In this paper, we propose a Gated Recurrent Neural Network(GRU)-based application for ultra-short-term wind power prediction, correlating and analyzing wind tower data, SCADA data of wind farms, and Lidar wind measurement data. By combining meteorological assimilation techniques for wind resource data correction, model feature data is extracted as input according to data characteristics, and then short-term wind power prediction is performed by the GRU algorithm. In this regard, the accuracy of wind power prediction is boosted and the power system scheduling is optimized, thereby promoting new energy consumption and improving the stability of the power grid.
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