IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v199y2020ics0360544220304357.html
   My bibliography  Save this article

An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control

Author

Listed:
  • Jing, Gang
  • Cai, Wenjian
  • Zhang, Xin
  • Cui, Can
  • Liu, Hongwu
  • Wang, Cheng

Abstract

A data-driven energy-saving control strategy applied to balance the multi-zone demand controlled ventilation system is presented. The proposed strategy consists of two steps: system model construction and air balancing control. Based on observed datasets, a multi-layer perceptron structure is employed to model the multi-zone ventilation system. The model is used to predict the pressure differences of each damper based on the static pressure of the main duct and the desired airflow rates of each damper. Air balancing control approach is implemented based on the empirical formula of the damper. This approach is use to predict the operating positions of each damper based on the predicted pressure differences of the developed model. An experimental apparatus consisting of original components of ventilation system is set up to collect the training and testing data, and simultaneously used to validate the performance of the proposed control strategy. Experimental results demonstrate that the issue of over-ventilation and under-ventilation of demand controlled ventilation system is eliminated, and energy savings of fan power can be obtained with the proposed control strategy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:199:y:2020:i:c:s0360544220304357
    DOI: 10.1016/j.energy.2020.117328
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220304357
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117328?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. 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.
    3. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    4. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    5. 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.
    6. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    7. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    8. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    9. 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.
    10. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    11. 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.
    12. Kusiak, Andrew & Li, Mingyang & Tang, Fan, 2010. "Modeling and optimization of HVAC energy consumption," Applied Energy, Elsevier, vol. 87(10), pages 3092-3102, October.
    13. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    14. 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.
    15. Okochi, Godwine Swere & Yao, Ye, 2016. "A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 784-817.
    16. 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.
    17. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    18. Chenari, Behrang & Dias Carrilho, João & Gameiro da Silva, Manuel, 2016. "Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1426-1447.
    19. 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.
    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. Liu, Peng & Justo Alonso, Maria & Mathisen, Hans Martin, 2023. "Global sensitivity analysis and optimal design of heat recovery ventilation for zero emission buildings," Applied Energy, Elsevier, vol. 329(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. 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.
    2. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    3. Guiqiang Wang & Haiman Wang & Zhiqiang Kang & Guohui Feng, 2020. "Data-Driven Optimization for Capacity Control of Multiple Ground Source Heat Pump System in Heating Mode," Energies, MDPI, vol. 13(14), pages 1-15, July.
    4. 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.
    5. 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).
    6. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    7. 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).
    8. Wang, Xinli & Cai, Wenjian & Yin, Xiaohong, 2017. "A global optimized operation strategy for energy savings in liquid desiccant air conditioning using self-adaptive differential evolutionary algorithm," Applied Energy, Elsevier, vol. 187(C), pages 410-423.
    9. Wang, Xinli & Cai, Wenjian & Lu, Jiangang & Sun, Youxian & Zhao, Lei, 2015. "Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm," Energy, Elsevier, vol. 82(C), pages 939-948.
    10. 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.
    11. Liu, Peng & Justo Alonso, Maria & Mathisen, Hans Martin, 2023. "Global sensitivity analysis and optimal design of heat recovery ventilation for zero emission buildings," Applied Energy, Elsevier, vol. 329(C).
    12. Le Cam, M. & Zmeureanu, R. & Daoud, A., 2017. "Cascade-based short-term forecasting method of the electric demand of HVAC system," Energy, Elsevier, vol. 119(C), pages 1098-1107.
    13. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
    15. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    16. Behzad Rismanchi & Juan Mahecha Zambrano & Bryan Saxby & Ross Tuck & Mark Stenning, 2019. "Control Strategies in Multi-Zone Air Conditioning Systems," Energies, MDPI, vol. 12(3), pages 1-14, January.
    17. Iivo Metsä-Eerola & Jukka Pulkkinen & Olli Niemitalo & Olli Koskela, 2022. "On Hourly Forecasting Heating Energy Consumption of HVAC with Recurrent Neural Networks," Energies, MDPI, vol. 15(14), pages 1-20, July.
    18. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    19. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    20. Balvís, Eduardo & Sampedro, Óscar & Zaragoza, Sonia & Paredes, Angel & Michinel, Humberto, 2016. "A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings," Applied Energy, Elsevier, vol. 177(C), pages 60-70.

    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:eee:energy:v:199:y:2020:i:c:s0360544220304357. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.