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The indoor heating set temperature of the VRF (Variable Refrigerant Flow) system has a significant impact on energy consumption and thermal comfort. The study aimed to classify and characterize heating set temperature operation modes based on big data. It analysed 13,662 heating operation process data, extracting features of temperature difference coefficient and heating duration, and employing these features for occupant behaviour classification. It found that two main heating set temperature operation modes. The first kind of users preferred higher set temperatures (30 ℃) for rapid heating and subsequently switched off the VRF indoor unit upon reaching thermal comfort, which accounted for 91% heating operation processes. The second kind of users preferred automatic control by VRF indoor unit for thermal comfort. The study revealed the occupant behaviour patterns and indoor temperature conditions during VRF heating operations, highlighting the potential for energy savings through appropriate setpoint strategies and controlling optimisation.

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Heating Temperature-Setting Behaviour of VRF Systems Based on Operation Big Data

  • 会议论文

  • 发表日期：

    2026年

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  • Mingyang QIAN,
  • Yi WU,
  • Hua LIU,
  • Da YAN

摘要

The indoor heating set temperature of the VRF (Variable Refrigerant Flow) system has a significant impact on energy consumption and thermal comfort. The study aimed to classify and characterize heating set temperature operation modes based on big data. It analysed 13,662 heating operation process data, extracting features of temperature difference coefficient and heating duration, and employing these features for occupant behaviour classification. It found that two main heating set temperature operation modes. The first kind of users preferred higher set temperatures (30 ℃) for rapid heating and subsequently switched off the VRF indoor unit upon reaching thermal comfort, which accounted for 91% heating operation processes. The second kind of users preferred automatic control by VRF indoor unit for thermal comfort. The study revealed the occupant behaviour patterns and indoor temperature conditions during VRF heating operations, highlighting the potential for energy savings through appropriate setpoint strategies and controlling optimisation.