## 4 Comparison Experiments  
### Comparison before and after Feature Extraction  
To demonstrate the necessity of feature extraction in industrial power scenarios, experiments were compared using raw power data and three independent feature sets (after feature extraction). KNN, SVM, DT, RF, and DA were chosen as the base classifiers in the experiments, and different data types were used as model inputs. The authors introduced 10-fold cross-validation to enhance the reliability and effectiveness of various classification models. The classification accuracy and computation time for nine pieces of industrial equipment are shown in Figure 8. As illustrated in Figure 8, the left panel exhibits a comparison of classification accuracy between the original power data and three independent features, while the right panel compares the computational time of the models.  
Firstly, observe the comparison of the accuracy before and after feature extraction. The raw power data were poor on different classifiers, where the classifier with the highest accuracy was RF with 62.5%. The classifier with the worst classification accuracy was DA, which was only 51.12%. For industrial power scenarios, raw power data cannot meet the usage requirements in terms of accuracy. The three load features extracted from the raw power data improved the effect on the classifier significantly. Among them, entropy features exhibited a higher classification accuracy in comparison to time domain and frequency domain features. Taking SVM as an example, the classification accuracy of the entropy value feature reached 84.17%, which was 52.18% better than that of the raw power data. Subsequently, focus on the change in model computation time before and after feature extraction. The difference in raw power data was more evident in different classification models. Among them, SVM had the shortest computation time of 4.2412 s. In contrast, RF had the longest computation time, which reached 86.3788 s. The main reason for such an extended model computation time for raw power data was the high dimensionality of the data and the complexity of the model computation. After extracting the features, the time cost of the model was greatly reduced. Among them, the computation time for the three independent feature sets was relatively similar. Taking SVM as an example, the computation  
Figure 7: Computation time of different classifiers.  
time for the entropy value feature model was only 0.1353 s, which was 3.19% that of the initial power dataset. Moreover, computation time for time domain features and frequency domain features also declined, to 0.1697 s and 0.1644 s, respectively. Due to the variability of classifiers, the time reduction for RF was the most obvious, and the computation time for entropy value features was reduced by 78.5805 s after feature extraction. With feature extraction, the model input dimensionality of industrial equipment was reduced from 288 to 14 dimensions (entropy value features). The remarkable decrease in dimensionality truly minimized the model computation time and simultaneously enhanced recognition accuracy. Thus, feature extraction not only preserves the information relevance of the industrial equipment, but also greatly reduces the redundancy of the data.