国家能源集团发布全球首个千亿级发电行业大模型“擎源” 驱动发电行业迈向智能化新阶段

Core Insights - The National Energy Group launched the world's first trillion-level power generation industry model, "Qingyuan," which aims to transform the energy sector through intelligent decision-making across the entire power generation chain [1][2] Group 1: Model Features and Innovations - "Qingyuan" achieves three major innovations: a multi-energy collaborative dynamic optimization engine, a vertical deep cultivation paradigm for the power generation industry, and a fully autonomous intelligent decision-making system [2][4] - The model integrates heterogeneous data sources, including operational monitoring, equipment status, and meteorological conditions, to create a comprehensive AI solution for the entire power generation process [2][3] Group 2: Application Domains - The model has been successfully applied in four core business areas: safety and environmental protection, power trading, production scheduling, and equipment maintenance, addressing long-standing industry pain points [3][4] - In the safety and environmental protection domain, "Qingyuan" enhances safety management and compliance, achieving over 100% efficiency improvement in technical supervision and reducing evaluation time from one week to one day [4][5] Group 3: Performance Metrics - In the power trading domain, "Qingyuan" improves weather prediction accuracy and enhances market analysis, leading to a 6.2% increase in price prediction accuracy compared to traditional methods [5][6] - The model optimizes production scheduling with a prediction accuracy of over 92% for key indicators, enabling proactive management of water resources and flood control [6][7] Group 4: Future Development Plans - The National Energy Group plans to focus on core scenario solutions, promote the first 13 published scenarios, and collaborate with universities and research institutions to advance key technologies [7] - The initiative aims to cultivate "AI + energy" interdisciplinary talents and accelerate low-carbon transformation while enhancing production efficiency and optimizing operational costs [7]