# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 2 Materials and Methods
### Overall Prediction Framework for the Proposed Method: A Real Case  
As illustrated in Figure 7, the proposed method is applied to the overall predictive framework for a real-case scenario. It mainly includes the preprocessing of the original data, prediction using the AM-MCNN-SBiLSTM model, and performance verification. The specific process is described as follows:  
(1) The wind power-related dataset includes meteorological characteristics such as wind speed, wind direction, temperature, humidity, air pressure, and historical wind power. In the subsequent experiments, the model's predictive performance was assessed by dividing the dataset into a training set comprising the first 95%, and a test set consisting of the remaining 5% [44,45]. (2) Typically, the information collected from wind farms inevitably embodies disparities in numeric dimensions across various characteristics. In order to transform the raw data into an input that the model can understand, we performed data min-max normalization on the collected dataset. It employs a common normalization method that maps data linearly onto the range of [0,1], as depicted by Equation (15). \[x_{nomm}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{15}\]  
\(x_{norm}\) is the normalized value, \(x\) is the original value in the dataset, and \(x_{max}\) and \(x_{min}\) are the maximum and minimum values in the dataset. (3) Input the preprocessed dataset into the attention mechanism to obtain a weighted new feature sequence. The new feature sequence is used as the input for the MCNN to fuse the high-dimensional features extracted by different convolutional kernels. The fused feature information is then input into a stacked three-layer BiLSTM network to obtain the prediction result. To give the data physical meaning, perform inverse normalization on the predicted results using the following formula:  
\[x_{nnorm}^{*}=(x_{max}-x_{min})x^{*}+x_{min} \tag{16}\]  
In the formula, \(x_{dnorm}^{*}\) represents the final prediction value, and \(x^{*}\) represents the normalized prediction value. (4) To verify the efficacy of proposed methodologies from multiple perspectives, we selected an ANN, LSTM, LSTM-AM, and AM-LSTM as the first group of comparative models to study the impact of introducing the attention mechanism on prediction results in the model. The CNN, MCNN, CNN-LSTM, and MCNN-LSTM were chosen as the second group of comparative models to verify the advantages of multi-convolutional neural networks in prediction. Different layers of BiLSTM networks were selected for the third group of comparative experiments to explore the influence of BiLSTM layers on prediction results. CNN-BiLSTM, CNN-SBiLSTM, and MCNN-SBiLSTM models were chosen for the fourth group of comparative experiments along with the proposed AM-MCNN-SBiLSTM prediction model to further validate the effectiveness of the proposed model. All the hybrid models mentioned above are composed of single models, and the specific descriptions of each single model can be found in Table 1. The batch size is set to 64, with the previous time step set to 96, the number of iteration epochs is 120, and the Adam optimizer is selected. All models were trained several times, and finally, the best parameters that do not produce overfitting were selected, as shown in Table 2.  
(5) By changing the prediction step size for multi-step prediction to verify the stability of the prediction model, four common error metrics are used to evaluate the performance of each model, including _MAE_, _MSE_, _RMSE_, and \(R^{2}\), as shown in Formulas (17) to (20). \[MAE=\frac{1}{N}\sum_{i=1}^{N}|\hat{p}_{i}-p_{i}| \tag{17}\]  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(\hat{p}_{i}-p_{i})^{2} \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{p}_{i}-p_{i})^{2}} \tag{19}\]  
Figure 7: Overall framework for the application of the proposed model to a real case.