## 1 Introduction
[12] proposed a model based on the autoregressive summation sliding averaging model with an echo state network (ESN) to predict a short-term wind speed time series with linear features. While these models perform well in handling linear and simple time series, they face limitations when dealing with nonlinear relationships and complex data patterns. Their ability to capture intricate patterns is restricted, which limits their generalization capabilities [13]. Machine learning models, such as support vector machines, random forests, artificial neural networks, boosted regression trees, and extreme learning machines [14; 15; 16], observe and analyze data, automatically identify patterns and regularities in the data, and decide and predict future trends. Support vector machines (SVMs) can effectively deal with nonlinear relationships and perform well on small-sample data. [PERSON] et al. [17] introduced a least-squares support vector machine to address the ARIMA's limitations with nonlinear and complex data and proposed an improved SVM model to correct prediction errors. [PERSON] et al. [18] used a least-squares support vector machine as a prediction model and tuned and optimized its parameters with the help of the particle swarm algorithm to improve the prediction performance. However, the training and prediction efficiencies of SVMs for large datasets are low, especially when the data dimension is high. In order to overcome the problem of the poor SVM performance for large datasets, the random forest model can be used, which improves the accuracy and stability of the prediction by using multiple decision trees through an integrated learning approach. [PERSON] et al. [19] proposed a random forest regression (RFR) model based on the improved Drosophila optimization algorithm, which improves the global search capability of the prediction model by incorporating a function mechanism. [PERSON] et al. [20] proposed an artificial neural network (ANN) model to achieve the accurate prediction of fluctuating, unstable sequences by introducing the cuckoo algorithm. Although machine learning models excel at handling nonlinear relationships and high-dimensional data, they are still deficient at capturing long time series dependencies and complex data structures. Deep learning models, especially recurrent neural networks, long short-term memory (LSTM) networks, and convolutional neural networks, have also been widely used in wind speed prediction [21; 22; 23]. RNNs can effectively capture dependencies in time series data and are suitable for short-term time series prediction. [PERSON] et al. [24] decomposed an original wind speed series into multiple subsequences and used an RNN in the subsequences to predict the wind speed and error series in the sub-model. However, RNNs suffer from the problem of vanishing or exploding gradients when handling long sequence data, which increases the training difficulty. To solve this problem, LSTM effectively captures the long-time dependence by introducing a gating mechanism. [PERSON] et al. [25] used a vulture search algorithm to optimize the LSTM hyperparameters, combining its adaptability with the advantages of LSTM time series modeling to improve the prediction accuracy and stability. [PERSON] et al. [26] used the seasonal ARIMA to extract the linear part of a wind speed series and EMD to decompose the nonlinear residuals, and they trained the components with LSTM, integrating the results for the final prediction. LSTM can better handle long series data, capture the long-distance dependence, ensure that the weights are updated stably during the training process, and improve the training and generalization performance. To improve the prediction accuracy, BiLSTM further extends the capability of LSTM to capture bidirectional dependencies in sequences through bidirectional processing. [PERSON] et al.