# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 1 Introduction
Commonly used CNNs have only one type of convolutional kernel, limiting their ability to capture hidden features of different scales when processing multivariate wind power data [27]. To accurately predict wind power, the use of CNNs with multiple convolutional kernels has become urgently needed [28]. However, CNNs still struggle to capture long-term trends in time series. Therefore, the prediction results using only a single model are not satisfactory. The combined models can leverage the strengths of diverse models and have strong adaptive abilities when dealing with non-stationary signals [29; 30]. A prediction method of CNN-LSTM was proposed in reference [31]. It was demonstrated that the prediction performance of the CNN-LSTM model exceeded that of either a standalone CNN or LSTM. [PERSON] et al. [32] combine LSTM and the K-Means clustering algorithm with the non-parametric kernel density estimation (KDE) method to improve prediction accuracy. The forecasting method described above uses the long-term trends of the raw data, which are messy, and with an increasing prediction time range, information loss is likely to occur, thus affecting the prediction performance. One way to tackle this challenge is to decompose the wind power data for the better learning of its structural and characteristic patterns by models. [PERSON] et al. [33] use variational mode decomposition and weighted permutation entropy (VMD-WPE) decomposition of historical wind power and key meteorological features as inputs to build a CNN-LSTM model and use different optimizers to seek out the best parameters for the model, with the aim of achieving accurate prediction outcomes. However, this two-stage decomposition method increases the complexity of data processing, and the sensitivity of VMD to noise can also affect the prediction results. In addition, analyzing only key meteorological features equally without considering the differences in the impact of various meteorological features on wind power output will also reduce the accuracy of the forecast. Another method to improve prediction accuracy is to add an attention mechanism to the model. [PERSON] et al. [34] proposed a CNN-LSTM-Attention prediction method, which weights the output of CNN-LSTM with attention to make the model focus more on the important features for prediction results, reduce information loss, and improve prediction accuracy. However, this way of introducing the attention mechanism requires the combined models to have a high ability to extract features and their long-term trends. In summary, distinguishing the importance of different wind power characteristics and exploring a combination model that can fully extract input feature information and capture the time dependency of feature sequences is a major challenge to improve prediction accuracy. Therefore, this paper proposes a wind power prediction method based on feature weighting and combination models to overcome the limitations of existing methods and achieve higher accuracy predictions. This article makes the following contributions:  
* The attention mechanism is used to dynamically assign the weights of each input feature to distinguish the importance of different features on the impact of wind power output. In addition, the order in which the attention mechanism is introduced allows the model to be more focused on the information that is more important to the prediction results when extracting features. * The MCNN with different convolutional kernels can extract the feature information of different scales more comprehensively. SBiLSTM can better capture the temporal dependencies of feature sequences. The two neural networks in the combined model play to their respective strengths, enhancing the model's capacity to extract features and their long-term trends. * Using real wind farm data, four groups of comparative experiments are carried out to verify the effectiveness and stability of the proposed method; based on four commonly used error indicators, the proposed models all demonstrated the best prediction accuracy.