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An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model

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  • Cui, Can
  • Zhang, Xin
  • Cai, Wenjian

Abstract

This paper proposes an energy-saving oriented air balancing method based on a branch and black-box (B2) model for multi-zone demand controlled ventilation (DCV) systems. The proposed method can achieve accurate air flow control in each zone, which avoids unnecessary energy consumption caused by over-ventilation. The operating procedures of the proposed method are as follows: Building the B2 model for the DCV system → Predicting the terminal static pressures under the given zone design flow values → Determining the damper angles of each zone based on the static pressure predictions. In the proposed air balancing method, the basic modelling unit is the duct branch, instead of the internal fittings in the previous model-based air balancing method. Therefore, the proposed method does not need to know the complicated fitting information (e.g., elbows, transitions, dampers, etc.,) in its modelling process and therefore becomes much simpler. In addition, the complexity of the proposed B2 model is also greatly reduced since it is independent with the fitting numbers and does not need to calculate the fitting loss coefficients. Furthermore, the radial basis function (RBF) kernel is also utilized in the proposed method to guarantee the air balancing accuracy. Compared with the existing air balancing methods, the proposed method is more efficient and effective in practice. In the lab, the proposed air balancing method was validated on a real DCV system of 5 zones under various design flow conditions. The experimental results verified the effectiveness of the proposed method on both the air balancing control accuracy and the energy saving ability.

Suggested Citation

  • Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:appene:v:264:y:2020:i:c:s0306261920302464
    DOI: 10.1016/j.apenergy.2020.114734
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    1. Radhakrishnan, Nikitha & Su, Yang & Su, Rong & Poolla, Kameshwar, 2016. "Token based scheduling for energy management in building HVAC systems," Applied Energy, Elsevier, vol. 173(C), pages 67-79.
    2. 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.
    3. Wang, Weimin & Katipamula, Srinivas & Ngo, Hung & Underhill, Ronald & Taasevigen, Danny & Lutes, Robert, 2015. "Field evaluation of advanced controls for the retrofit of packaged air conditioners and heat pumps," Applied Energy, Elsevier, vol. 154(C), pages 344-351.
    4. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
    5. Yau, Y.H. & Lee, S.K., 2010. "Feasibility study of an ice slurry-cooling coil for HVAC and R systems in a tropical building," Applied Energy, Elsevier, vol. 87(8), pages 2699-2711, August.
    6. Goyal, Siddharth & Ingley, Herbert A. & Barooah, Prabir, 2013. "Occupancy-based zone-climate control for energy-efficient buildings: Complexity vs. performance," Applied Energy, Elsevier, vol. 106(C), pages 209-221.
    7. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    8. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    9. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.
    10. 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.
    11. Wong, L.T. & Mui, K.W., 2008. "A transient ventilation demand model for air-conditioned offices," Applied Energy, Elsevier, vol. 85(7), pages 545-554, July.
    12. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    13. Ge, Gaoming & Xiao, Fu & Xu, Xinhua, 2011. "Model-based optimal control of a dedicated outdoor air-chilled ceiling system using liquid desiccant and membrane-based total heat recovery," Applied Energy, Elsevier, vol. 88(11), pages 4180-4190.
    14. Razmara, M. & Maasoumy, M. & Shahbakhti, M. & Robinett, R.D., 2015. "Optimal exergy control of building HVAC system," Applied Energy, Elsevier, vol. 156(C), pages 555-565.
    15. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
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    5. Li, Y. & Arulnathan, V. & Heidari, M.D. & Pelletier, N., 2022. "Design considerations for net zero energy buildings for intensive, confined poultry production: A review of current insights, knowledge gaps, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Zhang, Sheng & Ai, Zhengtao & Lin, Zhang, 2021. "Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving," Applied Energy, Elsevier, vol. 293(C).
    7. Joanna Ferdyn-Grygierek & Krzysztof Grygierek, 2024. "Ventilation Methods for Improving the Indoor Air Quality and Energy Efficiency of Multi-Family Buildings in Central Europe," Energies, MDPI, vol. 17(9), pages 1-21, May.
    8. Cheng, Fanyong & Cui, Can & Cai, Wenjian & Zhang, Xin & Ge, Yuan & Li, Bingxu, 2022. "A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system," Energy, Elsevier, vol. 239(PB).

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