## 3 Experiments and Analysis of Results
### Classification of Different Models  
To test the effectiveness of EO feature selection in industrial electricity scenarios, the experiments used a subset of EO-screened features as model input and trained KNN, SVM, DT, RF, DA as classifiers, respectively. The authors introduced 10-fold cross-validation to enhance the reliability and effectiveness of various classification models. The samples were randomly divided according to 70% training set and 30% test set, and five independent experiments were used to equalize the results. Table 2 presents information regarding the parameters of the basic classifier. The first concern is the accuracy. In Table 3, the mean value and standard deviation of accuracy from multiple experiments were calculated. Table 3 shows that all of the \begin{table}
\begin{tabular}{c c c} \hline
**Classifier** & **Core Parameter** & **Description** \\ \hline KNN & K = 5, Dmetric = Euclidean: & K denotes nearest neighbor number in input data used to classify each point, and Dmetric distance metric for the KNN algorithm. \\ \hline SVM & Kernel = \(r\)bf\({}^{\prime}\), C = 2: & “rbf” stands for radial basis function for kernel function selection, C represents penalty parameter. \\ \hline DT & DTmax = 20: & DTmax indicates the maximum number of decision splits or branch nodes that can exist in each tree. \\ \hline RF & Rftree = 25, FEmax = 10: & Rftree means the number of trees in the forest, FEmax indicates the setting of the maximum number of features. \\ \hline DA & DE = ’linear’, S = 0.2: & DE is the discriminant function, ”linear” means linear discriminant, S indicates the setting of the smoothing parameter. \\ \hline \end{tabular}
\end{table} 
Table 2: Classifier parameters. Figure 5: Visualization of the screened feature distribution. features screened by EO had an accuracy of more than 92% in electric load identification. Among them, the average accuracy of SVM was higher than that of the rest of the classifiers, reaching 95.58%. The average accuracy of DA was the lowest, but it also reached 92.63%. Additionally, the authors found that the stability of SVM was higher than that of the other classifiers in the experiment, with a standard deviation of 0.62%. The main reason why SVM provided the lowest classification stability while ensuring optimal classification was that it is more suitable for processing low-frequency power signals. To demonstrate the practical effectiveness of the method proposed in this paper for identifying electrical loads in industrial settings, visualizations were created. In Figure 6, the confusion matrix demonstrates the classification details of the electrical loads for SVM. From Figure 6, the identification of each industrial piece of equipment can be clearly understood. The blue color indicates incorrect classification and the red color indicates correct classification. Among them, 82% of the equipment type ETE was correctly identified and 18% was incorrectly identified as CMW. Additionally, 85% of the equipment type CME was correctly identified, while 5% was incorrectly identified as ETE, 5% was incorrectly identified as CMW, and 5% was incorrectly identified as CCA. Further, 95% of the equipment type KHT was correctly identified and 5% was incorrectly identified as CCA. All of the rest of the equipment was correctly identified. Therefore, the features screened by EO from the joint features can help the model analyze the industrial equipment types accurately. EO feature selection is feasible in terms of the accuracy of electric load identification. Figure 7 shows the computation time of the model. From Figure 7, the computation time of EO-screened features in electric load identification was less than 8 s. The time cost of different classifiers varied significantly, among which SVM had the most stable computation