# A Behaviour-Aware Federated Forecasting Framework for Distributed Stand-Alone Wind Turbines

arXiv: https://arxiv.org/abs/2603.05263 · HTML: https://mlsanity.com/paper/2603.05263/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2211.02674/) (2022)
- [A Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [Federated K-means Clustering](https://mlsanity.com/paper/2310.01195/) (2023)
- [Wind turbine condition monitoring based on intra- and inter-farm federated learning](https://mlsanity.com/paper/2409.03672/) (2024)

## Abstract (arXiv)

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.