鼓励企业牵头联合科研机构、高校等单位，深化产学研用合作，围绕未来能源前沿技术领域，高标准建 设一批行业研发创新平台，重点支持柔性钙钛矿电池组件中试、新型储能安全监测、流体力学共享测 试、固态电池材料研发、动力电池大数据、高潜热相变材料等共性技术平台。 加速自主可控核心部件装备规模化生产，重点布局新型储能(新型电池)、先进氢能装备、下一代钙钛矿 光伏关键组件、超导磁体及线材等关键核心零部件项目。 依托"未来能源小镇"建设，打造绿色集约的产城融合示范场景，鼓励多种先进低碳技术集成应用，支持 燃料电池汽车示范运营。开拓创新应用场景，以分布式光伏、新型储能和智能微电网为基础打造可复制 的零碳标杆园区转型样板。 12月11日，北京经济技术开发区管理委员会印发《北京经济技术开发区关于加快培育未来能源产业的若 干措施》。文件提出，聚焦新型储能、清洁能源、低碳转型、聚变能源等细分赛道，高标准建设北京新 型储能产业示范"协同发展区"。 文件提出，重点支持新型储能、清洁能源、低碳转型、聚变能源四大核心方向，鼓励原创性、颠覆性前 沿技术创新成果快速完成"技术熟化—中试验证—产业化"全链条孵化，促进产学研深度融合。培育一批 科技创新 ...

Core Viewpoint - The integration of artificial intelligence (AI) with renewable energy is essential for achieving high-quality development in the energy sector, addressing challenges such as the volatility and intermittency of renewable energy output [3][5][12]. Group 1: AI Applications in Renewable Energy - The implementation of AI in renewable energy focuses on high-precision power forecasting, smart operation of energy stations, and optimizing the collaboration of renewable resources [3][12]. - AI technology can enhance efficiency, reduce costs, and foster innovative models across all stages of renewable energy production, dispatch, and management [3][6]. Group 2: Importance of Power Forecasting - Improving the power forecasting level for renewable energy is crucial for the safe and stable operation of new power systems and efficient consumption of renewable energy [5][7]. - Extreme weather conditions can cause significant fluctuations in renewable energy output, as evidenced by a 97% drop in wind power output in Shandong within a day and a half during a cold wave [5][7]. Group 3: Challenges and Solutions - Traditional forecasting methods struggle under extreme weather conditions, leading to potential risks in power balance and supply reliability [7][11]. - Data quality is a critical issue affecting the integration of AI and renewable energy, with challenges in data accuracy, completeness, and consistency impacting AI model training and prediction accuracy [11][12]. Group 4: Efficiency Improvements through AI - The establishment of a centralized management platform for renewable energy stations can significantly enhance operational efficiency, with reported improvements in inspection efficiency by 6 to 10 times [9][10]. - AI models can achieve a monitoring accuracy of over 95%, with response times improved from hours to minutes [9][10]. Group 5: Future Prospects - The future of "AI + renewable energy" integration holds potential for deeper applications, including a unified model for weather forecasting, power forecasting, smart trading, and intelligent operation [12]. - This integration aims to increase the share of renewable energy in the energy structure, reduce reliance on fossil fuels, and lower carbon emissions [12].

Core Viewpoint - The integration of artificial intelligence (AI) with the energy sector is accelerating, driven by the need for precise forecasting and intelligent trading strategies in the context of a new power system and high penetration of renewable energy sources [1][7]. Group 1: AI and Renewable Energy Integration - The implementation of AI models is essential for enhancing power forecasting accuracy and optimizing trading strategies in the energy market [1][7]. - The company, Guoneng Rixin, is focusing on deepening the application of AI models in scenarios such as power forecasting, electricity trading, and virtual power plants to maintain a leading position in the renewable energy information service market [1][7]. Group 2: Technological Advancements - The company has developed the "Kuangming" renewable energy model, which has undergone two technical iterations this year, improving the overall accuracy of renewable energy forecasting by 1%-1.5% after the upgrade to version 2.0 in May [2][3]. - The latest version 3.0 of the model incorporates a stable and efficient computing architecture, allowing for pricing every 15 minutes, which significantly enhances revenue per kilowatt-hour for renewable energy companies [3]. Group 3: Revenue Growth and Market Position - In the first half of the year, the company's revenue from renewable energy power forecasting products reached 205 million yuan, a year-on-year increase of 55.14%, driven by the growth in installed renewable capacity and new demand from distributed photovoltaic customers [4]. - Despite the revenue growth, the gross margin for this business segment declined due to changes in the revenue structure, with expectations for recovery as software service fees are recognized [4]. Group 4: Business Strategies and Innovations - The company employs three main strategies to enhance customer retention and renewal rates: optimizing algorithms and AI model applications to improve forecasting accuracy, establishing a nationwide 24/7 operational service system, and adapting products to meet provincial grid assessment rule changes [4]. - The company has also ventured into innovative businesses such as energy storage management, electricity trading, virtual power plants, and microgrid energy management, generating 16 million yuan in revenue in the first half of the year [4]. Group 5: Future Directions - The company plans to further enhance its market share in power forecasting, particularly in the distributed market, while reducing hardware and service costs through standardized service processes [7]. - Continued focus on the iterative development of the "Kuangming" model and the integration of AI technology in deep learning and renewable energy business scenarios is expected to prepare the company for comprehensive marketization of electricity trading [7].