电力系统自动化2017，Vol.41Issue(18)：53-59,7.DOI:10.7500/AEPS20170118002
基于非参数回归分析的工业负荷异常值识别与修正方法
Outlier Detection and Correction Method for Industrial Loads Based on Nonparametric Regression Analysis
摘要
Abstract
In a power system,the information on power consumption patterns and electricity demand levels is recorded in industrial load curves,part of which,however,will be abnormal because of unexpected interference.Therefore,a method based on nonparametric regression theory is proposed to detect and correct the outliers in industrial load curves.First,for the lateral continuity of load data in time sequence,a fuzzy statistical method is employed for classifying the load curves by consumption patterns.The load data sets are classified into two data sets,one is of the basic consumption patterns and the other of special patterns.Then,considering the longitudinal continuity of load values in various time intervals,the nonparametric regression analysis method is used to estimate the center vector based on the data set of basic patterns.With the center vector,the outlier boundaries are achieved to detect all the outliers.Finally,the mapping of load levels is modeled to carry out the outlier correction in accordance with the weighted average method.The actual industrial load data are adopted to test the proposed method.The result shows the effectiveness of the proposed method.关键词
负荷管理/模式分类/异常数据识别/非参数回归分析Key words
load management/pattern classification/outlier detection/nonparametric regression analysis引用本文复制引用
赵天辉,王建学,马龙涛,朱宇超..基于非参数回归分析的工业负荷异常值识别与修正方法[J].电力系统自动化,2017,41(18):53-59,7.基金项目
This work is supported by Key Industry Innovation Chain Project of Key Research and Development Program in Shaanxi Province (No.2017ZDCXL-GY-02-03).陕西省重点研发计划重点产业创新链资助项目(2017ZDCXL-GY-02-03). （No.2017ZDCXL-GY-02-03）
