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Overall and local environmental collaborative control based on personal comfort model and personal comfort system

Author

Listed:
  • Wu, Yeyu
  • Jiang, Haihua
  • Chen, Weiming
  • Fan, Junhui
  • Cao, Bin

Abstract

Most methods for creating an indoor thermal environment are based on controlling heating, ventilation, and air conditioning (HVAC) systems and do not consider the various needs of individuals in a multiperson space. Personal comfort systems (PCS) and personal comfort models (PCM) are popular technologies for achieving personal thermal comfort. This paper presents a thermal environmental collaborative control system (TECCS) that regulates environments at different spatial scales by leveraging the advantages of the HVAC system, PCS, PCM, and PCM-based automatic control to address the issue of individual differences in thermal demand in multiperson environments. The TECCS predicts thermal sensation votes (TSV) by combining facial skin temperature data obtained by an infrared sensor with environmental parameters. Subsequently, it performs the corresponding PCS control and adjusts the air conditioner according to the operating state of the PCS. This study proposes a collaborative control strategy with PCS at the core, enabling communication between thermal state recognition, HVAC system, and PCS. Twenty-eight adult males participated in the experiments testing the TECCS's performance. The results indicate that the TECCS can automatically regulate environments at different spatial scales based on thermal sensation prediction and that the operating state of the PCS can effectively guide air conditioning operations. Compared with constant setpoint control, the TECCS offers the advantage of improving thermal comfort. This paper also proposes future optimization directions based on the research results, focusing on recognition, equipment, and control.

Suggested Citation

  • Wu, Yeyu & Jiang, Haihua & Chen, Weiming & Fan, Junhui & Cao, Bin, 2024. "Overall and local environmental collaborative control based on personal comfort model and personal comfort system," Applied Energy, Elsevier, vol. 371(C).
    • Handle: RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010900
    DOI: 10.1016/j.apenergy.2024.123707
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    References listed on IDEAS

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    1. Feng, Yanxiao & Liu, Shichao & Wang, Julian & Yang, Jing & Jao, Ying-Ling & Wang, Nan, 2022. "Data-driven personal thermal comfort prediction: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
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    3. Wang, Junqi & Jiang, Lanfei & Yu, Hanhui & Feng, Zhuangbo & Castaño-Rosa, Raúl & Cao, Shi-jie, 2024. "Computer vision to advance the sensing and control of built environment towards occupant-centric sustainable development: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. Barone, G. & Buonomano, A. & Forzano, C. & Giuzio, G.F. & Palombo, A. & Russo, G., 2023. "A new thermal comfort model based on physiological parameters for the smart design and control of energy-efficient HVAC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    Full references (including those not matched with items on IDEAS)

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