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<p>Following the trend of electricity marketization, the power distribution system needs to conduct accurate load forecasting to improve regional power distribution strategies. Meanwhile, power users emphasize the protection of electricity data privacy. It is challenging to simultaneously meet the requests for accurate regional-level load forecasting for distribution systems and fine-grained user-level load forecasting for power users. In this paper, we develop a multi-level short-term load forecasting (STLF) model based on an improved federated learning (FL) framework aimed at serving both distribution systems and power users. To construct a local load prediction model, the weather and historical load data are utilized, leveraging the strong correlation between short-term power load data and long sequence. A feature attention mechanism-enhanced bidirectional superimposed recurrent neural network (Fam-Bi-SRNN) is proposed. The federated average algorithm (FedAvg) and adaptive sample weighting (ASW) strategy are combined to build an improved FL prediction framework that integrates regional-level and user-level weight adjustments. Based on simulation examples using 96 real-time load datasets, the model has achieved a significant improvement in communication efficiency. Regional-level and user-level root mean square errors (RMSE) reach 0.025 and 0.018, outperforming the traditional algorithms. Simulation results show the model’s ability to ensure rapid training, high accuracy, and multi-level prediction while protecting user data privacy.</p>

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Multi-level short-term load forecasting model based on an improved federated learning framework

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Yu Yuan,
  • Shipeng Zhang,
  • Chao Yang,
  • Wenxuan Peng,
  • Weipeng Li,
  • Weifeng Zhong,
  • Xin Chen

摘要

Following the trend of electricity marketization, the power distribution system needs to conduct accurate load forecasting to improve regional power distribution strategies. Meanwhile, power users emphasize the protection of electricity data privacy. It is challenging to simultaneously meet the requests for accurate regional-level load forecasting for distribution systems and fine-grained user-level load forecasting for power users. In this paper, we develop a multi-level short-term load forecasting (STLF) model based on an improved federated learning (FL) framework aimed at serving both distribution systems and power users. To construct a local load prediction model, the weather and historical load data are utilized, leveraging the strong correlation between short-term power load data and long sequence. A feature attention mechanism-enhanced bidirectional superimposed recurrent neural network (Fam-Bi-SRNN) is proposed. The federated average algorithm (FedAvg) and adaptive sample weighting (ASW) strategy are combined to build an improved FL prediction framework that integrates regional-level and user-level weight adjustments. Based on simulation examples using 96 real-time load datasets, the model has achieved a significant improvement in communication efficiency. Regional-level and user-level root mean square errors (RMSE) reach 0.025 and 0.018, outperforming the traditional algorithms. Simulation results show the model’s ability to ensure rapid training, high accuracy, and multi-level prediction while protecting user data privacy.