重磅！远景发布行业首个伽利略AI风机

Core Viewpoint - Envision Energy has launched the Galileo AI Wind Turbine, which aims to address major pain points in the wind power industry by providing more flexible and precise power generation strategies and higher reliability, marking a new phase in the application of physical artificial intelligence in the sector [1][3]. Summary by Sections Addressing Industry Pain Points - The Galileo AI Wind Turbine offers a validated solution to three major pain points in the wind power industry: inaccurate forecasting (power/load/consumption/electricity price), poor turbine performance, and high safety and quality risks. The implementation of the "Tianshu" energy model intelligent control platform has led to over a 20% increase in revenue for wind farms equipped with AI compared to those without [3][4]. Enhancing Forecast Accuracy - The "Tianji" meteorological model utilizes advanced computing power and a model with over 10 billion parameters to achieve significant breakthroughs. It integrates multi-modal data from satellites, radar, and ground stations, along with data from over 800 GW of global energy assets, to generate precise forecasts for the next 15-30 days within just three minutes [5][6]. Improving Power Generation Capability - The core of the Galileo AI Wind Turbine is a neural network with over 100 million parameters, functioning as a "super brain" for the turbine. This system, supported by high-performance chips, enables real-time online reasoning to handle complex, non-linear problems that traditional control logic struggles with. The intelligent control platform allows for real-time adjustments and self-healing capabilities, enhancing overall efficiency [7][8]. Increasing Warning Accuracy - The development of a high-fidelity digital twin platform is crucial for improving warning accuracy in the wind power sector. By leveraging AI computing power and extensive operational data, the integration of multi-modal information has significantly enhanced prediction accuracy. For instance, early detection of blade failures through sound and strain monitoring has improved maintenance scheduling, resulting in substantial operational gains [9][10]. Future of AI in Wind Power - The transition from traditional wind turbines to intelligent systems capable of understanding weather changes and market dynamics represents a significant evolution in the industry. The potential for further advancements in artificial intelligence applications within wind power remains vast [11].