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A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions

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  • Nam, KiJeon
  • Heo, SungKu
  • Li, Qian
  • Loy-Benitez, Jorge
  • Kim, MinJeong
  • Park, DuckShin
  • Yoo, ChangKyoo

Abstract

Passengers are directly or indirectly exposed to fine dust in indoor air of underground subway stations, which greatly affects the comfort and health of the passengers. However, conventional ventilation systems are manually operated, resulting in high energy consumption, and it is hard to consider the dynamic characteristics of indoor air quality due to the complex relationships between the environment of the subway station and climate change-driven outdoor air quality. Therefore, an energy-efficient ventilation optimization system based on deep learning and artificial intelligence (AI)-iterative dynamic programming was developed in this study for proactive environmental and economic maintenance of the underground ventilation system for a subway’s indoor air quality. The deep learning model predicted the next 24 h of the subway’s environmental status, and the AI-iterative dynamic programming searched a piecewise operational policy of ventilation flow rate for the same operational duration. Energy efficiency was improved by 8.68% while maintaining healthy indoor air quality for the passengers. The proactive optimal ventilation system for the platform of a target subway station presented a decrease of 96 tons of CO2 per year to help address climate change and operating expenditure savings of up to $4217 dollars per year.

Suggested Citation

  • Nam, KiJeon & Heo, SungKu & Li, Qian & Loy-Benitez, Jorge & Kim, MinJeong & Park, DuckShin & Yoo, ChangKyoo, 2020. "A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions," Applied Energy, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:appene:v:266:y:2020:i:c:s0306261920304050
    DOI: 10.1016/j.apenergy.2020.114893
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    1. Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2016. "Development of a statistical analysis model to benchmark the energy use intensity of subway stations," Applied Energy, Elsevier, vol. 179(C), pages 488-496.
    2. Liang, Zhengtang & Liang, Jun & Zhang, Li & Wang, Chengfu & Yun, Zhihao & Zhang, Xu, 2015. "Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis," Applied Energy, Elsevier, vol. 159(C), pages 51-61.
    3. Liu, Minzhang & Zhu, Chunguang & Zhang, Huan & Zheng, Wandong & You, Shijun & Campana, Pietro Elia & Yan, Jinyue, 2019. "The environment and energy consumption of a subway tunnel by the influence of piston wind," Applied Energy, Elsevier, vol. 246(C), pages 11-23.
    4. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    5. Wang, Yamin & Wu, Lei, 2016. "On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation," Energy, Elsevier, vol. 112(C), pages 208-220.
    6. Zhang, Wen-Yao & Wei, Zong-Wen & Wang, Bing-Hong & Han, Xiao-Pu, 2016. "Measuring mixing patterns in complex networks by Spearman rank correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 440-450.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Zhongzhe Chen & Baqiao Liu & Xiaogang Yan & Hongquan Yang, 2019. "An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition," Energies, MDPI, vol. 12(16), pages 1-12, August.
    9. Gangolells, Marta & Casals, Miquel & Forcada, Núria & Macarulla, Marcel & Giretti, Alberto, 2016. "Energy performance assessment of an intelligent energy management system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 662-667.
    10. Chen, Jianli & Brager, Gail S. & Augenbroe, Godfried & Song, Xinyi, 2019. "Impact of outdoor air quality on the natural ventilation usage of commercial buildings in the US," Applied Energy, Elsevier, vol. 235(C), pages 673-684.
    11. Dedinec, Aleksandar & Markovska, Natasa & Taseska, Verica & Duic, Neven & Kanevce, Gligor, 2013. "Assessment of climate change mitigation potential of the Macedonian transport sector," Energy, Elsevier, vol. 57(C), pages 177-187.
    12. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
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    Cited by:

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    2. Nishant Raj Kapoor & Ashok Kumar & Tabish Alam & Anuj Kumar & Kishor S. Kulkarni & Paolo Blecich, 2021. "A Review on Indoor Environment Quality of Indian School Classrooms," Sustainability, MDPI, vol. 13(21), pages 1-43, October.
    3. Yu, Yanzhe & You, Shijun & Zhang, Huan & Ye, Tianzhen & Wang, Yaran & Wei, Shen, 2021. "A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    4. 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).
    5. 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).
    6. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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