  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.
