Abstract:

In view of the characteristics of large scale,high dimension,error and abnormal data of smart electricity meter fault data,a fault prediction method of smart electricity meter based on multi-classification machine learning model is proposed.The missing values and outliers are replaced in the original data set by using normal distribution completion and box diagram method.By calculating the correlation coefficient between feature attributes and fault types,the uncorrelated features are eliminated and the feature subset is formed.To solve the problem of unbalanced fault data,a mixed sampling strategy is built.The prediction accuracy of three typical machine learning algorithms to process the fault data of smart electricity meters is calculated,and the confusion matrix is constructed.Considering the prediction ability of each classifier,the multi-classifier fusion decision function is constructed.Finally,the effectiveness of the proposed method is verified by using public data sets and actual electricity consumption data as samples.

Key words: smart electricity meter, machine learning, data preprocessing, fusion algorithm, failure prediction