Optimization Model of Portable Storage of Power Metering Assets Based on Fuzzy Support Vector Machine

To enhance the prediction accuracy and evaluation reliability of the power metering asset management system, this paper introduces a system based on an improved fuzzy support vector machine algorithm. The system achieves precise prediction of electric power metering asset demand and comprehensive evaluation of operational status through two modules: the material prediction module and the automatic detection module. The material prediction module employs the improved fuzzy support vector machine algorithm, effectively addressing the fuzziness and uncertainty of input variables. By utilizing historical data and influencing factors, it improves the accuracy of material demand forecasting. The automatic detection module adopts the analytic hierarchy process, enabling comprehensive evaluation of the metering terminal’s running state and ensuring efficient utilization of power metering assets. Additionally, the system features intelligence and visualization, boosting management efficiency.After testing, the system achieved a prediction accuracy of 98.61% for power metering materials and a fault identification rate of 98.32 % for power metering terminals, meeting the requirements of power metering asset management.