“人工智能+”能源有了行动指南！

Core Viewpoint - The implementation of the "Artificial Intelligence + Energy High-Quality Development" initiative aims to accelerate the integration of artificial intelligence with the energy sector, supporting high-quality development and safety in the energy industry by establishing clear development goals for 2027 and 2030 [1][4]. Group 1: Development Goals - By 2027, a preliminary integration system of energy and artificial intelligence will be established, with significant breakthroughs in core technologies and widespread applications across various energy sectors [4]. - By 2030, the overall technology and application of artificial intelligence in the energy sector will reach a world-leading level, with a well-established mechanism for collaborative development of computing power and electricity [5]. Group 2: Key Tasks and Focus Areas - The initiative outlines a framework focusing on "application scenario empowerment + key technology supply," aiming to enhance the innovative application of artificial intelligence in the energy sector [7]. - It identifies eight key areas for the application of "Artificial Intelligence +" in traditional and emerging energy sectors, including electricity, oil, gas, and coal [7][8]. - A total of 37 key tasks for the integration of artificial intelligence and energy have been planned, covering over a hundred specific scenarios across various energy types [8]. Group 3: Technical Challenges and Solutions - The initiative addresses technical challenges such as data isolation, fragmented computing power, and high energy consumption in computing, proposing three key areas for technological breakthroughs: data foundation, computing power support, and model capability enhancement [8]. - It emphasizes the need to establish high-quality data sets, ensure the security of energy data, and create a collaborative development mechanism for computing power and electricity [8]. Group 4: Implementation and Coordination - The National Energy Administration will focus on strengthening organizational implementation, enhancing collaboration between industry, academia, and research, and accelerating the transformation of results to ensure the smooth progress of the initiative [9].