Study on Energy Meter Status Assessment Using Support Vector Regression Algorithm

The improved accuracy of energy meter status assessment helps the grid to set accurate electricity prices. A method of clustering and then evaluating the running state of electric meters is proposed. First, the indicators that affect the running state of the meter are extracted. Then, k-means algorithm is used to cluster electricity meters. Then, particle swarm optimization support vector regression algorithm is used to build the prediction model of electric meter. Simulation and comparative analysis show that the proposed method has higher prediction accuracy than the traditional BP algorithm.