A Cross Spatial Risk Model for Energy Internet Based on Machine Learning Algorithms

This article aims to study the problem of risk identification and assessment in the energy internet, using Machine learning (ML) based methods to construct a risk identification and assessment model, and verifying its accuracy and efficiency through experiments. The results show that the model has a high accuracy in the three risk categories of equipment failure, network security, and data security, reaching over 90%. The accuracy rates for natural disasters and technological risks are relatively low, at around 80% and 75%, respectively. And it has a shorter prediction time when predicting the risk labels of new data. Compared with traditional risk assessment methods, the risk identification and assessment model based on ML algorithms has higher accuracy and efficiency. Based on these experimental results, it can be concluded that the risk identification and assessment model constructed in this article can effectively identify various risk factors in the energy internet, and has the ability to quickly respond to risk events in practical applications. It provides useful reference and support for risk prevention and control of energy internet, and provides direction and improvement ideas for subsequent research.