With the transformation of the global energy structure and the rapid development of digital technologies, the application of large artificial intelligence models (large AI models) in the energy sector has gradually emerged as a significant force driving the sector transformation. As a foundational sector of the national economy, the energy sector faces multiple challenges including efficiency enhancement, cost optimization, and green transformation while adapting production and consumption patterns to complex and volatile energy supply-demand relationships. Through the analysis of multiple case studies and empirical data, this paper demonstrates that large AI models are revitalizing traditional energy industries. With robust data processing capabilities, deep learning algorithms, and efficient predictive analytics functionalities, these large AI models are empowering the energy sector by injecting new digital vitality.
Against the backdrop of global energy transition, the artificial intelligence (AI) technology provides a critical pathway for the mining industry to overcome efficiency bottlenecks and achieve safe, efficient, and green production. This paper systematically analyzes the current applications and challenges of AI in the mining industry, highlighting its current limitations to scenario-specific models and core problems such as poor adaptability to complex environments, high industry knowledge barriers, and data governance difficulties. By constructing a reference architecture of “foundation layer—technology layer—application layer”, this paper proposes AI-empowered mining scenarios, such as resource exploration, intelligent operations, safety warnings, and predictive equipment maintenance. These efforts aim to deepen the integration of AI with mining production, drive the intelligent development of the entire mining process, and facilitate the industry’s transition toward intelligent, green, and sustainable development.
The asymmetric development between Artificial Intelligence (AI)-driven computing power and energy technologies has led to a persistently widening energy-computing scissors gap. This study proposes a synergistic paradigm to reconcile this disparity by reconstructing AI infrastructure through energy revolution while leveraging novel energy technologies to drive algorithmic innovation, thereby achieving mutual reinforcement. The empirical analysis reveals that synergy intensity exhibits nonlinear growth characteristics, significantly influenced by regional heterogeneity and policy selection pressure. Sustainable pathways encompass integration of energy internet with federated learning architectures, establishment of global energy-computing trading markets, and breakthroughs in computing-electricity interoperability technologies. Building upon China’s industrial ecosystem, this paper advocates for coordinated technological-institutional innovation to establish a globally exemplary synergistic model by 2030.
Hydrogen energy, as a clean energy, has attracted much attention for its efficient storage and utilization technologies. In recent years, solid-state hydrogen storage materials have become a research hotspot due to their high safety and high hydrogen storage density. This paper systematically reviews the recent advances in hydrogen storage materials including various metal hydrides, focusing on the challenges of material design, performance optimization, and applications. In addition, it explores the potential role of machine learning in the industry chain of hydrogen storage materials. Finally, the future direction of low-cost and high-performance solid-state hydrogen storage materials is envisioned to facilitate the application of artificial intelligence (AI) technologies in the hydrogen energy industry and accelerate its commercialization.
The rapid development of Artificial Intelligence (AI) technology is reshaping the pattern of global energy industry. This paper analyzes the bidirectional influence relationship between AI technology and energy transformation from two aspects: the development of AI technology’s reliance on energy and the demand for AI technology in the green transformation of the entire energy industry chain. It systematically sorts out the domestic policies related to AI and energy development, and analyzes the policies at different stages in detail, providing a macro policy environment reference for the subsequent process of AI technology and energy transformation. Through the analysis of typical AI+energy cases, it summarizes the current achievements, and summarizes the existing problems and adjustments at the current stage, providing a certain reference for the subsequent formulation of scientific, effective and targeted integrated development policies.
Recently, new-generation artificial intelligence technologies represented by large models and intelligent agents have become key driving forces for the transformation and upgrading of traditional industries. As a pillar industry in the national economy, the petrochemical industry plays a critical role in ensuring security of national energy supply. This paper conducts an in-depth analysis of AI application cases in production and operation, engineering services, and technological innovation across the petrochemical industry. It explores potential challenges in AI-empowered transformation and proposes suggestions for facilitating deep integration between AI and petrochemical industry. The aim is to offer valuable insights for achieving safe, stable, efficient, and green sustainable development through AI technologies.
With the rapid development of Artificial Intelligence (AI), its applications in energy systems are expanding, from intelligent scheduling and fault diagnosis to energy management and optimization, driving the transition toward smarter, low-carbon, and more efficient energy infrastructures. However, this transformation faces multiple challenges, including data quality and security issues, lack of model interpretability, insufficient coordination between technology and regulation, and delays in ethical and policy responses. This paper systematically reviews key AI application scenarios in energy systems, analyzes core technological and governance constraints in the transformation process, and proposes countermeasures from the perspectives of technology, institutions, and policy. The goal is to provide theoretical support and practical strategies for building intelligent, efficient, and sustainable future energy systems.
In recent years, the scale of Artificial Intelligence (AI) models has been expanding continuously, leading to increasing energy consumption in training and inference processes. This has driven the rise of low-carbon AI research. This paper systematically reviews the key technologies of green computing for large models, with a focus on low-carbon training and inference optimization methods. In the training phase, existing studies aim to reduce energy consumption through model architecture optimization, computational precision adjustment, and resource scheduling strategies. These approaches include neural architecture search, mixed-precision computing, and distributed training. In the inference phase, optimization strategies primarily focus on techniques such as model pruning, quantization, edge computing, and cache reuse to reduce computational costs and carbon emissions. Additionally, this paper summarizes the challenges faced by low-carbon AI, including hardware energy efficiency bottlenecks, uncertainties in carbon footprint quantification methods, and limitations in the use of green energy in data centers. Furthermore, this paper explores the future development trends.
Amidst the accelerating global energy transition, decarbonization and digitalization have emerged as central priorities for the energy industry. Artificial Intelligence (AI) technologies, leveraging their robust capabilities in data analytics, predictive optimization, and intelligent decision-making, are profoundly transforming production, transmission, and consumption patterns in the energy sector. While recent breakthroughs in large-scale AI models (such as GPT, BERT, etc.) for natural language processing and computer vision demonstrate growing potential for energy applications, their industrial deployment faces multifaceted challenges including technical compatibility, data quality, and computational costs. This study investigates critical implementation aspects of large-scale AI models in energy systems, integrating analysis of industry trends, technological applications, and representative case studies to propose systematic solutions for practical adoption.
Batteries are one of the most important energy storage technologies. Traditional battery research and development methods suffer from long development cycles and high costs. Integrating artificial intelligence (AI) technology with the battery research and development field can not only improve research and development efficiency but also drive technological breakthroughs. This paper analyzes patent applications related to AI in battery research and development. The aim is to provide reference information for future research directions and technical insights for innovators in this field.
Integrating the latest generation of information technology deeply, processing all kinds of data uniformly, constructing mathematical models to conduct scientific analysis on the data, using the analysis results to support precise decision-making, linking and interacting the decision results with the control system, and ultimately achieving intelligent decision-making and control for subsystems and even the entire system, are the inevitable paths for technological innovation, production transformation, and management improvement in the coal industry. Utilizing the latest generation of information technologies such as artificial intelligence and digital twins, to centrally and uniformly manage the previously independently operating equipment and systems, creating a “perception-analysis-decision-control” closed loop, achieving an increase in production efficiency and management efficiency of “1+1>2”, can increase the coal output per unit working hour of coal mines, ensure the safety of production personnel, reduce the number of underground personnel per shift, and achieve the goals of transparent production and scientific decision-making.
At present, the deployment of industry private networks mainly consists of broadband trunking communication networks and narrowband trunking communication networks. However, with the continuously increasing demand for broadband and visual application scenarios in private networks, and considering multiple factors such as the autonomy of communication systems, existed network deployment, network construction costs, and network service application requirements, the broadband and narrowband converged technology based on trunking private network is proposed. This paper designs a network architecture for the converged technology of broadband trunking communication and narrowband trunking communication, and analyzes the future evolution path of broadband and narrowband converged technology in combination with the development of 5G technology in the industry.
Focusing on innovative practices in public data authorization operations, this paper conducts an in-depth exploration of two reference technical architectures for public data authorization operations based on data space. The comparative analysis reveals that data space constructed upon data element repositories demonstrates significant advantages in supporting public data authorization operations: it not only exhibits strong universality by providing technically simple and low-cost implementation solutions, but also fulfills data security and compliance requirements. Through establishing a trusted data space centered around data element repositories, effective management and trusted circulation throughout the entire lifecycle of public data authorization operations can be achieved.
With the wide application of automotive millimeter-wave radar systems in the field of vehicle active safety and autonomous driving, the problem of mutual interference between sensors has become more and more prominent. Interference cancellation and signal reconstruction methods utilize algorithms for interference cancellation and signal reconstruction respectively by analyzing the characteristics of the signal and the interference in order to improve the quality and reliability of the signal under the interference environment. This paper systematically summarizes and analyzes the mainstream technical methods from the perspectives of interference detection and signal reconstruction, respectively.