Quality Analysis and Prediction Method of Smart Energy Meter based on Data Fusion

Abstract

In order to study the quality analysis method of key links in smart energy meters, the author proposes a data fusion based quality analysis and prediction method for smart energy meters. This method is based on the relevant data of key links in the electric energy meter, and selects the data of the electric energy meter in research and development design, material procurement, production and manufacturing, acceptance testing, installation and operation, dismantling and scrapping as the sample data for model construction. The XGBoost algorithm classification method is used to establish an intelligent electric energy meter quality analysis model. Taking the dismantled electricity meter data of a certain power company as an example, this paper conducts modeling analysis and prediction of various quality issues of smart electricity meters, and conducts on-site verification. Based on the verification results, the model is continuously optimized. The results indicate that: The model was optimized using cross validation and grid search methods, and the final model achieved an accuracy rate of 0.74 and a recall rate of 0.82 on the validation set. This method can meet the actual needs of power grid business and objectively reflect the quality situation of key links in smart energy meters.