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A method for energy consumption optimization of air conditioning systems based on load prediction and energy flexibility

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Listed:
  • Li, Wenqiang
  • Gong, Guangcai
  • Ren, Zhongjun
  • Ouyang, Qianwu
  • Peng, Pei
  • Chun, Liang
  • Fang, Xi

Abstract

A new method for heating ventilation and air conditioning (HVAC) energy consumption optimization based on load prediction and energy flexibility is proposed. First, the energy consumption prediction of the chillers and air conditioning terminals is made. Then, an optimal chiller loading (OCL) equation is built, and is new in the following aspects: the electricity consumption of air conditioning terminals is included and amended by a penalty coefficient to consider thermal comfort. This penalty coefficient is calculated based on energy flexibility. The prediction results are used as constraints of the OCL equation. Next, the sensitiveness of the system's energy consumption with different penalty coefficients and different settled comfort air temperatures are tested. All cases are solved by the particle swarm optimization (PSO) algorithm and validated by the genetic algorithm (GA). Finally, economic analyses are made. The results show that the comprehensive energy-saving ratio is about 10%, and the discounted payback value is 5.8 years. The penalty coefficient is more sensitive than the settled comfort air temperature for the system's energy saving. This proposed method is significant for improving the reliability of the feedforward control strategy and reducing the response time of the feedback control strategy.

Suggested Citation

  • Li, Wenqiang & Gong, Guangcai & Ren, Zhongjun & Ouyang, Qianwu & Peng, Pei & Chun, Liang & Fang, Xi, 2022. "A method for energy consumption optimization of air conditioning systems based on load prediction and energy flexibility," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544222000147
    DOI: 10.1016/j.energy.2022.123111
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    References listed on IDEAS

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    Cited by:

    1. Lin Pan & Sheng Wang & Jiying Wang & Min Xiao & Zhirong Tan, 2022. "Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load," Energies, MDPI, vol. 15(24), pages 1-31, December.
    2. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    3. Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
    4. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    5. Gharibvand, Hossein & Gharehpetian, G.B. & Anvari-Moghaddam, A., 2024. "A survey on microgrid flexibility resources, evaluation metrics and energy storage effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    6. Jinfeng Shi & Haoyang Liu & Xiaowei Yang, 2024. "Precision Control for Room Temperature of Variable Air Volume Air-Conditioning Systems with Large Input Delay," Energies, MDPI, vol. 17(17), pages 1-16, August.

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