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AFGY-1000 New Energy Power Generation Prediction System

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    Overview
    Beijing Aodewei Electric Power Technology Co., Ltd., in collaboration with United Grid Hunan Disaster Prevention Technology Co., Ltd., leverages the technical expertise of the State Key Laboratory for Disaster Prevention and Mitigation of Power Transmission and Transformation Equipment. Based on the development direction of power forecasting technology, they utilize the supercomputing center of the State Key Laboratory to develop the AFGY-1000 new energy power generation forecasting system. This system can significantly enhance the accuracy of power forecasting, achieving high-precision power forecasting and disaster prediction.


    The AFGY-1000 new energy power generation forecasting system, based on the development direction of power forecasting technology, utilizes the supercomputing center of the State Key Laboratory and fully leverages the technical advantages of cloud-edge-terminal collaboration (supercomputing center with strong computing power and fast computing speed, edge computing with virtual computing, container technology, low latency, etc.). It selects multiple global weather forecasting systems as backgrounds, imports satellite cloud images and radar data, updates numerical weather forecasting data in real time, and introduces high-temporal and spatial resolution mesoscale and small-scale weather forecasting models and turbulence models, supplemented by high-precision topographic and geomorphic data of wind and solar power sites. By comprehensively utilizing numerical weather forecasting and observable data, and employing data assimilation technology, it conducts high-precision mesoscale and small-scale meteorological forecasting, thereby obtaining wind power forecasts and ground effective solar radiation flux forecasts accurate to individual wind turbine points. Based on the obtained high-precision meteorological forecasting results, the system comprehensively utilizes physical models, calculations of various statistical models, multiple prediction algorithms, optimization and correction functions, continuous data mining techniques, robotic learning, and other technical means to obtain power forecasts for individual wind turbines and solar panels. Combined with downtime maintenance plans, it calculates the total power forecast value of the entire wind-solar power station. This system significantly improves the accuracy of power forecasting, achieves high-precision power forecasting and disaster prediction, and provides strong technical support for the safe and stable operation of the power grid and the improvement of the efficiency of new energy power stations.