Research on prediction accuracy of port wind power systems based on different prediction models

Qingguo Liu; Chengzhi Yang; Lijun Yin; Wei Zhang; Muhan Zhang; Zheng Xie

doi:10.1117/12.3031244

19 July 2024 Research on prediction accuracy of port wind power systems based on different prediction models

Qingguo Liu, Chengzhi Yang, Lijun Yin, Wei Zhang, Muhan Zhang, Zheng Xie

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131814E (2024) https://doi.org/10.1117/12.3031244
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

The port has a good wind resource endowment and construction conditions to support sustained scale development, and therefore has the potential for renewable energy to be used in and sent out of the port. The prediction of wind resources in ports is an important foundation to ensure the reliability of the power supply. In this paper, the LGBM model, LSTM model, and Transformer neural network model are discussed in detail. Among them, the Transformer neural network model has the smallest prediction error and meets the prediction index.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingguo Liu, Chengzhi Yang, Lijun Yin, Wei Zhang, Muhan Zhang, and Zheng Xie "Research on prediction accuracy of port wind power systems based on different prediction models", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131814E (19 July 2024); https://doi.org/10.1117/12.3031244

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