改进的支持向量回归机在电力负荷预测中的应用  

Application of Power Load Prediction Based on Improved Support Vector Regression Machine

作　　者：唐承娥[1] 韦军 and WEI Jun;TANG Cheng-e;WEI Jun(College of Electromechanical and Information Engineering,Guangxi Vocational and Technical College,Nanning 530226,China;Guangxi Zhuang Autonomous Region Admission Examination Institute,Nanning 530021,China)

机构地区：[1]广西职业技术学院机电与信息工程学院,南宁530226 [2]广西壮族自治区招生考试院,南宁530021

出　　处：《计算机科学》2020年第S01期58-65,共8页Computer Science

基　　金：2018年广西高校中青年教师基础能力提升项目(2018KY0956)。

摘　　要：电力预测是一项重要的工程应用。为了解决多层次粒度支持向量回归机(Dynamical Granular Support Vector Regression Machine,DGSVRM)预测电力负低荷精度的问题,提出一种基于萤火虫群优化(Glowworm Swarm Optimization,GSO)算法与模式搜索算法(Pattern Search,PS)的混合算法来优化DGSVRM预测模型的关键参数。仿真实验表明,通过优化参数之后,预测模型的预测精度得到很大提高。Electricity forecasting is an important engineering application.In order to solve the accuracy problem of dynamical granular support vector regression machine for power load forecasting(DGSVRM),this paper proposes a hybrid algorithm of glowworm swarm optimization(GSO)and pattern search(PS)to optimize the key parameters of DGSVRM forecasting model.Simulation results show that the prediction accuracy is greatly improved by optimizing the parameters of the prediction model.

关 键 词：多层次粒度支持向量回归机 萤火虫群优化 模式搜索算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]