人工智能技术及应用：面向新型配电系统的数据机理融合

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The integration of artificial intelligence (AI) with energy systems is identified as a crucial driver for high-quality energy development, aiming to enhance the reliability and efficiency of energy systems while supporting green and low-carbon transitions [5][7][10]. - The report emphasizes the importance of data-mechanism fusion technology in advancing AI applications within the energy sector, particularly in the context of new power distribution systems [18][32]. - The future outlook includes the development of a digital twin system for new power distribution systems, which will enhance decision-making and risk management through real-time data integration and intelligent feedback mechanisms [111][113]. Summary by Sections 1. Background Significance - The report highlights the role of AI as a significant driver of productivity and a key component in the new energy landscape, particularly in the context of China's dual carbon goals [5][8][12]. - It discusses the challenges faced by traditional AI methods in energy systems, such as sample dependency and limited generalization capabilities, which necessitate the adoption of data-mechanism fusion approaches [15][18]. 2. Key Technologies - Scientific Intelligence: Defined as a new paradigm in scientific research driven by AI, focusing on learning, simulation, prediction, and optimization to facilitate scientific discovery [23][25]. - Data-Mechanism Fusion: This method combines data-driven and mechanism-driven approaches to enhance model accuracy and decision-making reliability in energy systems [32][44]. - The report outlines five fusion models: serial, feedback, parallel, guiding, and embedding, which facilitate the integration of data and mechanism knowledge [48][51]. 3. Application Exploration - Calculation Inference: The report details a two-phase method for identifying line parameters in distribution networks, which significantly reduces errors in parameter estimation [66][73]. - Source-Load Forecasting: It discusses the development of a core technology system for high-precision forecasting of source-load dynamics, addressing challenges such as complex temporal data relationships and information gaps [76][78]. - Operational Optimization: The report presents a framework for edge-cloud collaborative optimization, enhancing computational efficiency and data privacy in regional energy internet applications [87][89]. 4. Future Outlook - The report envisions a future where scientific intelligence and data-mechanism fusion technologies will drive innovations in power system operations, scheduling, and decision-making [104][107][109]. - It emphasizes the need for robust and interpretable models that can adapt to dynamic and uncertain environments, ensuring the safety and reliability of power grid operations [113].