Local Anomaly Data Detection Method of Smart Electricity Meters Based on Data Mining
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.
data miningsmart electricity meterlocalabnormaldatadetection
NETL
NSTL
万方数据
