English(EN) Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

负载预测的联邦学习通过新的初始化策略得到改进

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-31 04:00

本文解决了由客户负荷数据异构引起的联邦短期负荷预测（STLF）中的挑战。研究人员提出了两种模型初始化策略：一种使用辅助公共数据进行预训练初始化以减少客户漂移，另一种是顺序本地初始化（SLIAvg），该策略在通信轮次内逐步调整模型。这些方法与现有的联邦学习框架兼容，并在真实智能电表数据的实验中证明了预测性能、收敛性和误差的改善。 AI

影响引入新的初始化技术，以提高联邦学习模型在负荷预测等关键基础设施应用中的性能和收敛性。

排序理由详细介绍特定机器学习任务新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Jianing Chen, Vajiheh Farhadi, Yan Li, Thomas La Porta · 2026-08-31 04:00

初始化至关重要：通过模型初始化推进负载异构下的联邦短期负荷预测

arXiv:2608.27791v1 Announce Type: new Abstract: Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies on fine-grained smart-meter data from distributed users, raising increasing con…

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初始化至关重要：通过模型初始化推进负载异构下的联邦短期负荷预测

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