim & Wonchang Choi, 2019. "Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5122-:d:268521
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    References listed on IDEAS

    as
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    3. Navarro, Lidia & de Gracia, Alvaro & Colclough, Shane & Browne, Maria & McCormack, Sarah J. & Griffiths, Philip & Cabeza, Luisa F., 2016. "Thermal energy storage in building integrated thermal systems: A review. Part 1. active storage systems," Renewable Energy, Elsevier, vol. 88(C), pages 526-547.
    4. Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
    5. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
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    Cited by:

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    2. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).

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