初始化至关重要：通过模型初始化推进负载异质性下的联邦短期负荷预测
Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization
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- School of Electrical Engineering and Computer Science, The Pennsylvania State University（宾夕法尼亚州立大学电气工程与计算机科学学院）
- Department of Electrical and Computer Engineering, Bucknell University（巴克内尔大学电气与计算机工程系）
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中文总结 AI 辅助
针对联邦短期负荷预测中客户端负载数据的结构化异质性问题，该研究提出全局预训练初始化与局部SLIAvg策略，有效减少客户端漂移并提升预测性能，且兼容性强。
中文摘要 AI 辅助
短期负荷预测（STLF）为现代电力系统的众多应用提供关键信息，然而准确的STLF往往依赖分布式用户的细粒度智能电表数据，引发了对数据隐私的日益关注。因此，联邦学习（FL）作为一种有前景的隐私保护范式被应用于STLF。但本文揭示了客户端负载数据中存在结构化异质性：客户端对外部因素表现出不同的响应，且具有不同的时间负载曲线，这会降低FL下的预测性能。为缓解这些问题，本文研究了模型初始化在联邦STLF中的作用，并从全局和局部视角提出两种初始化策略。对于全局模型初始化，当有辅助公共负载数据时，开发预训练初始化策略，在联邦训练前初始化全局模型，从而减少训练过程中的客户端漂移；对于局部模型初始化，提出SLIAvg，一种顺序局部初始化策略，通过让参与客户端在每轮通信中从逐步适配的模型开始，促进更一致的训练过程。由于所提策略仅修改初始化过程，它们与大多数现有FL框架和隐私增强技术兼容。在具有两种代表性预测架构的真实智能电表数据上进行的实验表明，所提策略有效提升了预测性能，表现为客户端漂移减少、收敛行为改善以及预测误差降低。
英文摘要
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 concerns about data privacy. Federated learning (FL) has therefore emerged as a promising privacy-preserving paradigm for STLF. Nevertheless, this paper reveals structured heterogeneity in clients' load data. Specifically, clients exhibit different responses to exogenous factors and distinct temporal load profiles, which can degrade forecasting performance in FL. To mitigate these issues, this paper studies the role of model initialization in federated STLF, and proposes two initialization strategies from global and local perspectives. For global model initialization, when auxiliary public load data are available, a pretrained initialization strategy is developed to initialize the global model before federated training, thereby reducing client drift during the training process. For local model initialization, we propose SLIAvg, a sequential local initialization strategy that promotes a more consistent training process by allowing participating clients to start from progressively adapted models within each communication round. Since the proposed strategies only modify the initialization process, they are compatible with most existing FL frameworks and privacy-enhancing techniques. Experiments on real smart-meter data with two representative forecasting architectures demonstrate that the proposed strategies effectively improve forecasting performance, as evidenced by reduced client drift, improved convergence behavior, and lower forecasting errors.
