国家能源局科技司相关负责同志就《关于推进"人工智能+"能源高质量发展的实施意见》答记者问 问：《实施意见》出台的背景是什么？ 答： 党中央、国务院高度重视人工智能发展，近日，国务院印发《关于深入实施 "人工智能 + "行动的意见》，推动人工 智能与经济社会各行业各领域广泛深度融合。能源是创新创业高度活跃的领域，具有数字化基础好、数据质量高、应用场 景丰富等比较优势，应走在人工智能应用前列。特别是能源央企积极布局，围绕资源勘探、 生产运维、安全监测等环节 ， 已经成功研发应用了 电力、油气、煤炭等多个具有行业代表性的专业 大模型 。 总的看， 我国能源领域已形成了场景覆盖 广泛的人工智能发展格局。 与此同时，相比于 能源行业的高安全性与强专业性，以及对决策容错率和知识体系完备性的严苛要求， 人工智能技术在能 源领域应用仍然存在着技术可靠性不足、数据基础较为薄弱、电算供需逆向分布等不容忽视的问题与挑战。 大模型 "黑 箱"特性导致的可解释性缺陷和潜在幻觉风险，使得人工智能技术在涉及核电站安全决策、电网实时调度等核心领域尚无法 满足行业级可靠性要求 。随着越来越多场景融入人工智能应用， 亟需加强顶层设计和系统谋划， ...

Core Viewpoint - The implementation opinion aims to promote the integration of artificial intelligence (AI) and the energy sector, establishing a framework for high-quality development by 2027 and achieving world-leading levels by 2030 [1][10][12]. Group 1: Implementation Goals - By 2027, the initial framework for the integration of energy and AI will be established, focusing on the deep application of over five professional large models in various energy sectors such as power grids, generation, coal, and oil and gas [1][12]. - The plan includes identifying over ten replicable and competitive demonstration projects and exploring a hundred typical application scenarios [1][4][12]. - By 2030, the goal is to achieve systematic breakthroughs in AI-specific technologies and applications within the energy sector, enhancing safety, green transformation, and efficiency [5][13]. Group 2: Key Tasks - The implementation opinion outlines several key tasks, including empowering various energy scenarios with AI, focusing on coal, electricity, oil, and gas [6][7]. - It emphasizes the need for a comprehensive approach to AI applications across eight major scenarios, including smart grid, new energy, and nuclear power [7][8]. - A total of 37 key tasks have been identified, with specific applications in oil and gas, coal, electricity, and renewable energy [7][8]. Group 3: Technical Support - The opinion highlights the importance of strengthening the foundational technologies for AI applications in the energy sector, focusing on data, computing power, and algorithms [8][32]. - It calls for the establishment of high-quality data sets and a collaborative development mechanism for computing power and electricity [32][33]. - The need for enhancing model capabilities and addressing issues related to data security and algorithm transparency is also emphasized [32][33]. Group 4: Implementation Measures - The document stresses the importance of organizational implementation, encouraging local energy authorities and enterprises to establish mechanisms for promoting AI in the energy sector [34][35]. - It advocates for collaborative innovation among enterprises, research institutions, and universities to build a robust ecosystem for AI and energy integration [34][35]. - The need for pilot demonstrations and the selection of replicable scenarios for AI applications in the energy sector is also highlighted [35][36].