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Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO 2 with OpenModelica Modeling

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  • Wei Wang

    (School of Architecture, Southeast University, Sipailou 2#, Xuanwu District, Nanjing 210096, China)

  • Xiaofang Shan

    (School of Civil Engineering and Architecture, Wuhan University of Technology, No. 122 Luoshi Road, Wuhan 430070, China)

  • Syed Asad Hussain

    (Department of Architecture and Civil Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 999077, China)

  • Changshan Wang

    (Nanjing Lvzhen Construction Technology Co., Ltd. Building 11, Qinhefang, Chunjiang New Town, Yuhuatai District, Nanjing 210096, China)

  • Ying Ji

    (College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
    Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing 100124, China)

Abstract

As most residents spend more than 90% of their time in buildings, acceptable and reasonable control of both indoor thermal comfort and air quality is imperative to ensure occupants’ health status and work productivity. However, current control strategies generally take either thermal comfort or indoor air quality as a single loop, rather than the concurrent control of two. To analyze their mutual influence, this study investigated the performance of three multi-control approaches, i.e., proportional integral derivative (PID) control of thermal comfort and a fixed outdoor air ratio, PID control of thermal comfort and design outdoor air rate, and PID control of thermal comfort and occupancy-based demand-controlled ventilation. As a pilot study, three typical control methods were implemented to a multi-zone building via OpenModelica modeling. The results indicate that indoor air temperature can be well-maintained under three control methods, however, the CO 2 concentration under the fixed outdoor air ratio was over 1000 ppm, leading to poor indoor air quality. The control strategy with the design outdoor air rate could not properly ensure the CO 2 concentration, due to the over-ventilated or under-ventilated phenomena, subsequently resulting in unnecessary energy waste. The occupancy-based demand controlled ventilation could maintain the CO 2 concentration under the set-point with an intermediate power energy utilization.

Suggested Citation

  • Wei Wang & Xiaofang Shan & Syed Asad Hussain & Changshan Wang & Ying Ji, 2020. "Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO 2 with OpenModelica Modeling," Energies, MDPI, vol. 13(17), pages 1-20, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4425-:d:404865
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    References listed on IDEAS

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    2. Shengqiang Wei & Yiping Lu & Wei Yang & Yubin Ke & Haibiao Zheng & Lingbo Zhu & Jianfei Tong & Longwei Mei & Shinian Fu & Congju Yao, 2022. "Comparative Research on Ventilation Characteristics of Scattering and Sample Room from Chinese Spallation Neutron Source," Energies, MDPI, vol. 15(11), pages 1-16, May.

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