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Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving

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  • Eom, Yong Hwan
  • Yoo, Jin Woo
  • Hong, Sung Bin
  • Kim, Min Soo

Abstract

In heat pumps, refrigerant leakage is one of the frequent faults. Since the systems have the best performance at the optimal charge, it is essential to predict refrigerant charge amount. Hence, the refrigerant charge fault detection (RCFD) methods have been developed by researchers. Due to improvements in computing speed and big-data, data-driven techniques such as artificial neural networks (ANNs) have been highlighted recently. However, most existing ANN-based RCFD methods use low-performance shallow neural networks (SNNs) and require the features extracted by experts’ experiences. Also, they have some critical limitations. First, they cannot provide quantitative information on recharge amount due to a simple classification such as undercharge or overcharge. Second, many ANN-based RCFD methods can be used in one operation mode (cooling or heating mode). To improve the limitations, a novel RCFD strategy based on convolutional neural networks (CNNs) was suggested. Two prediction models using classification and regression can predict the quantitative refrigerant amount in both cooling and heating mode with a single model. The mean accuracy of the CNN-based classification model was 99.9% for the learned cases. Also, the CNN-based regression model showed the excellent prediction performance with root-mean-square (RMS) error of 3.1% including the untrained refrigerant charge amount data.

Suggested Citation

  • Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:energy:v:187:y:2019:i:c:s036054421931549x
    DOI: 10.1016/j.energy.2019.115877
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    References listed on IDEAS

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    1. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
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    Cited by:

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    12. Weigert, Andreas & Hopf, Konstantin & Günther, Sebastian A. & Staake, Thorsten, 2022. "Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data," Energy Policy, Elsevier, vol. 169(C).
    13. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
    14. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
    15. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Samuel Boahen & Kwesi Mensah & Selorm Kwaku Anka & Kwang Ho Lee & Jong Min Choi, 2021. "Fault Detection Algorithm for Multiple-Simultaneous Refrigerant Charge and Secondary Fluid Flow Rate Faults in Heat Pumps," Energies, MDPI, vol. 14(13), pages 1-19, June.
    17. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2023. "Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations," Energy, Elsevier, vol. 282(C).

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