Optimization model of electricity metering management based on MOPSO

In response to the difficulty of balancing economy and accuracy in traditional energy metering management methods, an improved particle swarm optimization model is designed to optimize energy metering management based on multi-objective particle swarm optimization, thereby achieving optimal resource allocation and maximizing management efficiency. The study uses Schaffer and Griewank functions to test its performance. The optimized algorithm performed well on both Schaffer and Griewank functions, indicating that the algorithm still had high computational efficiency while ensuring computational accuracy. In the case analysis, the MOPSO algorithm achieved mean absolute error, absolute percentage error, root mean square error, and mean absolute percentage error values of 8.61%, 3.08%, 0.2158, and 0.1295, respectively. Moreover, the line loss prediction curve closely matched the actual values. In addition, before and after the model was connected, the total active power loss decreased by 43.55 kW, and the operating costs decreased by a total of 2.2111 million RMB. The optimization model based on MOPSO algorithm can achieve both accuracy and economy in energy metering management.