caoyao130 发表于 2010-12-1 20:55:24

优化组合核函数相关向量机电力负荷预测模型

优化组合核函数相关向量机电力负荷预测模型
段青 , 赵建国 , 马艳
(1.山东大学电气工程学院,山东济南250061;2.国家电网技术学院,山东济南250002;
3.国核电力规划设计研究院,北京100094)
摘要:在单一核函数相关向量机模型的基础上,构建高斯核函数分别与多项式核函数和张量积
线性样条核函数进行线性组合的多种组合核函数相关向量机中期电力负荷预测模型,并利用粒子
群优化算法对组合核函数的各参数进行优化选择。以2001年组织的国际电力负荷预测竞赛提供
的公开数据为训练和测试样本,分别对多种核函数相关向量机中期电力负荷预测模型进行仿真预
测计算。结果显示,虽然各模型都取得了较好的预测精确度,但是基于组合核函数的相关向量机在
各项评价指标上都优于基于单一核函数的相关向量机。还利用相关向量机的概率预测优势得到了
其他模式识别模型无法得到的预测误差范围。
关键词:负荷预测;稀疏贝叶斯学习;相关向量机;组合核函数;粒子群优化
中图分类号:TM 715 文献标志码:A 文章编号:1007—449X(2010)06—0033—06
Relevance vector machine basedof compounding kernels in on particle swarm optimization electricity load forecasting
DUAN Qing , ZHAO Jian.guo , MA Yan
(1.School of Electrical Engineering Shandong University,Jinan 250061,China;2.State Grid of China Technology College。Jinan 250002,China;3.State Nuclear Electric Power Planning Design&Research Institute,Beijing 100094,China)
Abstract:Based on the single kernel function relevance vector machine(RVM)models,it constructs multi—ple middle-time—load—forecasting models.The RVM’S kernel functions were linearly compounded by Gauss—ian kernel with polynomi al kernel and tensor product spline kernel,and the compounding kernels’parameters are optimized by algorithm of particle swarm optimization(PSO).With the training and testing sample data of 2001 world—wide competition of electricity load forecasting,all the models’forecasting value were given.The results show,although the ever),model has a good accuracy,all the multi-linearity—compoun—ding kernels RVM models give the better accuracy than the single kernel ones.Besides the forecasting error bar also be given based on the exclusive probability character of relevance vector machine.
Key words:load forecasting;sparse Ba) esian learning;relevance vector machine;compounding kernel;particle swarm optimization

lyd200311 发表于 2019-5-22 14:46:30

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