Simulation of Reliability Assessment Model of EnergyNet Based on Convolutional Neural Network

This article focuses on exploring the reliability assessment index system and method for EnergyNet. It introduces a CNN (Convolutional Neural Network)-based reliability assessment model for EnergyNet and delves into its detailed design and parameter configuration. To validate the model's efficacy, simulation experiments are conducted, encompassing data preprocessing, model training, and comprehensive result analysis. The evaluation process involves assessing model performance, visualizing outcomes, and more. The experimental results reveal that the proposed model exhibits high accuracy, recall, and F1 score, thereby proving its effectiveness in identifying potential risks and failures within EnergyNet. The reliability assessment model based on CNN has higher efficiency and accuracy in processing EnergyNet data. This is mainly due to the advantages of CNN model in processing two-dimensional data such as images and videos, as well as the optimization and innovation of model design and parameter setting in this article. In this study, deep learning technology is applied to the reliability assessment of EnergyNet, which can promote the application and growth of deep learning in the energy field.