As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.

中文翻译：

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改进的基于EMD的风电预测复杂预测模型

为了应对现代电网中风力发电的快速增长，准确的预测模型对于应对相关的不确定性至关重要。由于风力发电的高度波动性和混乱性，必须使用复杂的智能预测工具。因此，本文提出了一种新的改进的经验模式分解（IEMD）版本，以分解风的测量值。将分解后的信号作为输入，输入到基于袋装神经网络（BaNN）和K-means聚类的混合预测模型。此外，一种称为ChB-SSO的新智能优化方法被应用于自动调整BaNN参数。通过与其他知名的预测策略进行全面的比较分析，使用现实世界中风电场案例研究（Alberta和Sotavento）的不同季节子集来测试所提出的预测框架的性能。此外，为了分析所提出框架的有效性，在不同的测试案例中考虑了不同的预测范围。使用了几种错误评估标准，并且获得的结果证明了在所有测试案例中，所提出的风预报方法相对于其他方法的优越性。