电力工程技术2026，Vol.45Issue(5)：61-68,156,9.DOI:10.12158/j.2096-3203.2026.05.006
基于安全联邦学习的分布式协同短期负荷预测方法
A distributed collaborative short-term load forecasting method based on secure federated learning
摘要
Abstract
Accurate short-term load forecasting is crucial for efficient grid dispatching and secure operation.Current methodologies predominantly employ centralized training of deep learning-based forecasting models.However,centralized data collection may violate data privacy regulations enforced by power utilities.To address this issue,this paper proposes a secure federated learning based bidirectional long short-term memory network(SFL-Bi-LSTM)to achieve privacy-preserving short-term load distributed collaborative forecasting.Specifically,SFL-Bi-LSTM incorporates a bidirectional long short-term memory network to comprehensively capture temporal features by simultaneously modeling forward and backward time dependencies,thereby enhancing prediction accuracy.To preserve data privacy during collaborative model training across multiple utilities,federated learning(FL)replaces centralized data collection with aggregated model parameters,ensuring raw load data remains local.Furthermore,homomorphic encryption is integrated to enable secure federated aggregation through ciphertext computation,effectively preventing potential reconstruction of raw load data via model parameter inversion attacks.Experimental validation on public datasets demonstrates that the proposed SFL-Bi-LSTM achieves a mean squared error of 2.022 9 MW in distributed collaborative forecasting while maintaining data privacy.Compared to conventional methods,it reduces the average mean squared error across different utilities by 19.89%,demonstrating its generalization capability.关键词
短期负荷预测/隐私保护/时空特征/神经网络/联邦学习/同态加密Key words
short-term load forecasting/privacy protection/temporal-spatial features/neural networks/federated learning/homomorphic encryption分类
信息技术与安全科学引用本文复制引用
邸强,马建功,李青,蔺红..基于安全联邦学习的分布式协同短期负荷预测方法[J].电力工程技术,2026,45(5):61-68,156,9.基金项目
国家自然科学基金资助项目(52367012) （52367012）
新疆维吾尔自治区自然科学基金资助项目(2022A01001-3) （2022A01001-3）
