IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i18p5122-d268521.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/18/5122/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/18/5122/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    5. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    2. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
    2. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Cabeza, Luisa F., 2017. "Simulation-based optimization of PCM melting temperature to improve the energy performance in buildings," Applied Energy, Elsevier, vol. 202(C), pages 420-434.
    3. Goopyo Hong & Byungseon Sean Kim, 2018. "Response to Comments by Yaolin Lin and Wei Yang “Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy”. Energies 2018, 11 , 407," Energies, MDPI, vol. 11(6), pages 1-2, June.
    4. Benedek Kiss & Jose Dinis Silvestre & Rita Andrade Santos & Zsuzsa Szalay, 2021. "Environmental and Economic Optimisation of Buildings in Portugal and Hungary," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    5. 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.
    6. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    7. Fei, Wenbin & Bandeira Neto, Luis A. & Dai, Sheng & Cortes, Douglas D. & Narsilio, Guillermo A., 2023. "Numerical analyses of energy screw pile filled with phase change materials," Renewable Energy, Elsevier, vol. 202(C), pages 865-879.
    8. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    9. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    10. Niamsuwan, Sathit & Kittisupakorn, Paisan & Suwatthikul, Ajaree, 2015. "Enhancement of energy efficiency in a paint curing oven via CFD approach: Case study in an air-conditioning plant," Applied Energy, Elsevier, vol. 156(C), pages 465-477.
    11. Svetlana Ratner & Yuri Chepurko & Larisa Drobyshecskaya & Anna Petrovskaya, 2018. "Management of Energy Enterprises: Energy-efficiency Approach in Solar Collectors Industry: The Case of Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 237-243.
    12. Yang, Xiaohu & Yu, Jiabang & Guo, Zengxu & Jin, Liwen & He, Ya-Ling, 2019. "Role of porous metal foam on the heat transfer enhancement for a thermal energy storage tube," Applied Energy, Elsevier, vol. 239(C), pages 142-156.
    13. Wang, Wenqing & Kolditz, Olaf & Nagel, Thomas, 2017. "Parallel finite element modelling of multi-physical processes in thermochemical energy storage devices," Applied Energy, Elsevier, vol. 185(P2), pages 1954-1964.
    14. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    15. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    16. Yuan, Zhipeng & Liu, Qi & Luo, Baojun & Li, Zhenming & Fu, Jianqin & Chen, Jingwei, 2018. "Thermodynamic analysis of different oil flooded compression enhanced vapor injection cycles," Energy, Elsevier, vol. 154(C), pages 553-560.
    17. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    18. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    19. Zu, Kan & Qin, Menghao & Cui, Shuqing, 2020. "Progress and potential of metal-organic frameworks (MOFs) as novel desiccants for built environment control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    20. Cui, X. & Islam, M.R. & Mohan, B. & Chua, K.J., 2016. "Theoretical analysis of a liquid desiccant based indirect evaporative cooling system," Energy, Elsevier, vol. 95(C), pages 303-312.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5122-:d:268521. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.