## 1 Introduction
[27] proposed a wind speed prediction model based on singular spectrum analysis and BiLSTM that incorporates a back-propagation layer to enhance the capture of bidirectional temporal information and accurately simulate time series dynamics. However, these methods rely excessively on selecting similar daily samples in the wind speed prediction modeling process, resulting in limited flexibility in the model construction and updating. Thus, meeting the accuracy requirements of the power grid is challenging [28]. Combined forecasting integrates multiple independent models, offering high practicality and flexibility, and leverages the strengths of each model while addressing their limitations. Through adjusting the weighting coefficients, combined forecasting can adapt to various prediction requirements [29]. The common combined prediction methods include error-based correction, multi-mode weighting, and data decomposition and optimization techniques. To improve the prediction accuracy, [PERSON] et al. [30] proposed an ultra-short-term wind speed prediction method incorporating advanced techniques, including variational mode decomposition, phase space reconstruction, the improved Northern Eagle optimization algorithm, and a shared weight gated memory network. The method optimizes two hyperparameters to achieve the optimal parameter combinations for wind speed prediction, improving the performance and better adapting the model to wind speed data characteristics and variations. Although this method significantly optimizes the prediction performance and adapts the model to the characteristics of and variations in wind speed data, it also increases the computational cost and training time because of its complexity. [PERSON] et al. [31] proposed a wind speed prediction model integrating variational modal decomposition, an extreme learning machine, and LSTM. Decomposition, feature extraction, and residual modeling were combined to improve the accuracy of the short-term wind speed prediction. Although this approach integrates the advantages of multiple algorithms, it may still be limited by single-model inadequacy when processing complex, nonlinear wind speed data. [PERSON] et al. [32] proposed an ultra-short-term wind speed prediction model that combines CEEMDAN with an attention-mechanism-enhanced BiLSTM model. The model predicts each decomposed modal component and significantly improves the prediction accuracy through the final reconstruction of the results. However, optimizing combined prediction models often requires adjusting various parameters, which typically relies on expert experience, and the hyperparameters set by experts are often not fully aligned with the optimal parameters required by the model. In addition, when predicting nonlinear wind speed series, the feature extraction may weaken as the model complexity increases, and the investment of more computational resources and training time is needed in order to obtain a good performance. Although the existing studies have made some progress in wind speed prediction, the existing models still need to be improved in terms of their accuracy and applicability. To address this issue, we propose an improved bidirectional long short-term memory (BiLSTM) neural network model based on fully adaptive noise ensemble empirical modal decomposition (CEEMDAN), the RIME optimization algorithm (RIME), and a multi-head attention mechanism (MHSA). The model is used to predict wind speed variations in wind farms, aiming to improve the prediction accuracy and reduce the impact of the wind speed uncertainty on the grid stability. When evaluating the performance of this model in wind speed prediction in comparison with those of other methods, the wind speed prediction based on the CEEMDAN-RIME-MHSA-BiLSTM method is more accurate. Simulation results show that the model proposed in this study performs well in dealing with wind speed series fluctuations and complex dependencies, which verifies its validity and superiority with high prediction accuracy and robustness.