# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 3 Results and Discussion
### Prediction and Analysis
#### 3.2.4 Experiment IV: The Performance of the Proposed Model  
As depicted in Figure 11, the predicted curve of the proposed model in the test samples closely aligns with the trend of the true values curve, and in Table 6, the AM-MCNN-SBiLSTM has the highest prediction accuracy, which all indicates that the model possesses remarkable predictive capabilities. In wind power forecasting, multi-step prediction is achieved by changing the future time steps to be predicted. In Table 7, the AM-MCNN-SBiLSTM model still has the highest prediction accuracy in two-step and three-step predictions. It can be further concluded from the comparison between the AM-MCNN-SBiLSTM and MCNN-SBiLSTM models that weighting the input feature data with attention can help improve the prediction accuracy. The predictions made by MCNN-SBiLSTM consistently outperformed those of CNN-SBiLSTM, which further validates the stronger feature extraction capability of multiple convolutions. Similarly, CNN-SBiLSTM outperforms CNN-BiLSTM in all four error metrics, once again proving that stacking three layers of BiLSTM has a more comprehensive learning ability for wind power data. Figure 10: Prediction curves of BILSTM for different layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline One layer & 6.255 & 64.357 & 8.022 & 0.961 \\ Two layers & 5.458 & 44.718 & 6.687 & 0.973 \\ Three layers & **5.132** & **43.638** & **6.606** & **0.974** \\ Four layers & 6.542 & 66.495 & 8.155 & 0.960 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error metrics for BiLSTM with different numbers of layers. Figure 12 demonstrates that the prediction performance of the contrast models fluctuates significantly as the prediction horizon increases. In contrast, the AM-MCNN-SBiLSTM model shows only slight variations in its error metrics. This indicates that the proposed model exhibits more efficient and stable predictive performance compared to other models, and can better describe the changing trends of wind power sequences. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline CNN-BiLSTM & 4.676 & 34.857 & 5.904 & 0.979 \\ CNN-SBiLSTM & 4.299 & 31.764 & 5.636 & 0.981 \\ MCNN-SBiLSTM & 3.681 & 22.081 & 4.699 & 0.987 \\ AM-MCNN-SBiLSTM & **3.194** & **17.424** & **4.174** & **0.990** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Error metrics for 1-step forecasts: 12 h ahead of schedule. Figure 11: The predicted curve of the proposed model. To further validate the model's performance, the time resolution range is adjusted from 12 h ahead to 24 h ahead. In Tables 8 and 9, the error metrics of the proposed AM-MCNN-SBiLSTM model are still optimal under different step sizes. The \(R^{2}\) values for one-step, two-step, and three-step forecasts are 0.994, 0.994, and 0.992 respectively. Figure 13 depicts the \(R^{2}\) fit effectiveness of the proposed model for multi-step predictions, indicating a high degree of compatibility between predicted values and actual observed data, as well as greater reliability in prediction performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R2\({}^{2}\)** \\ \hline CNN-BiLSTM & 4.380 & 36.180 & 6.015 & 0.985 \\ CNN-SBiLSTM & 4.341 & 33.823 & 5.816 & 0.986 \\ MCNN-SBiLSTM & 3.562 & 21.964 & 4.687 & 0.991 \\ AM-MCNN-SbiLSTM & **3.038** & **15.048** & **3.879** & **0.994** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Error metrics for 1-step forecasts: 24 h ahead of schedule. Figure 12: Comparison of multi-step, 12 h ahead forecasting performance.