[2603.05263] A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines

Computer Science > Machine Learning arXiv:2603.05263 (cs) [Submitted on 5 Mar 2026] Title:A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines Authors:Bowen Li, Xiufeng Liu, Maria Sinziiana Astefanoaei View a PDF of the paper titled A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines, by Bowen Li and 2 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.05263 [cs.LG]   (or arXiv:2603.05263v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.05263 Focus to learn more arXiv-issued DOI via DataCite (pending ...