# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 4 Experiments
### Comparative Analysis of Experimental Results
#### 4.5.2 Analysis of Prediction Results  
1. Power consumption prediction  
To intuitively illustrate the prediction performance of the proposed CACN-LSTM model, Figure 10 shows the prediction results of the proposed model and the baseline models on the power consumption dataset of the SAG mill grinding process. From the prediction results shown in Figure 10a, the performance of SVM and ARIMA in predicting the power consumption trend of the SAG grinding process is generally unsatisfactory. In addition, the local prediction is poor, making it difficult to forecast the local peaks in power consumption variation, which is primarily due to their inadequate feature capture ability. Compared with SVM and ARIMA, GRU and LSTM show better prediction performance. From Figure 10b, they can predict the changing trend and local peak of power consumption relatively effectively, but there is still much room for improvement. Compared to GRU and LSTM, the CNN-GRU, TCN, and CNN-LSTM models with added convolution operations have partial local feature extraction capabilities and can effectively predict local trends. However, they still face challenges in accurately predicting obvious peaks and troughs. As shown in Figure 10d, the predictions of the CACN-LSTM model closely align with the original data, accurately predicting not only the overall trend and local trends of power consumption but also effectively predicting significant peaks and troughs. This indicates that the proposed CACN-LSTM model is capable of reliably and accurately predicting the local variations and overall trends in power consumption during the SAG mill grinding process. To visualize the adaptive weighting process of high-dimensional features by the CACN-LSTM model, the high-dimensional feature weights from a certain epoch during the training process are visualized. Figure 9: Input parameter correlation. Figure 10: _Cont._  
As shown in Figure 11, the horizontal coordinate is the feature dimension, and the vertical coordinate is the size of the time window. The area marked by the blue box indicates a decrease in attention, while the area marked by the green box indicates an increase in attention. Upon analyzing Figure 11a,b, the colors of the 23 rd and 36 th high-dimensional features in the heatmap are fading, indicating that the attention is less focused on these features. In contrast, the attention is more focused on the 11 th and 17 th high-dimensional features. Figure 11c shows the weights assigned by the channel attention mechanism to each high-dimensional feature, with a range from 0.50 to 0.64. It can also be clearly seen from Figure 11c that among the high-dimensional feature weights after training, the weights of the 11 th and 17 th high-dimensional features are relatively high, while the weights of the 23 rd and 36 th high-dimensional features are low. This is consistent with the weight changes during the training process shown in Figure 11a,b. In summary, CACN-LSTM effectively predicts the trend features and peak (crest and trough) characteristics of SAG mill power changes. It addresses the issue of key features being easily submerged in previous models during predictions. (2) Model comparative analysis  
The prediction accuracy of the proposed CACN-LSTM model is evaluated using four metrics, i.e., MAE, RMSE, MAPE, and R\({}^{2}\). Table 4 shows the sample measurement accuracy results of the CACN-LSTM model and the baseline model for the SAG mill power consumption dataset of the grinding process. Figure 12 shows the box plots of different metrics for each model. Figure 13 shows the comparison of the final accuracy metrics of different models. From Table 7, it is evident that the proposed CACN-LSTM model consistently outperforms the benchmark model in terms of MAE, RMSE, MAPE, and R\({}^{2}\). By comparing the prediction performance of the three models, LSTM, CNN-LSTM, and CACN-LSTM, it can be concluded that the CACN-LSTM model exhibits the best prediction performance.