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Energy-saving potential of fresh air management using camera-based indoor occupancy positioning system in public open space

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  • Wang, Huan
  • Liang, Chenjiyu
  • Wang, Guijin
  • Li, Xianting

Abstract

The energy consumption of fresh air systems constitutes a significant proportion of the energy consumption of heating, ventilation, and air-conditioning system therefore, energy savings for fresh air handling units are essential. Typically, fresh air is supplied to a room, regardless of whether the space is occupied. Owing to the development of machine vision technology, camera-based indoor occupancy positioning systems that can provide the correct number and location of indoor occupancies have been developed. Thus, energy conservation for fresh air-handling units can be achieved by not serving vacant areas or by assuming a design load in the working zones. In this study, an occupancy position-oriented fresh air (OPFA) system is proposed to supply fresh air more directly to an occupied zone, thus conserving energy. To determine the location of indoor occupants, a stereo camera-based indoor occupancy positioning system is developed, and its accuracy is verified to be within 50 cm; furthermore, it can operate at only 30 W. To determine the annual energy consumption of the OPFA system, the positions of occupants in a classroom are monitored and recorded for 8 months. Subsequently, the typical occupancy patterns of the areas of interest are extracted via hierarchical clustering analysis. The results show that the total occupied time in the classroom is only 50.6% of the opening time. The most typical usage scenario is a self-study scenario with fewer than three persons. Therefore, typical operation strategies for a fresh air handling unit can be designed based on these typical occupancy scenarios, which can potentially conserve more than one-half of the supplied fresh air volume. The performance of the system is numerically investigated and compared with that of a conventional system. The result shows that the system reduces the total fresh air volume and energy consumption by 60.2% and 67.7%, respectively. This study offers a paradigm for efficiently reducing the energy usage of fresh air systems in multipurpose buildings based on occupancy positions to identify typical indoor occupancy patterns, thus allowing the operation strategies of ventilation systems to be optimized.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017221
    DOI: 10.1016/j.apenergy.2023.122358
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    References listed on IDEAS

    as
    1. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
    2. 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).
    3. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    4. Aviv, Dorit & Chen, Kian Wee & Teitelbaum, Eric & Sheppard, Denon & Pantelic, Jovan & Rysanek, Adam & Meggers, Forrest, 2021. "A fresh (air) look at ventilation for COVID-19: Estimating the global energy savings potential of coupling natural ventilation with novel radiant cooling strategies," Applied Energy, Elsevier, vol. 292(C).
    5. 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).
    6. Aktacir, Mehmet Azmi & Büyükalaca, Orhan & YIlmaz, Tuncay, 2006. "Life-cycle cost analysis for constant-air-volume and variable-air-volume air-conditioning systems," Applied Energy, Elsevier, vol. 83(6), pages 606-627, June.
    7. Pop, Octavian G. & Fechete Tutunaru, Lucian & Bode, Florin & Abrudan, Ancuţa C. & Balan, Mugur C., 2018. "Energy efficiency of PCM integrated in fresh air cooling systems in different climatic conditions," Applied Energy, Elsevier, vol. 212(C), pages 976-996.
    8. Liang, Chenjiyu & Li, Xianting & Zheng, Gonghang, 2022. "Optimizing air conditioning systems by considering the grades of sensible and latent heat loads," Applied Energy, Elsevier, vol. 322(C).
    9. Karunakaran, R. & Iniyan, S. & Goic, Ranko, 2010. "Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings," Applied Energy, Elsevier, vol. 87(4), pages 1158-1175, April.
    10. Zhang, Qinling & Liu, Xiaohua & Zhang, Tao & Xie, Ying, 2020. "Performance optimization of a heat pump driven liquid desiccant dehumidification system using exergy analysis," Energy, Elsevier, vol. 204(C).
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