信息通信技术与政策

信息通信技术与政策

信息通信技术与政策 ›› 2025, Vol. 51 ›› Issue (6): 52-59.doi: 10.12267/j.issn.2096-5931.2025.06.009

专题:AI 驱动能源变革 上一篇    下一篇

能源行业AI大模型工程化实践研究

Research on engineering practices of large-scale AI models in energy sector

王宇航, 史翔, 申一彤   

  1. 中电信数智科技有限公司,北京 100035
  • 收稿日期:2025-04-22 出版日期:2025-06-25 发布日期:2025-07-04
  • 作者简介:
    王宇航 中电信数智科技有限公司智慧城市研究院解决方案专家,工程师,长期致力于人工智能、大数据、数字孪生等新一代信息技术在数字政府、智慧城市的应用研究
    史翔 中电信数智科技有限公司智慧城市研究院解决方案部门负责人,主要从事数字政府、智慧城市及新技术、新业务的研究与咨询工作
    申一彤 中电信数智科技有限公司智慧城市研究院院长助理,长期负责城市及企业数字化转型研究及市场推广工作

WANG Yuhang, SHI Xiang, SHEN Yitong   

  1. China Telecom Digital Intelligence Co., Ltd., Beijing 100035, China
  • Received:2025-04-22 Online:2025-06-25 Published:2025-07-04

摘要:

随着全球能源转型加速,低碳化、智能化成为能源行业发展的核心方向。人工智能(Artificial Intelligence,AI)技术凭借强大的数据分析、预测优化和智能决策能力,正在深刻改变能源行业的生产、传输与消费模式。近年来,AI大模型(如GPT、BERT等)在自然语言处理、图像识别等领域取得突破性进展,在能源行业的应用潜力逐渐显现,然而其在工程化落地方面仍面临技术适配性、数据质量、算力成本等多重挑战。聚焦AI大模型在能源行业工程化落地关键环节,结合行业趋势、技术应用、典型案例等研判分析,提出系统化的实践路径解决方案。

关键词: 能源行业, 人工智能, 大模型, 工程化实践

Abstract:

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.

Key words: energy sector, artificial intelligence, large-scale models, engineering practices

中图分类号: