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Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution

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  • Wang, Wei
  • Chen, Jiayu
  • Huang, Gongsheng
  • Lu, Yujie

Abstract

Since commercial and residential buildings have become the largest energy consumers across all sectors, building energy efficiency has attracted increasing attention in recent years. Many studies suggest occupancy detection is critical in promoting building energy efficiency because it is premised on the idea of avoiding unnecessary waste while providing sufficient service. In this paper, we propose the integration of an iBeacon-enabled indoor positioning system (IPS) and a Variable Air Volume (VAV) heating, ventilation, and air-conditioning (HVAC) system to optimize system control and save energy based on high-resolution occupancy detection. The proposed system aims to match thermal service with the spatial distribution of occupants and redefine occupancy as a dynamic spatial occupancy distribution (DSOD) occupancy matrix. For this reason, this paper proposes measuring spatial occupancy by meshing large indoor spaces into zones and patches, and uses a feature-scaled artificial neural network algorithm to map the spatial IPS signal patterns. After acquiring the detailed spatial distribution, we also developed a ventilation control mechanism based on occupancy distribution. To validate the proposed control mechanism, we compared it with other traditional controllers in an on-site experiment and through a computational fluid dynamics (CFD) simulation. The results suggest that a 20% energy saving potential can be realized when the proposed approach is properly implemented.

Suggested Citation

  • Wang, Wei & Chen, Jiayu & Huang, Gongsheng & Lu, Yujie, 2017. "Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution," Applied Energy, Elsevier, vol. 207(C), pages 305-323.
    • Handle: RePEc:eee:appene:v:207:y:2017:i:c:p:305-323
    DOI: 10.1016/j.apenergy.2017.06.060
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    References listed on IDEAS

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    Cited by:

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    6. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    7. Zhang, Sheng & Cheng, Yong & Liu, Jian & Lin, Zhang, 2019. "Subzone control optimization of air distribution for thermal comfort and energy efficiency under cooling load uncertainty," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    9. Soltanaghaei, Elahe & Whitehouse, Kamin, 2018. "Practical occupancy detection for programmable and smart thermostats," Applied Energy, Elsevier, vol. 220(C), pages 842-855.
    10. Wang, Wei & Hong, Tianzhen & Li, Nan & Wang, Ryan Qi & Chen, Jiayu, 2019. "Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification," Applied Energy, Elsevier, vol. 236(C), pages 55-69.
    11. Zhang, Sheng & Cheng, Yong & Oladokun, Majeed Olaide & Huan, Chao & Lin, Zhang, 2019. "Heat removal efficiency of stratum ventilation for air-side modulation," Applied Energy, Elsevier, vol. 238(C), pages 1237-1249.
    12. Rashid, Syed Aftab & Haider, Zeeshan & Chapal Hossain, S.M. & Memon, Kashan & Panhwar, Fazil & Mbogba, Momoh Karmah & Hu, Peng & Zhao, Gang, 2019. "Retrofitting low-cost heating ventilation and air-conditioning systems for energy management in buildings," Applied Energy, Elsevier, vol. 236(C), pages 648-661.
    13. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    14. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    15. Pal, Monalisa & Alyafi, Amr Alzouhri & Ploix, Stéphane & Reignier, Patrick & Bandyopadhyay, Sanghamitra, 2019. "Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook," Applied Energy, Elsevier, vol. 238(C), pages 1452-1470.
    16. Liu, Yang & Sun, Yongjun & Gao, Dian-ce & Tan, Jiaqi & Chen, Yuxin, 2024. "Stacked ensemble learning approach for PCM-based double-pipe latent heat thermal energy storage prediction towards flexible building energy," Energy, Elsevier, vol. 294(C).

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