## 5 Conclusions  
Electric load identification in industrial power consumption scenarios can optimize power consumption structure and improve power consumption efficiency, which is a prerequisite for intelligent energy management. The sampling environment of the industrial electricity consumption scenario is harsh, and the sample quality is poor. Therefore, how useful features are extracted from the low-frequency data and appropriate feature combinations are screened out from the joint feature set determines the effectiveness of equipment identification. To solve this problem, this paper proposed an EO-based feature selection method to screen the time-frequency entropy joint feature set in industrial electricity scenarios. This study offers the following key contributions:  
1. [label=0., ref=]
2. Solved the problem of power load identification using low-frequency data in industrial power scenarios. The experiments used active and reactive power signals with a sampling frequency of only 1/300 Hz to accurately classify nine typical industrial equipment types. 3. Introduced entropy value features on the basis of time-frequency domain features and constructing joint feature sets. Taking SVM as an example, compared with the original power data classification effect, the extraction of entropy value features improved accuracy by 61.31%. At the same time, the computation time for the model was reduced to 0.1353 s. The construction of the joint feature set further improved the classification accuracy of power equipment to 89.35%, in which the entropy value features contributed the most to the entire feature set. 4. Used EO to screen the joint features of electric loads to obtain the best feature subset. The EO reduced the joint feature dimensionality from 72 to 21 dimensions. In power load identification, the 21 features screened by the EO improved the classification accuracy of devices to a maximum of 95.58% and reduced the time cost to a minimum of 0.045 s. Thus, the EO balanced the relevance and redundancy of feature screening and achieved the goal of filtering out invalid data while ensuring information integrity. In summary, in the problem of electric load identification in industrial power consumption scenarios, this paper was very effective in introducing entropy value features after extracting the time-frequency domain and using an EO to screen the joint features, which provides a basis for subsequent industrial energy optimization and intelligent energy management. In future research, the research process of this paper can be used as a template for task implementation, and the problems of long training times, memory limitations, and low data quality can be solved by combining multiple features with novel and effective feature selection methods to cope with the redundancy caused by massive data in general application scenarios. There were some limitations and areas for improvement in the current research content of this paper. Firstly, in terms of data types, there were limitations and differences in different feature extraction means for various types of signals and datasets. The quality of time and frequency domain features extracted from industrial power data in this paper must be improved. How to extract suitable features for data types will be the focus of the authors' work in the future. Secondly, in terms of application scenarios, the identification scenario in this paper was industrial, while commercial electricity consumption has also been greatly enhanced in recent years, so the results of this paper may have some limitations. To solve this problem, the authors will conduct joint research on multiple scenarios in the future. Conceptualization, M.Z. and X.Y. ; Methodology, X.Y. ; Software, X.Y. ; Validation, X.Y. ; Writing--original draft, X.Y. ; Writing--review & editing, X.Y. ; Project administration, Z.Z. and F.H. All authors have read and agreed to the published version of the manuscript.