Purpose

This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process.

Design/methodology/approach

The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (N_h) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated.

Findings

It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and N_h = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time.

Originality/value

The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.

中文翻译：

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基于参数最优选择的超极限学习机方法在风能应用中的应用

目的

本文旨在通过预测过程来处理基于经典ELM集合（称为Meta-ELM结构参数）的新型极限学习机（ELM）架构的最佳选择。

设计/方法/方法

Meta-ELM体系结构的建模性能根据其所包含的网络参数而有所不同。Meta-ELM参数的选择对于模型的准确性很重要。因此，针对风速预报问题，研究了Meta-ELM参数的最优选择。使用从土耳其的比利奇克和博兹卡阿达站获取的每小时风速数据。不同数量的ELM组（M）和节点（N _h进行分析，以确定Meta-ELM的最佳建模性能。此外，将最佳的Meta-ELM体系结构预测结果与四种不同的学习算法和一种混合的元启发式方法进行了比较。最后，将基于参数之间相关性的线性模型作为三维（3D）给出并进行计算。

发现

可以看出，对于Meta-ELM参数，M = 15 − 20和N _h = 5 − 10 ，该分析具有更好的性能。此外，考虑到性能指标，Meta-ELM模型在所有区域和Levenberg中提供了最佳结果-Marquardt算法-前馈神经网络和自适应神经模糊推理系统-粒子群优化在预测过程中显示出竞争优势。此外，Meta-ELM在经过时间方面提供了更好的结果。

创意/价值

该研究的原始贡献是研究基于预测过程的确定Meta-ELM参数。