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Operational optimisation of domestic refrigerators based on user behaviour prediction and deep reinforcement learning.

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Title: Operational optimisation of domestic refrigerators based on user behaviour prediction and deep reinforcement learning.
Authors: Li, Hao-Ran1 (AUTHOR), Zhang, Wei1,2 (AUTHOR) Weizhang@zju.edu.cn, Chen, Qiang3 (AUTHOR)
Source: International Journal of Refrigeration. Oct2025, Vol. 178, p479-487. 9p.
Subjects: Reinforcement learning, Energy consumption, Thermal stability, Energy conservation, Human behavior models, Refrigerators, Feedback control systems
Abstract: • PreLSAC: an RL-based control strategy for domestic refrigerators. • Predictive information is used to anticipate user behaviour and loads. • PreLSAC reduces energy use and improves temperature stability. • Achieves up to 28.7 % energy savings over one-week operation. • Combines RL and prediction for efficient, sustainable control. The refrigerator holds a significant place in domestic energy usage as a 24-hour operating appliance. Thus, one of the current research hotspots is the development of advanced energy-saving control systems. This study proposes a control technique based on user behaviour prediction and deep reinforcement learning. It addresses the issue that existing control methods struggle to identify the optimal settings among components and fail to account for the influence of user behaviour on energy consumption. First, Transformer coding in conjunction with Kolmogorov-Arnold Networks (KAN) is used to build a prediction system for user door-opening and closing behaviour. This method outperforms the baseline KAN by more than 85 %. Based on this, an enhanced Pre-LSTM-SAC (PreLSAC) method is proposed to combine the Long Short-Term Memory (LSTM) networks and prediction information, thereby enhancing the agent's control capacity during refrigerator operation. According to experimental results, PreLSAC has a considerable advantage over conventional control methods in terms of temperature control and energy consumption reduction. It also shows good generalisation and robustness under various thermal loads and random door-opening and closing events, indicating its potential for use in real-world scenarios. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Refrigeration is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 187322392
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Operational optimisation of domestic refrigerators based on user behaviour prediction and deep reinforcement learning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Hao-Ran%22">Li, Hao-Ran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wei%22">Zhang, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> Weizhang@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Qiang%22">Chen, Qiang</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Refrigeration%22">International Journal of Refrigeration</searchLink>. Oct2025, Vol. 178, p479-487. 9p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+stability%22">Thermal stability</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+conservation%22">Energy conservation</searchLink><br /><searchLink fieldCode="DE" term="%22Human+behavior+models%22">Human behavior models</searchLink><br /><searchLink fieldCode="DE" term="%22Refrigerators%22">Refrigerators</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+systems%22">Feedback control systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • PreLSAC: an RL-based control strategy for domestic refrigerators. • Predictive information is used to anticipate user behaviour and loads. • PreLSAC reduces energy use and improves temperature stability. • Achieves up to 28.7 % energy savings over one-week operation. • Combines RL and prediction for efficient, sustainable control. The refrigerator holds a significant place in domestic energy usage as a 24-hour operating appliance. Thus, one of the current research hotspots is the development of advanced energy-saving control systems. This study proposes a control technique based on user behaviour prediction and deep reinforcement learning. It addresses the issue that existing control methods struggle to identify the optimal settings among components and fail to account for the influence of user behaviour on energy consumption. First, Transformer coding in conjunction with Kolmogorov-Arnold Networks (KAN) is used to build a prediction system for user door-opening and closing behaviour. This method outperforms the baseline KAN by more than 85 %. Based on this, an enhanced Pre-LSTM-SAC (PreLSAC) method is proposed to combine the Long Short-Term Memory (LSTM) networks and prediction information, thereby enhancing the agent's control capacity during refrigerator operation. According to experimental results, PreLSAC has a considerable advantage over conventional control methods in terms of temperature control and energy consumption reduction. It also shows good generalisation and robustness under various thermal loads and random door-opening and closing events, indicating its potential for use in real-world scenarios. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Refrigeration is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ijrefrig.2025.07.016
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 479
    Subjects:
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Thermal stability
        Type: general
      – SubjectFull: Energy conservation
        Type: general
      – SubjectFull: Human behavior models
        Type: general
      – SubjectFull: Refrigerators
        Type: general
      – SubjectFull: Feedback control systems
        Type: general
    Titles:
      – TitleFull: Operational optimisation of domestic refrigerators based on user behaviour prediction and deep reinforcement learning.
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          Name:
            NameFull: Li, Hao-Ran
      – PersonEntity:
          Name:
            NameFull: Zhang, Wei
      – PersonEntity:
          Name:
            NameFull: Chen, Qiang
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          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 01407007
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              Value: 178
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            – TitleFull: International Journal of Refrigeration
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