# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 4 Experiments
### Comparative Analysis of Experimental Results
#### 4.5.2 Analysis of Prediction Results
Furthermore, the results from the three sets of experiments illustrate the effectiveness of incorporating the channel attention mechanism. Figure 10: Prediction results of different models. (**a**) Power consumption prediction based on SVM and ARIMA. (**b**) Power consumption prediction based on GRU and LSTM. (**c**) Power consumption prediction based on TCN and CNN-GRU. (**d**) Power consumption prediction based on CNN-LSTM and CACN-LSTM. Figure 11: The weights of the high-dimensional features. (**a**) High-dimensional feature weights without channel attention. (**b**) High-dimensional feature weights with channel attention. (**c**) The weight of each channel. Figure 12: Box plots of prediction metrics. (**a**) MAE box plots of different models; (**b**) RMSE box plots of different models; (**c**) MAPE box plots of different models; (**d**) R\({}^{2}\) box plots of different models. As can be seen from the box plot in Figure 12, the proposed CACN-LSTM model achieves the best results in terms of MAE, RMSE, MAPE, and R\({}^{2}\). As can be seen from Figure 12, the fluctuation range of each indicator of CACN-LSTM is also the smallest, indicating that CACN-LSTM not only has high prediction accuracy but also surpasses the benchmark model in terms of stability. Figure 13 shows the different evaluation metrics for each model. The validation through various metrics demonstrates that the proposed model outperforms the others. The MAE value for CACN-LSTM is 57.156, the RMSE value is 65.625, the MAPE value is 2.032, and the R\({}^{2}\) value is 0.935, all of which are superior to those of the other models. These results indicate that the CACN-LSTM model scores the highest across different evaluation metrics, followed by CNN-LSTM and CNN-GRU. In summary, the proposed CACN-LSTM model consistently outperforms the baseline model in terms of prediction accuracy. In addition, the results of ablation experiments show that CACN is effective in feature extraction of the SAG grinding process power consumption dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline SVM & 139.843 & 171.873 & 5.431 & 0.711 \\ ARIMA & 134.931 & 164.723 & 5.272 & 0.734 \\ GRU & 133.344 & 161.552 & 5.186 & 0.751 \\ LSTM & 127.551 & 155.591 & 5.025 & 0.763 \\ TCN [32] & 113.367 & 137.541 & 4.431 & 0.815 \\ CNN-GRU [33] & 99.206 & 122.329 & 3.892 & 0.853 \\ CNN-LSTM & 86.18 & 94.532 & 3.331 & 0.883 \\ CACN-LSTM & 57.156 & 65.625 & 2.032 & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Evaluation metrics of different models. Figure 13: Prediction metrics of different models. (**a**) MAE for different models. (**b**) RMSE for different models. (**c**) MAPE for different models. (**d**) R\({}^{2}\) for different models. Prediction values distribution  
In addition to analyzing the predictive performance and results of the model based on the result plots and evaluation metrics, this paper additionally assesses the model according to the distribution of its predicted values. The horizontal coordinates in Figure 14 represent the actual power consumption, while the vertical coordinates denote the predicted power consumption. The blue dots represent the distribution of predicted values, and the red line represents the true value. The distance from the blue dots to the red line signifies the distance between the model's predicted value and the actual value. The closer the blue dots are to the red line, the more accurate the prediction result. Figure 14: The distribution of prediction values and true values. (**a**) The prediction distribution of SVM; (**b**) The prediction distribution of ARIMA; (**c**) The prediction distribution of GRU; (**d**) The prediction distribution of LSTM; (**e**) The prediction distribution of TCN; (**f**) The prediction distribution of CNN-GRU; (**g**) The prediction distribution of CNN-LSTM; (**h**) The prediction distribution of CACN-LSTM.