Cluster-based ensemble learning for wind power modeling with meteorological wind data
This work addresses wind energy generation optimization for turbine control and grid management, but it is incremental as it combines existing ensemble and clustering methods.
The paper tackled wind power modeling by integrating ensemble learning algorithms with clustering approaches, resulting in models that outperformed non-clustered ones by about 15% on average, with the best method achieving around 30% improvement.
Optimal implementation and monitoring of wind energy generation hinge on reliable power modeling that is vital for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea of similar wind condition leads to similar wind power; this paper constructs a modeling scheme that orderly integrates three types of ensemble learning algorithms, bagging, boosting, and stacking, and clustering approaches to achieve optimal power modeling. It also investigates applications of different clustering algorithms and methodology for determining cluster numbers in wind power modeling. The results reveal that all ensemble models with clustering exploit the intrinsic information of wind data and thus outperform models without it by approximately 15% on average. The model with the best farthest first clustering is computationally rapid and performs exceptionally well with an improvement of around 30%. The modeling is further boosted by about 5% by introducing stacking that fuses ensembles with varying clusters. The proposed modeling framework thus demonstrates promise by delivering efficient and robust modeling performance.