Ding Min, Zhou Hao, Xie Hua, Wu Min, Liu Kang-Zhi, Nakanishi Yosuke, Yokoyama Ryuichi
School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
ISA Trans. 2021 Feb;108:58-68. doi: 10.1016/j.isatra.2020.09.002. Epub 2020 Sep 16.
In this paper, a time series model based on hybrid-kernel least-squares support vector machine (HKLSSVM) with three processes of decomposition, classification, and reconstruction is proposed to predict short-term wind power. Firstly, on the basis of the maximal wavelet decomposition (MWD) and fuzzy C-means algorithm, a decomposition method decomposes wind power time series and classifies the decomposition time series components into three classes according to amplitude-frequency characteristics. Then, time series models on the basis of least-squares support vector machine (LSSVM) with three different kernels are established for these three classes. Non-dominated sorting genetic algorithm II optimizes the parameters of each forecasting model. Finally, outputs of forecasting models are reconstructed to obtain the forecasting power. The proposed model is compared with the empirical-mode-decomposition least-squares support vector machine (EMD-LSSVM) model and wavelet-decomposition least-squares support vector machine (WDLSSVM) model. The results of the comparison show that proposed model performs better than these benchmark models.
本文提出了一种基于混合核最小二乘支持向量机（HKLSSVM）的时间序列模型，该模型具有分解、分类和重构三个过程，用于预测短期风电功率。首先，在最大小波分解（MWD）和模糊C均值算法的基础上，提出一种分解方法对风电功率时间序列进行分解，并根据幅频特性将分解后的时间序列分量分为三类。然后，针对这三类分别建立基于三种不同核的最小二乘支持向量机（LSSVM）时间序列模型。非支配排序遗传算法II对各预测模型的参数进行优化。最后，对预测模型的输出进行重构以得到预测功率。将所提模型与经验模态分解最小二乘支持向量机（EMD-LSSVM）模型和小波分解最小二乘支持向量机（WDLSSVM）模型进行比较。比较结果表明，所提模型的性能优于这些基准模型。
