Optimization of Wireless Communication Networks for Smart Grids by Incorporating Artificial Intelligence

In order to improve the reliability, low-latency characteristics and resource scheduling efficiency of the smart grid wireless communication network, a network optimization method integrating multiple intelligences reinforcement learning, graph neural network and adaptive optimization strategy is constructed. Key aspects such as network architecture optimization, resource allocation, routing, load balancing and energy consumption control are studied, and the effectiveness of the optimization scheme is verified through simulation. The results show that the proposed method can reduce end-to-end delay, improve spectrum utilization, optimize computational resource allocation, and effectively reduce energy consumption in complex electromagnetic environments. The study provides support for the adaptive scheduling and efficient operation of wireless communication networks in smart grids, and promotes the informationization and intelligent development of power systems.