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HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study

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  • Kong, Meng
  • Dong, Bing
  • Zhang, Rongpeng
  • O'Neill, Zheng

Abstract

Building sensing technologies have evolved rapidly in the last two decades in aid of monitoring building environment and energy system performance. A series of occupancy sensing systems were developed to track the occupant behavior in the indoor space. Occupancy-based building system control is defined as a control method that adjusts the building system operation schedules and setpoints based on the measured occupant behavior and has been identified as a smart building control strategy that can improve building energy efficiency as well as occupant comfort. Some studies demonstrated energy-saving potential and comfort-maintaining capability from occupancy-based control. This study adopted a first-of-its-kind side-by-side experimental approach to quantify the performance of the occupancy-based control in commercial buildings. Three state-of-the-art occupancy sensing technologies were integrated into the real-time Heating, Ventilation, and Air-Conditioning (HVAC) system control in this study. Their detection accuracy and its effectiveness on energy-saving and thermal comfort were analyzed. It was found that the occupancy-based control can maintain good thermal comfort and perceived indoor air quality with a satisfaction ratio greater than 80%. Although the daily energy-saving varied with occupancy sensor accuracy and outdoor environment conditions, the weekly averaged energy saving was between 17 and 24%.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012903
    DOI: 10.1016/j.apenergy.2021.117987
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    References listed on IDEAS

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    1. Naylor, Sophie & Gillott, Mark & Lau, Tom, 2018. "A review of occupant-centric building control strategies to reduce building energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 1-10.
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    5. Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2020. "Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates," Applied Energy, Elsevier, vol. 279(C).
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    Cited by:

    1. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    2. Li, Han & Sang, Tong & Kong, Xiangfei & Zheng, Wandong & Wang, Zhaoying & Li, Jinchao & Wang, Leilei, 2023. "Performance analysis of interactive cascade ventilation combined with solar energy for the epidemic prevention and control," Applied Energy, Elsevier, vol. 349(C).
    3. Guanjing Lin & Armando Casillas & Maggie Sheng & Jessica Granderson, 2023. "Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building," Energies, MDPI, vol. 16(20), pages 1-21, October.
    4. Mahmud, Arafat & Dhrubo, Ehsan Ahmed & Ahmed, S. Shahnawaz & Chowdhury, Abdul Hasib & Hossain, Md. Farhad & Rahman, Hamidur & Masood, Nahid-Al, 2022. "Energy conservation for existing cooling and lighting loads," Energy, Elsevier, vol. 255(C).
    5. 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).
    6. 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).
    7. 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).
    8. Yin, Peng & Xie, Jingchao & Ji, Ying & Liu, Jiaping & Hou, Qixian & Zhao, Shanshan & Jing, Pengfei, 2023. "Winter indoor thermal environment and heating demand of low-quality centrally heated houses in cold climates," Applied Energy, Elsevier, vol. 331(C).
    9. Antonella Yaacoub & Moez Esseghir & Leila Merghem-Boulahia, 2023. "A Review of Different Methodologies to Study Occupant Comfort and Energy Consumption," Energies, MDPI, vol. 16(4), pages 1-18, February.
    10. 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).
    11. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).

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