Energy Consumption Management and Optimization of Artificial Intelligence Systems based on Computer Communication

Aiming at the problem of high energy consumption and difficult optimization of artificial intelligence systems in computer communications, accurate energy efficiency prediction and adaptive adjustment strategies are used to reduce system operation energy consumption and improve overall performance and efficiency. First, the deep neural network (DNN) is used to model the system energy consumption and predict the energy consumption trend under different working conditions. Then, the deep Q network (DQN) algorithm is combined to design an adaptive energy consumption scheduling strategy. Finally, the PSO (particle swarm optimization) algorithm is used to tune the key parameters in the system and optimize resource allocation and task scheduling strategies. Under light load conditions, the response time of the PSO optimized system is 0.43 seconds, while the response time of the non-optimized system is 0.48 seconds. The proposed method effectively reduces energy consumption while maintaining high performance.