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An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system

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  • Jing, Gang
  • Cai, Wenjian
  • Zhang, Xin
  • Cui, Can
  • Yin, Xiaohong
  • Xian, Huacai

Abstract

For addressing the energy waste resulted by over-ventilation or under-ventilation in conventional demand-controlled ventilation system, an air balancing strategy is proposed to solve the over-ventilation and under-ventilation problems of the multi-zone demand-controlled ventilation system. In this study, an energy-saving oriented mathematical model is constructed to simulate the non-linear behavior of the multi-zone ventilation system and Bayesian linear regression supervised machine learning algorithm is used to estimate the unknown parameters of the constructed model. On the basis of the developed model, the damper control method is established to determine the position of the damper according to the desired airflow rate to ensure the system well-balanced. Therefore, with the constructed system model and the damper control method, the system can be well-balanced to overcome the disadvantages of over-ventilation and under-ventilation, and consumes less energy compared to the system that are not balanced. The performance of the proposed air balancing strategy for demand-controlled ventilation system is practically tested in an experimental rig with five terminals and validated by comparing to the demand-controlled ventilation strategy without air balancing. The experimental results demonstrate that the proposed strategy achieved the desired airflow rate within 4.6% maximum absolute percentage error, and also achieved a maximum value 14.3% for fan power reduction compared to conventional the strategy without air balancing.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:1053-1065
    DOI: 10.1016/j.energy.2019.02.044
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    as
    1. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    2. da Graça Carvalho, Maria, 2012. "EU energy and climate change strategy," Energy, Elsevier, vol. 40(1), pages 19-22.
    3. Chang, Yung-Chung & Chen, Wu-Hsing, 2009. "Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy," Energy, Elsevier, vol. 34(4), pages 448-456.
    4. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.
    5. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    6. Ke, Yu-Pei & Mumma, Stanley A., 1997. "Optimized supply-air temperature (SAT) in variable-air-volume (VAV) systems," Energy, Elsevier, vol. 22(6), pages 601-614.
    7. 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.
    8. Zaheer-Uddin, M., 1993. "Energy start-stop and fluid flow regulated control of multizone HVAC systems," Energy, Elsevier, vol. 18(3), pages 289-302.
    9. Braun, M.R. & Altan, H. & Beck, S.B.M., 2014. "Using regression analysis to predict the future energy consumption of a supermarket in the UK," Applied Energy, Elsevier, vol. 130(C), pages 305-313.
    10. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    11. 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.
    12. 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.
    13. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    14. Rana, Rajib & Kusy, Brano & Wall, Josh & Hu, Wen, 2015. "Novel activity classification and occupancy estimation methods for intelligent HVAC (heating, ventilation and air conditioning) systems," Energy, Elsevier, vol. 93(P1), pages 245-255.
    15. Yang, Zheng & Ghahramani, Ali & Becerik-Gerber, Burcin, 2016. "Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency," Energy, Elsevier, vol. 109(C), pages 641-649.
    16. Zheng, G.R. & Zaheer-Uddin, M., 1996. "Optimization of thermal processes in a variable air volume HVAC system," Energy, Elsevier, vol. 21(5), pages 407-420.
    17. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    18. 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.
    19. Galatioto, A. & Ciulla, G. & Ricciu, R., 2017. "An overview of energy retrofit actions feasibility on Italian historical buildings," Energy, Elsevier, vol. 137(C), pages 991-1000.
    20. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
    21. Chang, Yung-Chung, 2006. "An innovative approach for demand side management—optimal chiller loading by simulated annealing," Energy, Elsevier, vol. 31(12), pages 1883-1896.
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    Cited by:

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    5. 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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