北京四方继保赵凤青：AI破解分布式光伏预测难题，数据质量与标准是规模化关键

Core Insights - The "AI + Energy Development Conference" held in Beijing focused on exploring new paths and opportunities for the integration of AI and the energy industry, attracting over 300 representatives from government, energy companies, and academia [1] Group 1: AI Integration in Energy - The core challenge in renewable energy is its volatility and randomness, particularly with wind and solar power, which poses high demands on grid scheduling and operational optimization [1] - Distributed photovoltaic (PV) systems are expected to account for over 40% of China's total PV installed capacity by 2024, complicating management due to their small size and dispersed nature [1] Group 2: AI Implementation Achievements - Beijing Sifang Automation has achieved significant results in AI applications for power automation, integrating data from distribution automation systems with electricity information collection systems to enhance the accuracy of distributed PV power forecasting [4] - A pilot project in Huai'an has successfully covered over 60,000 PV stations, achieving an average prediction accuracy of over 80%, which meets engineering standards and supports grid scheduling [4] Group 3: Challenges in AI Deployment - Despite recognizing the value of AI, significant bottlenecks remain in its large-scale implementation in the power sector, primarily related to data quality and standardization [5] - The lack of high-quality fault and anomaly data limits the training of adaptable AI models, while inconsistent data standards across different manufacturers complicate the deployment of AI technologies [5] Group 4: Proposed Solutions - Data sharing and standardization are identified as key areas for overcoming current challenges, with suggestions for utilizing federated machine learning to enable collaborative data use without compromising privacy [6] - The establishment of unified data input/output standards and IoT communication protocols is essential for facilitating efficient collaboration and scaling AI applications in the energy sector [6]