基于决策树分析的卡尔曼滤波预测方法及应用

龙 羿; 侯兴哲; 肖剑锋; 孙洪亮; 刘永相; 朱 彬

龙羿,侯兴哲,肖剑锋,孙洪亮,刘永相,朱彬.基于决策树分析的卡尔曼滤波预测方法及应用[J].电测与仪表,2017,54(21):.
LONG Yi,HOU Xing-ze,XIAO Jian-feng,SUN Hong-liang,LIU Yong-xiang,ZHU Bin.A Prediction Method based on a Modified Kalman Filter Algorithm with Decision Tree Analysis and Its Application[J].Electrical Measurement & Instrumentation,2017,54(21):.

基于决策树分析的卡尔曼滤波预测方法及应用

A Prediction Method based on a Modified Kalman Filter Algorithm with Decision Tree Analysis and Its Application

DOI：

中文关键词: 电动汽车分时租赁车速预测卡尔曼滤波算法人为干预

英文关键词:Electric vehicle Time-sharing leasing Vehicle speed Kalman filter algorithm Human intervention

基金项目:国家科技支撑计划项目(2015BAG10B00)

作者中文名	作者英文名	单位
龙羿	LONG Yi	国网重庆市电力公司电力科学研究院
侯兴哲	HOU Xing-ze	国网重庆市电力公司电力科学研究院
肖剑锋	XIAO Jian-feng	国网重庆市电力公司电力科学研究院
孙洪亮	SUN Hong-liang	国网重庆市电力公司电力科学研究院
刘永相	LIU Yong-xiang	国网重庆市电力公司电力科学研究院
朱彬	ZHU Bin	国网重庆市电力公司电力科学研究院

摘要点击次数: 1612

中文摘要:

随着社会环保意识的增强,大力推广采用清洁能源的电动汽车,电动汽车分时租赁业务随之不断拓展,多级管理平台纷纷建立。总管理平台所采集的电动汽车参数日益积累,为大数据分析研究奠定了坚实的基础。预处理技术在数据采集过程中起着至关重要的作用。特别地,从各运营商子平台所采集的监测数据存在人为干预风险。为此,本文提出采用人为干预概率曲线量化监测数据与协方差比间关系,将人为干预概率曲线区间和观测量关系作为输入,建立了回归对象决策树并引入传统卡尔曼滤波算法,从而提出基于决策树分析的卡尔曼滤波预测方法,从而减少人为篡改的影响,以达到对运营总平台所采集的数据预处理的目的。本文将该算法应用于车速预测领域,得到可信度更高的车速预测数据,为有效地实现大数据分析奠定坚实的技术支撑。

英文摘要:

With the enhancement of social environmental protection consciousness, electric vehicles using clean energy are promoted widely. Scope of time-sharing leasing business for the electric vehicle keeps expanding, and multi-level management platforms are set up one after another. Electric vehicle parameters collected by main management platform are accumulated continually, which lays a firm foundation for big data analysis research. Preprocessing technology plays an important role in the process of data acquisition. Specially, monitoring data collected from the each sub-platform of operators may have the risk of being tampered manually. Therefore, in this paper, human intervention probability curve is used to quantify the relationship between monitoring data and covariance ratio, and both of human intervention probability curve interval and observation data relationship are treated as input, so that the regression object decision tree is established and introduced to the traditional Kalman filter algorithm. Therefore, a modified Kalman filter prediction method based on decision tree analysis is presented which can be used to reduce the influence of human tampering, and achieve preprocessing function of main operating platform for collecting data. In this paper, this modified method is successfully used in the field of vehicle speed prediction, and used to get speed data with higher credibility, which provides a strong technical support for large data analysis.

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