摘要
为提高风电预测的精度,提出一种鲸鱼优化支持向量机SVM(support vector machine)的组合预测模型。该模型针对风电序列的非平稳波动特性,首先应用集合经验模态分解技术EEMD(ensemble empirical mode de?composition)将原始风电序列分解为一系列不同特征尺度的子序列;并引入鲸鱼优化算法WOA(whales optimiza?tion algorithm)解决SVM中学习参数选择难的问题,进而对各子序列建立WOA_SVM预测模型;最后,叠加各子序列的预测值以得到最终预测值。仿真表明,所提EEMD_WOA_SVM模型具有较高的风电预测精度,显著优于其他基本模型。
To improve the accuracy of wind power forecasting,a combined prediction model based on whales optimiza?tion algorithm(WOA)and support vector machine(SVM)is proposed in this paper.First,according to the non-station?ary fluctuation characteristics of wind power sequences,the ensemble empirical mode decomposition(EEMD)tech?nique is used to decompose the original wind power sequences into a series of sub-sequence with different time charac?teristic scales.Then,WOA is introduced to solve the problem of difficulty in selecting the learning parameters in SVM,so that the WOA_SVM prediction model can be established for each sub-sequence.At last,the predicted values of each sub-sequence are superimposed to obtain the final predicted value.Simulation results show that the proposed EEMD_WOA_SVM model has higher prediction accuracy,which is significantly better than other basic models.
作者
岳晓宇
彭显刚
林俐
YUE Xiaoyu;PENG Xiangang;LIN Li(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China;School of Automation,Guangdong University of Technology,Guangzhou 510006,China)
出处
《电力系统及其自动化学报》
CSCD
北大核心
2020年第2期146-150,共5页
Proceedings of the CSU-EPSA
关键词
风电预测
集合经验模态分解技术
支持向量机
鲸鱼优化算法
组合模型
wind power forecasting
ensemble empirical mode decomposition (EEMD)
support vector machine (SVM)
whales optimization algorithm( WOA)
combination model
