Core Viewpoint - The integration of artificial intelligence (AI) into the energy sector is essential for driving high-quality development and addressing the urgent needs of the industry, as outlined in the recent government initiatives [2][3][4]. Group 1: Development Goals and Implementation - The "Implementation Opinions" set clear development goals for 2027 and 2030, focusing on foundational work and establishing benchmarks in the initial phase, followed by comprehensive empowerment and ecosystem building in the later phase [4]. - The 2027 goals emphasize the application of industry-level professional models and typical scenario exploration, aiming to lay a solid foundation for large-scale applications [4]. - The 2030 goals aim for the energy sector's AI technologies to reach a world-leading level, fostering global innovation bases and cross-domain empowerment [4]. Group 2: Key Technical Support - The core foundations for AI application in the energy sector include data, computing power, and algorithms, addressing issues like data silos and algorithm interpretability [6]. - The "Implementation Opinions" propose three key technical breakthroughs: solidifying data foundations, enhancing computing power support, and improving model capabilities [6]. Group 3: Specialized AI Model Development - The focus is on developing over five specialized models tailored to the unique characteristics of energy sectors such as electricity, coal, and oil and gas [8]. - The integration of large models with specialized software and innovative application modes is crucial for enhancing decision-making capabilities in the energy sector [8]. Group 4: High-Value Application Scenarios - The "Implementation Opinions" identify key application scenarios in areas like 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 the transition to a green and low-carbon energy system [9]. Group 5: Innovation Ecosystem - The establishment of an open and collaborative innovation ecosystem is vital for the systemic transformation of the energy sector [10]. - The "Implementation Opinions" emphasize pilot demonstrations to unlock application potential and the creation of standards to ensure orderly development [11][12]. - Collaborative innovation through platforms and alliances is encouraged to address common challenges and promote effective technology transfer [13].

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].