IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v329y2023ics0306261922015458.html
   My bibliography  Save this article

A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency

Author

Listed:
  • Li, Chunxiao
  • Cui, Can
  • Li, Ming

Abstract

This paper presents a novel method, named proactive 2-stage demand-controlled ventilation (P2S-DCV) method, to maintain indoor air quality (IAQ) and reduce the energy consumption of multi-zone ventilation systems. The proposed P2S-DCV method applies a proactive control scheme, which predicts future indoor CO2 concentration and supplies proper ventilation to each zone. The method includes two stages. In Stage I, a DNN prediction model is established to predict the future CO2 concentration to calculate the corresponding demand airflow. In Stage II, a reinforcement learning method is designed to achieve rapid and accurate control, and further reduce the energy consumption by optimizing the fan pressure and damper positions. A 5-zone ventilation system is established to validate the proposed P2S-DCV method. The experiment verifies that: a) it can maintain comfortable IAQ via predicting the change of future indoor CO2 and applying effective ventilation control in advance; b) it can improve the control performance, the accuracy is maintained within 8 % (satisfied the ASHRAE Standards), and the control time is maintained within minutes. It can reduce the regulating time by 83.62 % compared with ASHRAE Ratio method, and up to 51.68 % compared with PID method; c) it can reduce the fan energy consumption by 16.4 % compared with ASHRAE Ratio method, and up to 21.8 % compared with PID method; d) it has good generalization ability for various IAQ requirements and ventilation systems with different topologies.

Suggested Citation

  • Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(C).
  • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922015458
    DOI: 10.1016/j.apenergy.2022.120288
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922015458
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.120288?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. 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).
    2. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    3. 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).
    4. Lu, Xing & O'Neill, Zheng & Li, Yanfei & Niu, Fuxin, 2020. "A novel simulation-based framework for sensor error impact analysis in smart building systems: A case study for a demand-controlled ventilation system," Applied Energy, Elsevier, vol. 263(C).
    5. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    6. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    7. 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.
    8. Nam, KiJeon & Heo, SungKu & Li, Qian & Loy-Benitez, Jorge & Kim, MinJeong & Park, DuckShin & Yoo, ChangKyoo, 2020. "A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions," Applied Energy, Elsevier, vol. 266(C).
    9. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    10. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    11. Ye, Yunyang & Chen, Yan & Zhang, Jian & Pang, Zhihong & O’Neill, Zheng & Dong, Bing & Cheng, Hwakong, 2021. "Energy-saving potential evaluation for primary schools with occupant-centric controls," Applied Energy, Elsevier, vol. 293(C).
    12. Fix, Andrew J. & Pamintuan, Bryan C. & Braun, James E. & Warsinger, David M., 2022. "Vapor-selective active membrane energy exchanger with mechanical ventilation and indoor air recirculation," Applied Energy, Elsevier, vol. 312(C).
    13. Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    14. Kim, Moon Keun & Baldini, Luca & Leibundgut, Hansjürg & Wurzbacher, Jan Andre, 2020. "Evaluation of the humidity performance of a carbon dioxide (CO2) capture device as a novel ventilation strategy in buildings," Applied Energy, Elsevier, vol. 259(C).
    15. Li, Bingxu & Wu, Bingjie & Peng, Yelun & Cai, Wenjian, 2022. "Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality," Applied Energy, Elsevier, vol. 307(C).
    16. 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).
    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. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    2. Ekaterina Dudkina & Emanuele Crisostomi & Alessandro Franco, 2023. "Prediction of CO 2 in Public Buildings," Energies, MDPI, vol. 16(22), pages 1-17, November.
    3. 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.

    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. Li, Bingxu & Wu, Bingjie & Peng, Yelun & Cai, Wenjian, 2022. "Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality," Applied Energy, Elsevier, vol. 307(C).
    2. 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.
    3. Pouranian, Fatemeh & Akbari, Habibollah & Hosseinalipour, S.M., 2021. "Performance assessment of solar chimney coupled with earth-to-air heat exchanger: A passive alternative for an indoor swimming pool ventilation in hot-arid climate," Applied Energy, Elsevier, vol. 299(C).
    4. Sinha, Anshuman & Thakkar, Harshul & Rezaei, Fateme & Kawajiri, Yoshiaki & Realff, Matthew J., 2022. "Reduced building energy consumption by combined indoor CO2 and H2O composition control," Applied Energy, Elsevier, vol. 322(C).
    5. Su, Wei & Ai, Zhengtao & Liu, Jing & Yang, Bin & Wang, Faming, 2023. "Maintaining an acceptable indoor air quality of spaces by intentional natural ventilation or intermittent mechanical ventilation with minimum energy use," Applied Energy, Elsevier, vol. 348(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. Jiang, Zixin & Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk," Applied Energy, Elsevier, vol. 334(C).
    8. Pang, Zhihong & O'Neill, Zheng & Chen, Yan & Zhang, Jian & Cheng, Hwakong & Dong, Bing, 2023. "Adopting occupancy-based HVAC controls in commercial building energy codes: Analysis of cost-effectiveness and decarbonization potential," Applied Energy, Elsevier, vol. 349(C).
    9. Dong, Lianxin & Wu, Qing & Hong, Juhua & Wang, Zhihua & Fan, Shuai & He, Guangyu, 2023. "An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings," Applied Energy, Elsevier, vol. 330(PA).
    10. Li, Wenzhuo & Tang, Rui & Wang, Shengwei & Zheng, Zhuang, 2023. "An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings," Applied Energy, Elsevier, vol. 349(C).
    11. Wang, Huan & Liang, Chenjiyu & Wang, Guijin & Li, Xianting, 2024. "Energy-saving potential of fresh air management using camera-based indoor occupancy positioning system in public open space," Applied Energy, Elsevier, vol. 356(C).
    12. Zhang, Sheng & Liu, Jun & Wang, Fenghao & Chai, Jiale, 2023. "Design optimization of medium-deep borehole heat exchanger for building heating under climate change," Energy, Elsevier, vol. 282(C).
    13. Li, Wenzhuo & Wang, Shengwei, 2022. "A fully distributed optimal control approach for multi-zone dedicated outdoor air systems to be implemented in IoT-enabled building automation networks," Applied Energy, Elsevier, vol. 308(C).
    14. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    15. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    16. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    17. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    18. Mukhtar, A. & Ng, K.C. & Yusoff, M.Z., 2018. "Passive thermal performance prediction and multi-objective optimization of naturally-ventilated underground shelter in Malaysia," Renewable Energy, Elsevier, vol. 123(C), pages 342-352.
    19. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2022. "Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change," Energy, Elsevier, vol. 258(C).
    20. David Vérez & Luisa F. Cabeza, 2021. "Which Building Services Are Considered to Have Impact on Climate Change?," Energies, MDPI, vol. 14(13), pages 1-16, June.
    21. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.

    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:appene:v:329:y:2023:i:c:s0306261922015458. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.