Short-term wind power prediction based on ICSA-BiLSTM

Lei Chen; Jianguo Li; Yingbo Tao

doi:10.1117/12.3004337

25 September 2023 Short-term wind power prediction based on ICSA-BiLSTM

Lei Chen, Jianguo Li, Yingbo Tao

Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 1278827 (2023) https://doi.org/10.1117/12.3004337
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia

Abstract

In order to improve the accuracy of short-term wind power prediction, an improved chameleon Swarm algorithm is proposed and applied to the wind power prediction model established in BiLSTM. Firstly, Circle chaotic mapping was used to initialize the population of the standard Chameleon Swarm algorithm, then spiral search in the whale algorithm was used to improve the global search ability and Levy flight strategy was introduced to improve the local development ability, and then Gaussian detection was used to improve the optimization ability. Finally, the Bilstm-based prediction model was established and the historical wind farm data was used for example analysis.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lei Chen, Jianguo Li, and Yingbo Tao "Short-term wind power prediction based on ICSA-BiLSTM", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 1278827 (25 September 2023); https://doi.org/10.1117/12.3004337

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