AI智联新能源 重塑产业新生态

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].