基于VMD-SSA-LSSVM的短期风电预测  

SHORT-TERM WIND POWER FORECASTING BASED ONVMD-SSA-LSSVM

作　　者：王维高 魏云冰 滕旭东[1] Wang Weigao;Wei Yunbing;Teng Xudong(School of Electrical and Electronic Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气工程学院,上海201620

出　　处：《太阳能学报》2023年第3期204-211,共8页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(51507157)。

摘　　要：为解决由于风电预测中出现的波动性和随机性造成风电功率预测精确度不高的问题,提出一种基于变分模态分解(variational mode decomposition,VMD)、Tent混沌映射、随机游走的麻雀搜索优化算法(sparrow search algorithm,SSA)和最小二乘支持向量机(least squares support vector machines,LSSVM)的组合模型。首先应用鲸鱼优化算法(whales optimization algorithm,WOA)对VMD的核心参数(K值和惩罚系数α)进行自动寻优。经过WOA-VMD对原始风电功率时间序列分解过后,引入改进的麻雀搜索算法SSA优化最小二乘支持向量机LSSVM中的学习参数,然后对分解得到的各个子序列建立SSA-LSSVM预测模型;最后叠加各个子序列的预测值并得到最终预测值。经实验仿真对比,该文组合模型较现有单一预测模型和普通组合模型在预测精度上有较大提高。In order to solve the problem of low accuracy of wind power prediction caused by the volatility and randomness in wind power prediction,an integrated model combined with the least squares support vector machine(LSSVM)and an sparrow search algorithm based on variational mode decomposition(VMD).Tent chaotic mapping and random walkis proposed.First,the whale optimization algorithm(WOA)is used to automatically optimize the core parameters(K value and penalty coefficientα)of VMD.After original wind power time series is decomposed by WOA-VMD,the improved sparrow search algorithm SSA is introduced to optimize the learning parameters of LSSVM,and then the SSA-LSSVM prediction model is established for each subsequence obtained by the decomposition.Finally,the prediction value of each subsequence is superimposed to get the final predicted value.Compared with the existing single prediction models and the general integrated models in simulation experiment,the proposed integrated model has a great improvement in the prediction accuracy.

关 键 词：自适应算法 风电功率 预测模型分析 最小二乘支持向量机 变分模态分解 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]