Abstract: The fault risk prediction of power metering equipment can reduce the loss caused by the fault risk of the national grid.Firstly,the data preprocessing and feature selection are carried out.Secondly,the GBDT-based fault categories,fault subclasses and equipment life cycle prediction are designed.Finally,the validity and advancement of the designed model are verified.Data used in the experiment are provided by China Electric Power Research Institute.The experimental results show that the prediction accuracy of the six fault types by using the proposed algorithm is 90.56%,the recall rate is 92.95%,and the F1 value is 91.71%.Compared with regression,BP neural network,Adaboost and decision tree algorithm,the gradient lifting decision tree algorithm has the best performance under parameter tuning conditions.

Key words: Data cleaning, GBDT, Measurement risk prediction