基于数据挖掘的智能电表局部异常数据检测方法
Local Anomaly Data Detection Method of Smart Electricity Meters Based on Data Mining
沈王平1
作者信息
- 1. 国网浙江省电力有限公司营销服务中心,杭州 311100
- 折叠
摘要
智能电表通常会存储用户的用电数据,如果电表存在异常,导致异常数据检测精度较低,因此设计一种基于数据挖掘的智能电表局部异常数据检测方法.收集智能电表的局部数据,然后进行数据预处理.基于数据挖掘技术,建立异常数据检测模型.通过模型训练和测试,检测异常数据输出特征值,这些特征值能够反映智能电表运行的状态.实验结果表明,设计的基于数据挖掘的智能电表局部异常数据检测方法,异常数据检测精度最高达到98.6％,说明该方法进行智能电表局部异常数据检测的精度高.
Abstract
Smart meters usually store the user's electricity consumption data.If the meter is ab-normal,the abnormal data detection accuracy is low.Therefore,a local abnormal data detection method of smart meters based on data mining is designed.Collect local data of smart meters,and then perform data preprocessing.Based on data mining technology,the abnormal data detection model is established.Through model training and testing,abnormal data can be detected and out-put characteristic values,which can reflect the running state of smart meters.The experimental results show that the designed method based on data mining for detecting local abnormal data of smart meters has the highest detection accuracy of 98.6％,which indicates that the method has high accuracy for detecting local abnormal data of smart meters.
关键词
数据挖掘/智能电表/局部/异常/数据/检测Key words
data mining/smart electricity meter/local/abnormal/data/detection引用本文复制引用
出版年
2024
NETL
NSTL
万方数据