基于VMD-FE-SSA-SVR模型的超短期风速预测  

Prediction of Ultra-Short-Term Wind Speed Based onVMD-FE-SSA-SVR Model

作　　者：王胜研 王娟娟[1] Model WANG shengyan;WANG juanjuan(College of Automation and Electrical Engineering,Dalian Jiaotong University,Dalian 116028,China)

机构地区：[1]大连交通大学自动化与电气工程学院,辽宁大连116028

出　　处：《电器与能效管理技术》2024年第4期57-64,共8页Electrical & Energy Management Technology

摘　　要：为有效降低风速的非线性和无序性带来的风速预测难度,提高预测准确性,提出一种结合变分模态分解(VMD)、模糊熵(FE)、麻雀搜索算法(SSA)和支持向量回归(SVR)的组合预测模型来预测超短期风速。首先利用VMD技术将风速数据分解为若干模态分量,再通过FE方法对各分量进行筛选,将FE值相近的分量进行叠加,形成若干个新序列,然后采用经SSA优化过的SVR模型对新序列进行训练与预测,最后将各新序列的预测结果叠加,形成最终预测结果。通过不同模型验证对比,VMD-FE-SSA-SVR模型预测效果较好,表明所提模型显示出较好的预测精度与稳定性,可有效预测超短期风速。In order to effectively reduce the difficulty of wind speed prediction caused by nonlinear and disordered wind speed and improve the prediction accuracy,a combined forecasting model combining variational mode decomposition(VMD),fuzzy entropy(FE),sparrow search algorithm(SSA)and support vector regression(SVR)is proposed to predict ultra-short-term wind speed.Firstly,the wind speed data is decomposed into several modal components by VMD technology,and then each component is screened by FE,and the components with similar FE values are superimposed to form several new series.Then the new series are trained and predicted by the SVR model optimized by SSA.Finally,the prediction results of the new series are superimposed to form the final prediction results.Through the verification and comparison of different models,the prediction effect of the VMD-FE-SSA-SVR model is better,which shows that the proposed model has better prediction accuracy and stability,and can effectively predict ultra-short-term wind speed.

关 键 词：风速预测 变分模态分解 模糊熵 麻雀搜索算法 支持向量回归 

分 类 号：TM614[电气工程—电力系统及自动化]