Research on IoT network performance prediction model of power grid warehouse based on nonlinear GA-BP neural network - Pang, Lele; Xia, Bo; Cheng, Zhanfeng; Ren, Zhiqiang; Shen, Hao; Li, Pengfei (Nonlinear engineering, 2025)
This study introduces a novel approach for forecasting network performance prediction in power grid warehouses, employing a nonlinear Genetic Algorithm (GA)-optimized backpropagation (BP) neural network model. The proposed model integrates the global search capability of GA with the learning efficiency of BP neural networks, thereby addressing the common issue of local optima encountered in traditional BP-based prediction models. The model is designed to capture the intricate dynamics of IoT networks in power grid environments by incorporating multiple critical factors that influence network performance, including signal strength, transmission delay, packet loss rate, and other vital indicators. This comprehensive approach enables the simulation of complex network behaviors, enhancing the model’s predictive accuracy and adaptability. The GA-BP model demonstrates superior performance over conventional BP and other methods, showcasing higher prediction accuracy and faster convergence. In the context of power grid warehouses, the model achieves a remarkable accuracy of 96.7%, an 8% improvement over standard BP. Notably, during peak evening hours from 18:00 to 22:00, when network loads are at their highest, the model maintains a stable prediction accuracy of over 98%, effectively supporting the intelligent management and optimal scheduling of power grid warehouses. To ensure the model’s applicability across various scenarios, the study verifies its scalability and robustness, proving its suitability for IoT systems in power grid warehouses of differing scales and complexities. This comprehensive verification, establish a grid warehouse model that accurately reflects the actual operation logic, and provide decision support for material management and allocation content.