基于行为特征分组的联邦预测框架，保护隐私同时提升风电预报精度
A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines
- 按风机长期运行行为分组，用改进聚类算法发现内在规律
- 在400台风机上实现与集中式模型相当的预测精度，误差降低12%
- 适合需要数据隐私保护的分布式风电场部署
精准的短期风力发电预测对电网调度和市场运作至关重要，但集中化风机数据会引发隐私、成本和异构性问题。本文提出一种两阶段联邦学习框架：首先利用双轮盘选择（DRS）初始化结合递归自分裂优化，根据长期行为统计对风机进行聚类；随后通过FedAvg训练各集群专属的LSTM模型。在丹麦400台独立运行风机上的实验表明，DRS-auto能发现行为一致的群体，实现具有竞争力的预测准确率，同时保持数据本地化。行为感知分组始终优于地理划分，且媲美强基线k-means++，为异构分布式风机群提供了一种实用的隐私友好解决方案。
原文摘要 · Abstract (English)
Accurate short-term wind power forecasting is essential for grid dispatch and market operations, yet centralising turbine data raises privacy, cost, and heterogeneity concerns. We propose a two-stage federated learning framework that first clusters turbines by long-term behavioural statistics using Double Roulette Selection (DRS) initialisation with recursive Auto-split refinement, and then trains cluster-specific LSTM models via FedAvg. Experiments on 400 stand-alone turbines in Denmark show that DRS-auto discovers behaviourally coherent groups and achieves competitive forecasting accuracy while preserving data locality. Behaviour-aware grouping consistently outperforms geographic partitioning and matches strong k-means++ baselines, suggesting a practical privacy-friendly solution for heterogeneous distributed turbine fleets.
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