## 1 Introduction
### Related Works  
Currently, domestic and foreign wind power prediction models are mainly categorized as physical methods, statistical models, machine learning, and deep learning methods. Among them, deep learning models are the main focus of current research. The physical model does not require historical data and needs to be combined with wind farm topography and meteorological conditions. However, the complexity of the method needs to be lowered and is computationally challenging. Statistical models are modeled after historical data on wind farms, such as probabilistic autoregression [6] and probability mass bias [7], which builds a relationship between wind power generation and explanatory variables to forecast power generation. These models need to be more capable of dealing with substantial data uncertainty. In nonlinear data, the performance of these models needs to be improved. Machine learning models are effective in handling nonlinear data. An improved support vector machine (ISVM) was used to forecast short-term wind power [8]. [PERSON] et al. used an improved extreme learning machine (ELM) for wind power prediction [9]. However, these models need to be improved in handling large datasets. Deep learning has become a research hotspot in wind power prediction due to its rapid development in recent years. Common deep learning methods include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc. The long short-term memory network (LSTM) is proposed based on RNNs, which can solve the problem of RNN gradient disappearance. [PERSON] et al. used a heap-based optimizer (HBO) to optimize the LSTM prediction of wind [10]. Although a gated recurrent unit (GRU) can solve the gradient disappearance problem like LSTM, it has a simpler model structure and a faster training speed and is widely used in wind power prediction. [PERSON] et al. performed a multi-step wind prediction using a combined model of GRU [11]. A CNN is utilized in time series prediction for accurate wind speed prediction using CNN-extracted features [12]. [PERSON] et al. converted features obtained after the variational mode decomposition (VMD) process into images and employed an improved residual-based deep CNN to predict wind power [13]. However, CNNs can only extract local features and ignore the relationship between the time series data. For this purpose, [PERSON] et al. used a TCN for time series prediction [14]. A TCN takes into account the timing characteristics and allows for flexibility in adding sensory fields. [PERSON] et al. used a TCN for wind prediction and compared it with other recurrent neural networks. The experiments showed that the TCN had better prediction results for time series data [15]. Due to the complexity of time series forecasting, it is difficult for a single model to predict the results accurately. Some scholars proposed a hybrid model to improve accuracy. [PERSON] et al. proposed a hybrid CNN-GRU model, which effectively extracts raw data features using a CNN. A GRU predicts wind power using the input features and verifies the model's effectiveness by comparing the prediction results with those of a single model [16]. [PERSON] et al. first preprocessed the raw data using GAN and proposed a hybrid TCN-GRU model for wind speed prediction, which was evaluated using four metrics [17]. [PERSON] et al. applied CNN-LSTM to predict wind speed and concluded that the combined model can deeply extract the temporal and spatial correlation features simultaneously [18]. Due to the non-stationarity and nonlinearity of time series data, [PERSON] et al. chose the VMD algorithm to decompose the wind speed. They used the particle swarm optimization (PSO) method to optimize the bi-directional long and short-term memory (Bi-LSTM) prediction model [19]. [PERSON] et al.