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A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption

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  • Shao, Junqiang
  • Huang, Zhiyuan
  • Chen, Yugui
  • Li, Depeng
  • Xu, Xiangguo

Abstract

The large ownership of direct-expansion (DX) air-conditioning (A/C) systems in small and medium-sized buildings brings with it the need to reduce their energy consumption without damaging the thermal comfort of the occupants. Model predictive control (MPC) is an effective method to optimally control the operation of air-conditioners. However, most existing MPC methods require the investment of additional equipment and labor-intensive work, which greatly increases the cost of MPC and hinders its practical application. To solve the problem, this paper presents an economical and practical MPC algorithm for DX A/C systems, capable of achieving a balance between thermal comfort and energy saving. The proposed algorithm was experimentally validated on both an experimental DX A/C system and a market available split-type air-conditioner. Experimental results on the experimental DX A/C system show that temperature and humidity set-points selected at α = 1 saved 23.3% of energy consumption compared to those selected at α = 0, while keeping indoor thermal comfort within acceptable range. And results on the split-type air-conditioner demonstrate energy savings of up to more than 32% compared to the baseline and proved that the algorithm can be practically applied on market available D/X air-conditioners.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001421
    DOI: 10.1016/j.energy.2023.126748
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    References listed on IDEAS

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    1. Lee, Zachary E. & Zhang, K. Max, 2021. "Scalable identification and control of residential heat pumps: A minimal hardware approach," Applied Energy, Elsevier, vol. 286(C).
    2. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2021. "Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems," Applied Energy, Elsevier, vol. 297(C).
    3. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    4. Juricic, Sarah & Goffart, Jeanne & Rouchier, Simon & Foucquier, Aurélie & Cellier, Nicolas & Fraisse, Gilles, 2021. "Influence of natural weather variability on the thermal characterisation of a building envelope," Applied Energy, Elsevier, vol. 288(C).
    5. Knudsen, Michael Dahl & Georges, Laurent & Skeie, Kristian Stenerud & Petersen, Steffen, 2021. "Experimental test of a black-box economic model predictive control for residential space heating," Applied Energy, Elsevier, vol. 298(C).
    6. Hui, Hongxun & Ding, Yi & Liu, Weidong & Lin, You & Song, Yonghua, 2017. "Operating reserve evaluation of aggregated air conditioners," Applied Energy, Elsevier, vol. 196(C), pages 218-228.
    7. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
    8. 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.
    9. Li, Ao & Xiao, Fu & Zhang, Chong & Fan, Cheng, 2021. "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, Elsevier, vol. 299(C).
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