Smart meter life prediction method based on health index and random forest

Smart electricity meter is the key instrument to realize the accurate measurement of electricity consumption. However, with the increase of the service life of electricity meter, its measurement accuracy will deteriorate. Aiming at the problems that a single parameter cannot accurately characterize the performance degradation process of the electric meter, and the traditional intelligent learning model is difficult to accurately fit the degradation model of the smart electric meter, a new method is proposed to fuse the data to construct the electric meter health index, and combine the multi-model similarity matching and the integrated model to calculate the residual energy of the electric meter. Methods of life prediction. Hierarchical clustering and silhouette coefficients are used to filter parameters and fuse them into a meter health index. Multi-model similarity matching is used for regression model prediction to optimize the prediction results of the model. Select a meter measurement data set to verify the effectiveness of the proposed method. The results show that the method has the advantages of fast prediction speed, and the fusion of health index and multi-model similarity matching greatly improves the prediction accuracy of the remaining life of the electric meter.