会议指出，2025年作为 "十四五" 收官之年，全省能源系统迎难而上、攻坚克难，推动能源发展改革取得突破性成 绩。"十四五" 规划 18 项目标任务全面完成，特高压交直流、百万千瓦煤电等重大项目建成投产，为后续发展奠定坚实 基础；能源保供能力显著提升，成功应对历史最高负荷考验，连续 3 年实现 "电力零停电、煤油气零短供、民生零断 供"；重大项目建设亮点纷呈，"宁电入湘" 工程等标志性项目投运，从根本上转变能源供需格局；绿色低碳转型稳步推 进，新能源装机规模持续扩大，储能设施建设有序开展，充电网络覆盖不断完善；科技创新动能加速转换，多项首台 （套）装备纳入国家名录，国家级研发平台在全国评估中名列前茅；体制机制改革持续深化，电力、油气体制改革取得 实效，用能成本稳步降低，能源产业竞争力逐步显现。 会议强调，党的二十届四中全会明确 "建设能源强国" 的宏伟目标，为能源工作提供了根本遵循。全省能源系统要牢牢 把握 "五个关键"：把握能源强国建设目标，强化战略引领；把握能源安全首要职责，筑牢发展基石；把握碳达峰战略 任务，推动绿色转型；把握 "人工智能 + 能源" 融合趋势，塑造未来优势；把握以人民为中心立场，促进普 ...

Core Viewpoint - The article emphasizes the importance of integrating artificial intelligence (AI) into the energy sector to enhance innovation, ensure energy security, and support the transition to a green and low-carbon energy system [2][3][4]. Group 1: Strategic Guidance - The implementation of AI in the energy sector is seen as a strategic choice to seize opportunities in technological competition and industrial transformation [3][4]. - The release of the "Implementation Opinions" serves as a guiding framework for the energy industry to deeply integrate AI, providing direction and a blueprint for development [3][4]. Group 2: Clear Objectives - The "Implementation Opinions" outlines a systematic approach to the "AI + Energy" initiative, focusing on high-value application scenarios and enhancing innovative application technology levels [5]. - The initiative aims to drive a comprehensive transformation in the energy sector through coordinated planning and sustained efforts [5]. Group 3: Application Focus - The article highlights the need to overcome common bottlenecks in AI applications within the energy sector, such as insufficient high-value scenarios and low resource sharing [6]. - It identifies eight key high-value application areas, including AI + power grid and AI + renewable energy, to facilitate the integration of AI into the energy system [6]. Group 4: Practical Exploration - The Southern Power Grid Company has proactively engaged in AI practices, establishing significant infrastructure and launching the first self-controlled large model "Da Watt" [7]. - The company aims to support the construction of a new power system and energy system through its AI initiatives [7]. Group 5: Accelerating Application Scenarios - The development of AI in the energy sector is expected to address challenges posed by the transition to a new power system, enhancing the capacity for clean energy consumption [8][9]. - The company has planned over 350 application scenarios across various business domains, demonstrating significant empowerment effects [9]. Group 6: Strengthening Key Technology Supply - The article stresses the importance of core elements such as computing power, data, and models in supporting the development of AI in the energy sector [10]. - The establishment of a self-controlled computing power cluster and high-quality data sets is crucial for driving innovation in electric power AI [10]. Group 7: Ensuring Development Support - A comprehensive approach is needed to ensure the long-term and complex integration of AI and energy, including the establishment of AI companies and research centers [11]. - The company aims to build a high-level talent pool and foster collaborative development within the AI ecosystem [11]. Group 8: Embracing AI for Future Development - The Southern Power Grid is committed to leveraging its digital transformation achievements to create a "digital grid intelligent body" that promotes efficient human-machine collaboration [12]. - The company aims to contribute to national AI innovation and support global green and low-carbon energy transitions [12].

Core Viewpoint - The article emphasizes the urgent need for the integration of artificial intelligence (AI) in the energy sector, highlighting its role as a key technological engine for building a new energy system and driving industry innovation [3][4]. Group 1: Necessity for Breakthrough - AI is recognized as a strategic force leading a new wave of technological revolution and industrial transformation, significantly impacting energy production and consumption [3]. - Current AI applications in the energy sector are fragmented, leading to resource redundancy and systemic barriers, which hinder long-term development [3][4]. Group 2: Development Goals - The "Implementation Opinions" set two key development targets for 2027 and 2030, focusing on foundational work and establishing benchmarks in the initial phase, followed by comprehensive empowerment and ecosystem construction in the later phase [4]. - The 2027 goal aims to establish industry-level professional large models and typical scenario exploration, while the 2030 goal seeks to achieve world-leading levels in AI technology within the energy sector [4]. Group 3: Implementation Pathways - The article outlines a systematic approach to enhance the quality and efficiency of AI in the energy sector, focusing on key technological breakthroughs, widespread application of industry-level large models, and deep empowerment of high-value scenarios [5][6]. - Key technical directions include solidifying data foundations, enhancing computational power, and improving model capabilities to address common challenges in the energy sector [7]. Group 4: Deep Application of AI - The "Implementation Opinions" propose focusing on high-value application scenarios in areas such as power grids, new energy, and traditional energy sources, aiming to enhance AI's role in energy supply-demand balance and safety monitoring [9]. - The goal is to create an intelligent closed-loop system for perception, analysis, decision-making, and execution, driving energy security and green transformation [9]. Group 5: Innovation Ecosystem - The article stresses the importance of building an open and collaborative industrial ecosystem to support systemic changes in the energy sector [10]. - It highlights the need for pilot demonstrations to stimulate innovation, establish standard norms for orderly development, and strengthen collaborative innovation mechanisms [11][13][14].

Core Insights - The article emphasizes the integration of artificial intelligence (AI) in the energy sector, particularly through the implementation of policies by the National Development and Reform Commission and the National Energy Administration to promote "AI + Energy" initiatives [1][5] - New Hope Group is highlighted as a leading example of successfully transforming technological innovations into green practices within the energy industry [1][5] Group 1: AI Applications in Energy Management - New Hope Group has developed a virtual power plant platform in Zhejiang Linping Economic Development Zone, utilizing AI algorithms for efficient resource aggregation and response, achieving a load aggregation of nearly 12.6 MW since its launch in July 2023, increasing revenue by approximately 0.5 yuan per kilowatt-hour [2] - The "Four Networks Integration" project in Anhui Xuancheng Economic Development Zone showcases AI's capability for comprehensive governance in regional energy systems, resulting in an annual carbon reduction of about 130,000 tons and significant cost savings in steam and electricity [3] Group 2: Advancements in Fusion Energy - New Hope Group is pioneering the application of AI in the field of nuclear fusion, aligning with national policies that support AI technology exploration in advanced energy sectors [4][5] - The company has established a digital twin system for fusion devices, enhancing the development of hydrogen-boron technology and achieving significant advancements in data integration and simulation analysis, which are crucial for future intelligent operations in fusion energy [4] Group 3: Strategic Vision and Industry Impact - The article outlines New Hope Group's dual approach of driving AI applications in current energy systems while also investing in cutting-edge technologies like fusion energy, positioning itself as a leader in the transition to a more intelligent and sustainable energy framework [5] - The integration of AI is portrayed as a core driving force behind the transformation of the energy sector, focusing on both immediate economic benefits and long-term environmental goals [5]