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Short-term wind speed prediction using time varying filter-based empirical mode decomposition and group method of data handling-based hybrid model
Energy Conversion and Management ( IF 10.4 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.enconman.2020.113076
Yan Jiang , Shuoyu Liu , Ning Zhao , Jingzhou Xin , Bo Wu

Abstract The realization of precise and reliable short-term wind speed prediction is extremely essential to wind power development, especially for its integration into traditional grid system. For this purpose, this study develops a novel forecasting method based on time varying filter-based empirical mode decomposition, auto-regressive integrated moving average model and group method of data handling-based hybrid model. This method mainly contains four individual steps for grasping the major behavioral characteristics of wind speed data. The first step adopts time varying filter-based empirical mode decomposition to handle the nonlinearity and nonstationarity of the raw wind speed data by decomposing them into a number of subseries with more stability and regularity. Then, auto-regressive integrated moving average model is applied to depict the linear characteristic hidden in the data. For the above modeling errors (i.e., the nonlinear residuals), the third step employs three nonlinear models with different action mechanisms (i.e., least square support vector machine, genetic programming algorithm and spatio-temporal radial basis function neural network) to systematically capture their complex nonlinear features. Finally, group method of data handling neural network is utilized to combine these nonlinear models and perform the selective prediction, where the involved models and their weights could be determined automatically. Four groups of the measured wind speed datasets with two different time intervals are used to assess the performance of the proposed method. The experimental results indicate it outperforms the other compared models and may have great potential for the practical application in power system.

中文翻译:

使用基于时变滤波器的经验模式分解和基于数据处理的混合模型的组方法的短期风速预测

摘要 实现精确可靠的短期风速预测对于风电的发展尤其是与传统电网系统的整合至关重要。为此,本研究开发了一种基于时变滤波器的经验模式分解、自回归综合移动平均模型和基于数据处理的混合模型的组方法的新型预测方法。该方法主要包含四个单独的步骤,用于掌握风速数据的主要行为特征。第一步采用基于时变滤波器的经验模态分解来处理原始风速数据的非线性和非平稳性,将它们分解成多个更稳定和更有规律的子序列。然后,应用自回归积分移动平均模型来刻画隐藏在数据中的线性特征。对于上述建模误差(即非线性残差),第三步采用三个不同作用机制的非线性模型(即最小二乘支持向量机、遗传规划算法和时空径向基函数神经网络)系统地捕捉它们的复杂的非线性特征。最后,利用数据处理神经网络的分组方法将这些非线性模型组合起来进行选择性预测,其中涉及的模型及其权重可以自动确定。使用具有两个不同时间间隔的四组测量风速数据集来评估所提出方法的性能。
更新日期:2020-09-01
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