Wind speed prediction and reconstruction based on improved grey wolf optimization algorithm and deep learning networks

Zhu, Anfeng; Zhao, Qiancheng; Yang, Tianlong; Zhou, Ling; Zeng, Bing

doi:10.1016/j.compeleceng.2024.109074

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…It optimizes bidirectional gated recurrent networks with the Improved Chimpanzee Optimization Algorithm, significantly enhancing the model's predictive accuracy and robustness. In summary, utilizing optimization algorithms enables the model to maintain optimal parameters [36][37][38][39][40], thereby potentially enhancing the accuracy of wind speed predictions.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Wind Speed Prediction for Bridge Site Area Based on Wavelet Denoising OOA-Transformer

Gao,

Cao,

et al. 2024

Mathematics

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Predicting wind speed in advance at bridge sites is essential for ensuring bridge construction safety under high wind conditions. This study proposes a short-term speed prediction model based on outlier correction, Wavelet Denoising, the Osprey Optimization Algorithm (OOA), and the Transformer model. The outliers caused by data entry and measurement errors are processed by the interquartile range (IQR) method. By comparing the performance of four different wavelets, the best-performing wavelet (Bior2.2) was selected to filter out sharp noise from the data processed by the IQR method. The OOA-Transformer model was utilized to forecast short-term wind speeds based on the filtered time series data. With OOA-Transformer, the seven hyperparameters of the Transformer model were optimized by the Osprey Optimization Algorithm to achieve better performance. Given the outstanding performance of LSTM and its variants in wind speed prediction, the OOA-Transformer model was compared with six other models using the actual wind speed data from the Xuefeng Lake Bridge dataset to validate our proposed model. The experimental results show that the mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of this paper’s method on the test set were 4.16%, 0.0152, and 0.9955, respectively, which are superior to the other six models. The prediction accuracy was found to be high enough to meet the short-term wind speed prediction needs of practical projects.

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