文献类型：期刊文献

中文题名：相关向量机和特征选取技术在短期负荷预测中的应用

英文题名：The Application of Relevance Vector Machine and Feature Selection Technology in Short-Term Electric Load Forecasting

作者：刘刚

第一作者：刘刚

机构：[1]贵州理工学院电气工程学院;

第一机构：贵州理工学院电气与信息工程学院

年份：2016

期号：16

起止页码：56-58

中文期刊名：电子世界

外文期刊名：Electronics World

语种：中文

中文关键词：短期负荷预测;Relief算法;相关性分析;特征选取;相关向量机

外文关键词：Short-term load forecasting;Relief Algorithm;Correlation Analysis;Feature Selection;Relevance Vector Machine

摘要：短期负荷预测对电力系统的经济运行意义重大,本文通将传统用于分类问题的Relief算法改进后,使其可用于短期负荷预测的特征输入量选取,同时结合相关性分析法来消除冗余特征。在所选特征和气温数据的基础上,应用相关向量机来建立预测模型。以美国德州电力市场某东部城市的真实负荷数据来进行仿真分析,结果表明本文的特征选取方法可以将负荷的周期性特征和短期趋势特征选取出来,且就预测效果来看,和SVM和BP网络相比较,相关相量机也获得了较好的预测效果。
ABSTRACT:Short-term load forecasting has momentous significance to the economic operation of power system,in this paper,an improved Relief Algorithm was proposed to extract the inputs feature of short-term load forecasting model,and the correlation analysis was used to eliminate the redundant features.Based on the selected features and temperature data,the forecasting model is established by using the Relevance Vector Machine. The real power load data and temperature data from Texas electricity market was used for simulation analysis.The results show that the proposed feature selection method can extract load short-term trend features and cycle features,and the Relevance Vector Machine also obtained better forecast results than support vector machine(SVM)and BP neural network did.