Improved Genetic Algorithm-Based Whole-Life Simulation Prediction of Metering Assets in Power Supply Enterprises

Effective management of metering assets is crucial for power supply enterprises, ensuring reliability and efficiency. This study presents an improved genetic algorithm designed to enhance the accuracy of whole-life cycle simulation predictions for metering assets. The methodology involves optimizing the genetic algorithm’s calculation methods, improving the initial population selection, and incorporating expert judgment for comprehensive evaluation. Key findings reveal that the enhanced algorithm significantly improves prediction accuracy, showcasing superior performance in lifecycle assessment. This novel approach addresses existing challenges in asset management by providing a robust solution for balanced scheduling and efficient lifecycle management of metering assets. The practical implications of this research are profound, offering a reliable tool for power supply enterprises to optimize their supply chain operations and asset management strategies. The improved genetic algorithm not only ensures precise predictions but also contributes to the overall stability and efficiency of metering asset management, laying a solid foundation for future advancements in this field.