Wind power interval prediction based on optimized deep learning network

Mengyuan Tong; Qingyang Shu; Xiangxi Guan; Yao Dong

doi:10.1117/12.3034449

16 October 2024 Wind power interval prediction based on optimized deep learning network

Mengyuan Tong, Qingyang Shu, Xiangxi Guan, Yao Dong

Author Affiliations +

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 132912L (2024) https://doi.org/10.1117/12.3034449
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

In order to implement the "dual carbon" goal and sustainable development strategy, this paper proposes a method for interval prediction of wind power through Bayesian-optimized bidirectional long short-term memory network. By combining the existing deep learning algorithms, the CEEMDAN-BO-QRBiLSTM model was constructed. The empirical analysis results show that the newly established model is feasible and has higher accuracy and better effect than other existing models.

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

Citation Download Citation

Mengyuan Tong, Qingyang Shu, Xiangxi Guan, and Yao Dong "Wind power interval prediction based on optimized deep learning network", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 132912L (16 October 2024); https://doi.org/10.1117/12.3034449

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