Research on the Application of Artificial Intelligence Models in Smart Energy and Carbon Emission Systems

Abstract

At this critical juncture of advancing the "dual carbon" goals and transforming energy structures, the efficient coordination between smart energy systems and carbon emission monitoring systems serves as the cornerstone for implementing green development concepts and achieving sustainable growth. Artificial intelligence technologies, with their robust data processing capabilities, deep feature extraction, and intelligent decision-making prowess, offer innovative solutions to address persistent challenges in traditional energy systems—including low energy efficiency, insufficient carbon emission monitoring accuracy, and supply-demand imbalance. This study systematically examines the application status of mainstream AI models (including machine learning, deep learning, and reinforcement learning) across the entire smart energy production, transmission, and consumption chain, as well as carbon emission accounting, monitoring, and optimization processes. It thoroughly analyzes core issues such as data heterogeneity, model generalization limitations, and inadequate technological integration, while exploring practical scenarios through case studies to demonstrate the effectiveness of different AI models. Finally, the research outlines future trends in deep integration between AI and smart energy systems/carbon monitoring systems, proposing targeted optimization pathways and implementation strategies. These insights provide theoretical foundations and practical references for accelerating digital transformation in energy systems, enhancing precision carbon management, and facilitating the realization of "dual carbon" objectives