## 1 Introduction
The identification of electric loads is widely studied but is currently focused on residential electricity scenarios. The industrial electricity scenario is more challenging to study due to the harsh sampling environment and diverse equipment types. Therefore, how to achieve accurate identification of electric loads in industrial electricity scenarios is an urgent problem. The technical route of electric load identification usually includes feature extraction, feature selection, and equipment classification. For industrial power scenarios, extracting valuable features from low-frequency data and screening out the appropriate feature combinations from the joint feature set determine the effectiveness of equipment identification. YU et al. [24] used V-I trajectories and active power for feature extraction. To recognize power consumption categories, their algorithm employs Siamese neural networks to compute the V-I track similarities before comparing signatures in the feature library. The findings reveal that the suggested approach performs better than utilizing the raw power data alone. High-frequency data can be collected in residential electricity scenarios. [PERSON] et al. [25] proposed an improved load identification method with the help of current harmonic characteristics of electrical loads. An intelligent identification method based on fuzzy rules detects and monitors a wide range of electrical loads. The proposed method enhances the accuracy of identifying several devices by considering the load characteristics on the power side. After determining the electrical parameters, feature types also need to be selected [26; 27; 28]. [PERSON] et al. [29] suggested a recognition approach that combines time-frequency features. First, time and frequency domain features are extracted to construct fused features, then a random forest algorithm is employed to identify a significant subset of features, and finally, SVM is utilized as experiment classifier. To assess feasibility, a case study was performed using an authentic Irish dataset. The findingsdemonstrated that this method performs better with the introduction of frequency domain features. [PERSON] et al. [30] put forward a feature selection approach founded on fuzzy entropy values and similar classifiers. The proposed method is effective in reducing noise. The results on the experimental dataset show that the classification accuracy is improved, and the computation time is reduced. Metaheuristic algorithms are a class of problem-independent optimization techniques that explore and develop the entire search space based on the principle of multiple iterations [31; 32]. EO algorithms, as metaheuristics, are derived from the dynamic mass equilibrium model of the control container and have been used in several fields [33]. [PERSON] et al. [34] introduced the equilibrium optimizer algorithm in 2020. In order to demonstrate the superiority of the EO, metaheuristic algorithms of various types and periods of development were selected for comparison, and 58 mathematical benchmark functions and three engineering problems were experimentally examined. In terms of performance, the algorithm obtains optimal results and has the utmost efficacy across a wide range of problems, outperforming traditional and newly developed algorithms. [PERSON] et al. [35] combined EO and chaos theory and performed experiments in the UCI machine learning library. The findings indicate that the algorithm can identify the optimal subset of features and acquire the best classification outcomes while selecting the least number of features. Considering the dimensional disaster generated by many features, [PERSON] et al. [36] proposed an EO-based variant of the algorithm, which is considered a wrapper-based approach for feature selection. There were sixteen datasets used to validate the effectiveness. The results show that the algorithm performs well regarding fitness value, accuracy, and feature size compared to other metaheuristics.