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Systematic Method for the Energy-Saving Potential Calculation of Air Conditioning Systems via Data Mining. Part II: A Detailed Case Study

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  • Rongjiang Ma

    (Department of Building Science, Tsinghua University, Beijing 100084, China
    School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Shen Yang

    (Department of Building Science, Tsinghua University, Beijing 100084, China
    Human-Oriented Built Environment Lab, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland)

  • Xianlin Wang

    (Department of Building Science, Tsinghua University, Beijing 100084, China
    State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China
    Gree Electric Appliances, Inc. of Zhuhai, Zhuhai 519070, China)

  • Xi-Cheng Wang

    (State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519070, China
    Gree Electric Appliances, Inc. of Zhuhai, Zhuhai 519070, China)

  • Ming Shan

    (Department of Building Science, Tsinghua University, Beijing 100084, China)

  • Nanyang Yu

    (School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Xudong Yang

    (Department of Building Science, Tsinghua University, Beijing 100084, China)

Abstract

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process.

Suggested Citation

  • Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air Conditioning Systems via Data Mining. Part II: A Detailed Case Study," Energies, MDPI, vol. 14(1), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:86-:d:468518
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    References listed on IDEAS

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    1. Rongjiang Ma & Xianlin Wang & Ming Shan & Nanyang Yu & Shen Yang, 2020. "Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data," Energies, MDPI, vol. 13(18), pages 1-14, September.
    2. Sarafraz, M.M. & Tlili, I. & Tian, Zhe & Bakouri, Mohsen & Safaei, Mohammad Reza, 2019. "Smart optimization of a thermosyphon heat pipe for an evacuated tube solar collector using response surface methodology (RSM)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    3. Amoah B.O. Kwame & Nguyen V. Troy & Najafi Hamidreza, 2020. "A Multi-Facet Retrofit Approach to Improve Energy Efficiency of Existing Class of Single-Family Residential Buildings in Hot-Humid Climate Zones," Energies, MDPI, vol. 13(5), pages 1-26, March.
    4. Li, Guannan & Hu, Yunpeng & Chen, Huanxin & Li, Haorong & Hu, Min & Guo, Yabin & Liu, Jiangyan & Sun, Shaobo & Sun, Miao, 2017. "Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions," Applied Energy, Elsevier, vol. 185(P1), pages 846-861.
    5. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
    6. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
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