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An unsupervised data mining-based framework for evaluation and optimization of operation strategy of HVAC system

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  • Tian, Zhe
  • Lu, Zhonghui
  • Lu, Yakai
  • Zhang, Qiang
  • Lin, Xinyi
  • Niu, Jide

Abstract

The evaluation and optimization of existing operation strategies are significant for energy-saving in heating, ventilation, and air conditioning (HVAC) systems. The personalized characteristics of HVAC systems, however, pose a challenge in developing the baseline. This paper proposes an unsupervised data mining-based framework for the evaluation and optimization of HVAC system operation strategies. The framework involves several steps: firstly, data preprocessing is performed to clean and reduce the raw data. Then, an improved clustering method is used to divide the external conditions of system operation, followed by an association rule algorithm to mine the evaluation baseline for operation strategies. Finally, the low-energy efficiency operating strategies can be identified and optimized using the baseline. Validity of the proposed framework is confirmed by practical cases and data. The results indicate that the mined operation evaluation baseline effectively identifies low-energy efficiency operating strategies that can be improved, resulting in energy savings of 6.9 %. Notably, optimizing the operation number of chillers and water pumps has the best energy-saving potential. Furthermore, compared to model-based operation evaluation methods, this data-driven approach offers advantages such as low computational costs and quick execution.

Suggested Citation

  • Tian, Zhe & Lu, Zhonghui & Lu, Yakai & Zhang, Qiang & Lin, Xinyi & Niu, Jide, 2024. "An unsupervised data mining-based framework for evaluation and optimization of operation strategy of HVAC system," Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:energy:v:291:y:2024:i:c:s0360544223034370
    DOI: 10.1016/j.energy.2023.130043
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    Cited by:

    1. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2024. "Uncertainty analysis of the electric vehicle potential for a household to enhance robustness in decision on the EV/V2H technologies," Applied Energy, Elsevier, vol. 365(C).
    2. Wu, Jiani & Fan, Jianhua & Ma, Tianzeng & Kong, Weiqiang & Chang, Zheshao & Li, Xin, 2024. "Identifying driving factors in cascaded packed bed latent thermal energy storage: An experimental validation," Renewable Energy, Elsevier, vol. 224(C).

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