Research and application of ultra-short-term wind power prediction based on gated recurrent neural network

Tao Xing

doi:10.1117/12.2656754

20 October 2022 Research and application of ultra-short-term wind power prediction based on gated recurrent neural network

Tao Xing

Author Affiliations +

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124515F (2022) https://doi.org/10.1117/12.2656754
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

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.

Citation Download Citation

Tao Xing "Research and application of ultra-short-term wind power prediction based on gated recurrent neural network", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124515F (20 October 2022); https://doi.org/10.1117/12.2656754

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