A Model and Simulation of Energy Net Information Risk Transmission Based on Deep Learning

The complexity and correlation of the EnergyNet greatly increase the speed and scope of information risk transmission, which may not only affect the stable operation of the energy system, but also pose a significant threat to social and economic security. This article aims to conduct in-depth research and exploration on the risk transmission mechanism in energy information fusion systems, and propose a deep learning based prediction model to simulate and predict the dynamic transmission process of risks in energy systems. To achieve this goal, deep learning algorithms were used in the study to construct a model that can learn and identify risk transmission patterns in energy information fusion systems, and real energy monitoring data was used for verification and comparison. The proposed model not only converges but also approximates real energy data well, demonstrating strong predictive ability. This result validates the effectiveness of deep learning in the field of complex system prediction, and also demonstrates the superiority of our model in predicting energy system risks.