基于不同核支持向量机变权综合的短期负荷预测  

Short-Term Load Forecasting Based on Variable Weighted Synthesis of Different Kernel SVM

作　　者：马冬芬 

机构地区：[1]新疆财经大学统计与数据科学学院,中国 乌鲁木齐

出　　处：《统计学与应用》2020年第1期73-80,共8页Statistical and Application

基　　金：新疆财经大学研究生科研创新项目(XJUFE2018K043)。

摘　　要：针对短期负荷预测准确度提升及预测稳定性改进问题,给出了基于不同核支持向量机变权综合的短期负荷预测方法。该方法首先将负荷历史数据进行特征展开,利用相关分析进行特征选择,映射历史数据为输入输出关系视图,构建预测空间。随后,分别采用高斯径向基核函数、拉普拉斯核函数以及多项式核函数的支持向量机在预测空间进行训练学习,使用十折交叉验证进行模型性能测试。最后,利用性能测试的准确率及其标准差构造变权,借助多模型变权综合实现电力负荷预测。实例分析表明,与高斯核支持向量机、偏最小二乘、决策树及Bagging等常用方法相比,新方法将准确度分别提升了0.382%、3.079%、3.188%以及2.6%,将稳定性分别改进了0.383%、2.452%、1.781%以及1.43%。To improve the accuracy and stability of short-term load forecasting, a method of short-term load forecasting based on different kernel support vector machine (SVM) variable weight synthesis is proposed. In this method, firstly, the load history data is expanded, the feature is selected by correlation analysis, and the historical data is mapped to the input-output relationship view to build the forecasting space. Then, support vector machines of Gaussian kernel, Laplace kernel and Polynomial kernel function are used to study in the forecasting space respectively, and the performance of the model is tested by the 10 fold cross validation. Finally, the variable weight is constructed by using the accuracy and standard deviation of performance test, and the power load forecasting is realized by variable weight synthesis of multiple model. Example analysis shows that compared with methods such as Gaussian kernel support vector machine, partial least squares, decision tree and Bagging, the new method improves accuracy by 0.382%, 3.079%, 3.188% and 2.6%, and stability by 0.383%, 2.452%, 1.781% and 1.43%, respectively.

关 键 词：短期负荷预测 支持向量 核函数 多模型变权综合 

分 类 号：G63[文化科学—教育学]