Cluster-based ensemble learning for wind power modeling with meteorological wind data

TL;DR

This paper presents a novel ensemble learning framework that combines clustering with bagging, boosting, and stacking to improve wind power modeling accuracy using meteorological data, outperforming traditional methods.

Contribution

It introduces a combined clustering and ensemble learning approach for wind power modeling, demonstrating significant performance improvements over existing models.

Findings

Ensemble models with clustering outperform non-clustered models by ~15%.

Farthest first clustering offers rapid computation and ~30% performance gain.

Stacking ensembles with different clusters further improves accuracy by ~5%.

Abstract

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.