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

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  • 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
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    References listed on IDEAS

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    Cited by:

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    3. Ekaterina Dudkina & Emanuele Crisostomi & Alessandro Franco, 2023. "Prediction of CO 2 in Public Buildings," Energies, MDPI, vol. 16(22), pages 1-17, November.

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