文献类型：期刊文献

中文题名：组合聚类与最优核v-SVR融合方法在能源系统应用

英文题名：Application of Fusion Strategy with Optimal Kernel v-SVR and Assembled Clustering Algorithm in Energy System

作者：彭亦功[1];陆维朋[1];俞金寿[1]

机构：[1]华东理工大学信息学院,上海200237

年份：2010

卷号：22

期号：12

起止页码：3020

中文期刊名：系统仿真学报

外文期刊名：Journal of System Simulation

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：上海市重点学科建设项目(B504)

语种：中文

中文关键词：能源供需;组合聚类;最优核;v-SVR

外文关键词：energy supply/demand; assembled clustering algorithm; optimal kernel; v-SVR

摘要：针对能源复杂系统样本数量有限、变量维数高、偶合关系复杂等问题,提出了一种组合聚类算法和最优核v-支持向量回归机SVR融合的方法。该方法采用SOM自组织映射神经网络和K-means组合的聚类算法对初始样本集合进行聚类,构成不同核函数的子支持向量回归机SVR模型,再用均方误差标准(MSE)和小误差概率对其各核函数进行优选,得到最优核函数的v-支持向量回归机SVR模型。仿真结果表明,采用这种方法进行能源供需预测是有效的,其精度优于常规的支持向量回归建模方法。
A method of fusion strategy with Optimal Kernel v-SVR （Support Vector Regression） and assembled clustering algorithm was proposed,facing the issues of complex energy system like limited samples,high dimension,complex coupling.The assembled clustering algorithm was used to cluster the initial samples related to energy data to form sub-SVR models based on different Kernel functions,combining Self-Organizing Map （SOM） neural network with K-means algorithm.The Mean Square Error criterion （MSE） and small error probability were used to evaluate the Kernel functions to obtain the optimal Kernel v-SVR model.The simulation results demonstrate that the proposed method is valid for predicting energy supply/demand and its accuracy is superior to the conventional SVR method.