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Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting

机译：基于负相关学习的Relm集合模型与OVMD集成，以进行多步前风速预测

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Accurate and reliable wind speed forecasting is vital in power system scheduling and management. Ensemble techniques are widely employed to enhance wind speed forecasting accuracy. This paper proposes a negative correlation learning-based regularized extreme learning machine ensemble model (NCL-RELM) integrated with optimal variational mode decomposition (OVMD) and sample entropy (SampEn) for multi-step ahead wind speed forecasting. For this purpose, the original wind speed time series is firstly decomposed into a few variational modes and a residue using OVMD, and then the decomposed subseries with approximate SampEn values are aggregated into a new subseries to reduce the computational burden. Secondly, a NCL-RELM ensemble model is employed to model each aggregated subseries. The NCL technique is employed to enhance the diversity among multiple sub-RELM models such that the predictability of a single RELM model can be enhanced. Finally, the prediction results of all subseries are added up to obtain an aggregated result for the original wind speed. The simulation results indicate that: (1) the NCL-RELM model performs better than other ensemble approaches including BAGTREE, BOOST and random forest; (2) the proposed OS-NCL-RELM model obtains the best statistical metrics from 1- to 3-step ahead forecasting compared with the other nine benchmark models. (C) 2020 Elsevier Ltd. All rights reserved.
机译：准确可靠的风速预测在电力系统调度和管理方面至关重要。集合技术被广泛用于提高风速预测精度。本文提出了一种基于负相关的基于基于学习的正则化的极端学习机组集成模型（NCL-Relm），其集成了与最佳变分模式分解（OVMD）和样本熵（Sampen）进行了用于多步前的风速预测。为此目的，原始风速时间序列首先用OVMD分解成几种变分模式和残留物，然后将具有近似幅度值的分解的子系统聚合到新的子系列中以降低计算负担。其次，使用NCL-Relm集合模型来模拟每个聚合的子系列。使用NCL技术来增强多个子relm模型之间的多样性，使得可以提高单个relm模型的可预测性。最后，添加了所有子系的预测结果，以获得原始风速的聚合结果。仿真结果表明：（1）NCL-Relm模型比其他集成方法更好地表现出包括巴格特，升压和随机森林的其他方法; （2）与其他九个基准模型相比，所提出的OS-NCL-Relm模型从1到3步之前获得最佳统计指标。 （c）2020 elestvier有限公司保留所有权利。

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