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An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system

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  • Mei, Jun
  • Xia, Xiaohua
  • Song, Mengjie

Abstract

This paper presents an autonomous hierarchical control method for a direct expansion air conditioning system. The control objective is to maintain both thermal comfort and indoor air quality at required levels while reducing energy consumption and cost. This control method consists of two layers. The upper layer is an open loop controller that allows obtaining tradeoff steady states by optimizing the energy cost of the direct expansion air conditioning system and the value of predicted mean vote under the time-of-use price structure of electricity. On the other hand, the lower layer designs a model predictive controller, which is in charge of tracking the tradeoff steady states calculated by the upper layer. Control performance of the proposed control method is compared to a conventional control strategy. The results show that the proposed control strategy reduces the energy consumption and energy cost of the direct expansion air conditioning system by 31.38% and 33.85%, respectively, while maintaining both the thermal comfort and indoor air quality within acceptable ranges, which validate the proposed methodology in terms of both comfort and energy efficiency.

Suggested Citation

  • Mei, Jun & Xia, Xiaohua & Song, Mengjie, 2018. "An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 221(C), pages 450-463.
    • Handle: RePEc:eee:appene:v:221:y:2018:i:c:p:450-463
    DOI: 10.1016/j.apenergy.2018.03.162
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    References listed on IDEAS

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    Cited by:

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    2. He, Deqiang & Teng, Xiaoliang & Chen, Yanjun & Liu, Bin & Wang, Heliang & Li, Xianwang & Ma, Rui, 2022. "Energy saving in metro ventilation system based on multi-factor analysis and air characteristics of piston vent," Applied Energy, Elsevier, vol. 307(C).
    3. Raman, Naren Srivaths & Devaprasad, Karthikeya & Chen, Bo & Ingley, Herbert A. & Barooah, Prabir, 2020. "Model predictive control for energy-efficient HVAC operation with humidity and latent heat considerations," Applied Energy, Elsevier, vol. 279(C).
    4. Shao, Junqiang & Huang, Zhiyuan & Chen, Yugui & Li, Depeng & Xu, Xiangguo, 2023. "A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption," Energy, Elsevier, vol. 269(C).
    5. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
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    7. Guangzeng You & Peng Sun & Yi Lei & Donghui Zhang & Haibo Li, 2024. "Optimal Planning of Urban Building-Type Integrated Energy Systems Considering Indoor Somatosensory Comfort and PV Consumption," Sustainability, MDPI, vol. 16(1), pages 1-20, January.
    8. 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.
    9. 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.

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