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Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology

Author

Listed:
  • 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., Zhuhai 519070, China)

  • Xi-Cheng Wang

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

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

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 I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:81-:d:468456
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    References listed on IDEAS

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    1. 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.

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