考虑低通信成本的张量分解个性化联邦学习超短期电力负荷预测

doi:10.19595/j.cnki.1000-6753.tces.251325

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Abstract With the continuous growth of electricity demand, high-precision ultra-short-term load forecasting plays a vital role in improving grid dispatching efficiency, enhancing operational flexibility, and ensuring the reliability of power supply. However, due to the data island problem, individual power suppliers typically have access to only limited user load data, making it difficult to effectively conduct data-driven load forecasting and thus significantly restricting forecasting accuracy. Although centralized data aggregation by power companies can partially alleviate this issue, the growing emphasis on data security has introduced strict regulatory constraints on centralized data collection. Moreover, the potential risk of data leakage during the transmission of critical equipment data remains a crucial concern. Federated learning (FL) provides an effective solution to the data island problem by enabling cross-domain collaboration through distributed joint modeling under the principle of data locality, thereby avoiding both the risk of data leakage during transmission and the high storage costs associated with centralized data collection. Nevertheless, existing FL frameworks often overlook the communication costs incurred during parameter exchanges between clients and the central server throughout the training process. Furthermore, they fail to fully account for the data heterogeneity among clients during global model aggregation, resulting in slow convergence and limited generalization performance.
To address these challenges, this paper proposes a low-communication-cost tensor decomposition-based personalized federated learning method for ultra-short-term load forecasting. The proposed approach aims to achieve efficient and robust distributed forecasting while ensuring data privacy and security. Firstly, a novel tensorized local model based on tensor decomposition is designed to extract and compress spatiotemporal features, thereby enhancing the representational capacity of model parameters and reducing communication costs during FL training. Secondly, a dual-loss optimization framework is constructed to alternately optimize the personalized model and the tensorized local model, constraining their distance to decouple personalized optimization from global model learning. Finally, leveraging the tensor characteristics of model parameters, an efficient personalized FL training strategy and an integrated tensor aggregation mechanism are developed to achieve efficient model training and aggregation, thereby improving the generalization capability of the global model.
To systematically evaluate the comprehensive performance of the proposed model, a series of multi-dimensional comparative experiments were conducted. Firstly, pFedACT was compared with several state-of-the-art federated learning methods in the field of power load forecasting to assess its capability in capturing the spatiotemporal variability of load data. Secondly, the predictive performance of pFedACT was compared with that of centralized modeling approaches to evaluate its learning effectiveness under privacy-preserving conditions. Thirdly, the model's robustness was verified across four dimensions: data imbalance, temporal granularity, load diversity, and scenario variability. Next, a convergence analysis is conducted from two perspectives, namely convergence stability and convergence efficiency, to evaluate the algorithm's behavior during training. Then, by comparing the performance of the global model and personalized models under heterogeneous client data distributions, the generalization ability of pFedACT was examined. Subsequently, the effectiveness of the proposed method in distributed modeling was then validated by comparing the performance differences between various base models trained in centralized settings and within the pFedACT framework. Furthermore, experiments with varying numbers of clients were conducted to evaluate the model's feasibility and scalability across diverse power system scenarios. Finally, a detailed communication cost analysis was performed to quantify the advantages of pFedACT in terms of communication efficiency.
Extensive simulation results demonstrate that the proposed method not only achieves rapid model convergence but also exhibits excellent robustness and generalization capability across power system datasets under various operating conditions. Meanwhile, it enhances prediction stability and reduces communication costs during the training process. The simulation analysis leads to the following conclusions: (1) The proposed pFedACT employs a dual-loss function to impose regularization between the personalized model and the tensorized model, thereby enhancing the model's adaptability to diverse load scenarios and significantly improving both robustness and convergence speed. (2) The tensor decomposition and integrated tensor aggregation strategies strengthen the model's spatiotemporal representation ability, enabling superior generalization performance under non-independent and identically distributed data conditions. (3) Compared with baseline federated learning methods, pFedACT reduces communication costs by approximately 32.23%, and this advantage becomes increasingly prominent as the number of clients grows or the model complexity increases.

Key words： Federated learning tensor decomposition personalized learning strategy low communication cost load forecasting

Received: 26 July 2025

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TM743

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	Luo Guanghao
	Cui Mingjian
	Han Yining
	Huang Yong
	Zhang Jian

Cite this article:

Luo Guanghao,Cui Mingjian,Han Yining等. A Low-Communication-Cost Tensor Decomposition-Based Personalized Federated Learning Method for Ultra-Short-Term Power Load Forecasting[J]. Transactions of China Electrotechnical Society, 2026, 41(17): 5917-5936.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.251325 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I17/5917

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