Design and simulation of intelligent control systems for grid-side and user-side in power IoT based on bionic algorithms and neural networks

This paper presents an intelligent control system for optimizing grid-side and user-side operations in Power IoT environments by integrating a bionic algorithm and neural networks. The bionic algorithm, inspired by swarm intelligence, enables decentralized and adaptive real-time optimization of grid parameters such as voltage control and load balancing. Neural networks, particularly LSTMs, are employed to predict energy demand and optimize resource usage. The proposed system was evaluated using three datasets, and the results show that it outperforms traditional methods like Genetic Algorithms, Random Forests, and Support Vector Machines in terms of predictive accuracy, energy efficiency, and convergence time. This hybrid approach allows for real-time adaptability in dynamic grid conditions and provides a scalable solution for modern smart grids. Future work will explore integrating more distributed energy resources and advanced reinforcement learning to further enhance the system’s performance.