论文标题
通过气象风数据进行风能建模的基于群集的合奏学习
Cluster-based ensemble learning for wind power modeling with meteorological wind data
论文作者
论文摘要
对可靠的功率建模的最佳实施和监视，这对于了解涡轮机控制，农场运营优化和电网负载平衡至关重要。基于类似风条件的想法，导致风力相似；本文构建了一种建模方案，该方案有序地集成了三种类型的集合学习算法，装袋，增强和堆叠以及聚类方法，以实现最佳的功率建模。它还研究了不同聚类算法和方法的应用，以确定风能建模中的簇数。结果表明，所有带有聚类的集成模型都利用了风数据的固有信息，因此平均超过了大约15％的模型。最远的第一个聚类的模型在计算上是迅速的，并且表现出色，改善了约30％。通过引入与群集不同的集合融合的堆叠，该建模进一步增强了约5％。因此，所提出的建模框架通过提供高效且健壮的建模性能来证明有希望。
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.
