Evaluation on the impact of digital transformation on the economic resilience of the energy industry in the context of artificial intelligence

Abstract

In recent years, the energy industry based on energy resources has achieved rapid development, but under the background of green development strategy, traditional energy companies are also in trouble during the development process. However, the accompanying development of technologies such as the Internet and artificial intelligence has opened the door to a new world of digital transformation of the energy industry. The focus of whether the energy industry can truly achieve digital transformation lies in how to improve its own level of economic resilience. In order to explore how digital transformation affects the economic resilience of the energy industry in the context of artificial intelligence, after discussing the development dilemma of the energy industry and the integration of artificial intelligence and digital transformation, this paper established a Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) algorithm. Experiments have shown that the model built in this paper has a good prediction effect on the economic resilience index, and the average prediction error after training is 0.0028. Compared with the standard least squares support vector machine and back-propagation neural network methods, this method not only has more stable prediction results, but also greatly improves the prediction accuracy.

Keywords

• Economic management
• Economic resilience
• Digital change
• Artificial intelligence
• Energy industry
• Support Vector Machines