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Personalized federated learning for household electricity load prediction with imbalanced historical data

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  • Zhu, Shibo
  • Shi, Xiaodan
  • Zhao, Huan
  • Chen, Yuntian
  • Zhang, Haoran
  • Song, Xuan
  • Wu, Tianhao
  • Yan, Jinyue

Abstract

Household consumption accounts for about one-third of global electricity. Accurate results of household load prediction would help in energy management at both the building and the grid levels. Data-driven household load prediction methods have shown great advantages and potential in terms of accuracy. However, these methods still face challenges such as limited data for individual households, diversified electricity consumption behaviors, and data privacy concerns. To solve these problems, this paper proposes a personalized federated learning household load prediction framework (PF-HoLo), which allows personal models to learn collectively, leverages multisource data to capture diverse consumption behaviors, and ensures data privacy. In addition, the global encoder model and mutual learning are proposed to enhance the performance of the PF-HoLo framework considering imbalanced residential historical data. Ablation experiments results prove that the PF-HoLo framework could achieve significant improvements, with 13.41% Mean Square Error and 11.33% Mean Absolute Error, compared to traditional federated learning methods.

Suggested Citation

  • Zhu, Shibo & Shi, Xiaodan & Zhao, Huan & Chen, Yuntian & Zhang, Haoran & Song, Xuan & Wu, Tianhao & Yan, Jinyue, 2025. "Personalized federated learning for household electricity load prediction with imbalanced historical data," Applied Energy, Elsevier, vol. 384(C).
    • Handle: RePEc:eee:appene:v:384:y:2025:i:c:s0306261925001497
    DOI: 10.1016/j.apenergy.2025.125419
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    References listed on IDEAS

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    1. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
    2. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    3. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    4. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    5. Li, Ke & Mu, Yuchen & Yang, Fan & Wang, Haiyang & Yan, Yi & Zhang, Chenghui, 2023. "A novel short-term multi-energy load forecasting method for integrated energy system based on feature separation-fusion technology and improved CNN," Applied Energy, Elsevier, vol. 351(C).
    6. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    7. Wang, Fei & Lu, Xiaoxing & Chang, Xiqiang & Cao, Xin & Yan, Siqing & Li, Kangping & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data," Energy, Elsevier, vol. 238(PB).
    8. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
    9. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    10. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    11. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    Full references (including those not matched with items on IDEAS)

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