Abstract At present, accurate forecast of very-short-term wind speed is still a critical issue, mainly due to the complex characteristics of wind variations such as intermittence, fluctuation and randomness. On this topic, our paper contributes to the development of an effective multi-step forecasting method termed ECKIE, which provides multi-step forecast for the very-short-term wind speed in specific stations. This method consists of three stages: a data filtering process driven by the ensemble empirical mode decomposition (EEMD), an improved K-harmonic mean (KHM) clustering optimized by the Cuckoo search (CS) algorithm and a single-hidden-layer feedforward network (SLFN) trained by the incremental extreme learning machine (IELM) algorithm. The developed method is capable of clustering the model inputs into groups according to their characteristics and of constructing the models for each group. It is further capable of reducing forecasting errors by choosing a suitable model. It is a purely data-driven process and is an effective method for very-short-term wind speed forecasts. The simulation demonstrates that the developed method drastically improves upon original model performance and performs the best among comparable models.

中文翻译：

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基于样本聚类和优化混合系统的多步风速预测

摘要 目前，极短期风速的准确预测仍然是一个关键问题，主要是由于风变具有间歇性、波动性和随机性等复杂特征。在这个主题上，我们的论文有助于开发一种称为 ECKIE 的有效多步预测方法，该方法为特定站点的极短期风速提供多步预测。该方法由三个阶段组成：由集成经验模态分解（EEMD）驱动的数据过滤过程、由 Cuckoo 搜索（CS）算法优化的改进 K 谐波均值（KHM）聚类和单隐藏层前馈网络（SLFN）由增量极限学习机（IELM）算法训练。所开发的方法能够根据模型输入的特征将模型输入聚类到组中，并为每个组构建模型。它还能够通过选择合适的模型来减少预测错误。这是一个纯数据驱动的过程，是非常短期风速预测的有效方法。仿真表明，所开发的方法大大提高了原始模型的性能，并且在可比模型中表现最好。