# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Line Loss Indicator Model Based on Principal Component Analysis
In light of the aforementioned analysis, it can be posited that extracting the initial 13 factors is an appropriate course of action for the multiple detection indicators of line loss. This approach ensures an adequate explanation of the original variables while avoiding the introduction of superfluous factors, thereby rendering the analysis more concise and efficient. The eigenvalues and contribution rates of the KPCA at the optimal kernel parameter are presented in Table 1 for reference. The thirteen indicators for evaluating power loss management, together with their corresponding parameters, can be found in Supplementary Materials File S1. The equivalent parameters for the metrics enumerated in Table S1 of the Supplementary Materials correspond to the various indicators for power loss management. The equivalent parameters, which are abbreviated as indicators in the subsequent Supplementary Materials, serve two purposes. Firstly, they provide a set of standardized evaluation tools for power loss management. Secondly, they allow these parameters to be incorporated into a comprehensive evaluation model. The data types included in Supplementary Materials Table S2 encompass equipment, operational, capacity, and topology data. These data types not only provide a rich source of information, but also serve to establish a robust foundation for the weight analysis in the loss management assessment. As illustrated in the parallel coordinate charts in Figures S1 and S2 of the Supplementary Materials, the discrepancies between the data points in multiple dimensions are readily apparent. Nevertheless, the considerable volume of data precludes the direct generation of indicator weights and comprehensive evaluations. It is therefore necessary to undertake further analysis of the data in  
Figure 6: Impact diagram of kernel principal components. order to determine the specific weight of each indicator in the comprehensive evaluation, with a view to more accurately reflecting the degree of influence of each indicator on the efficiency of power loss management. The cumulative variance contribution rate of the 13 principal components reaches 71.211%, representing a proportion exceeding 71.211% of the characteristic information of the line loss indicators. The line loss influencing factors identified through kernel principal component analysis (KPCA) can be classified into four principal categories. Planning dimension indicators account for 37.470% of the characteristic information, management dimension indicators account for 32.762%, operational dimension indicators account for 14.286%, and technical dimension indicators account for 1.482%, for a total of 13 indicators. The feature extraction of line loss based on KPCA retains the features that contribute the most to the variance in the dataset. The replacement of 37 indicators with 13 has the effect of greatly reducing the computational load, which is a scientific evaluation of the objective phenomenon.