# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Data Analysis and Experimental Platform
Among the methods presented, Tables S4 and S5 of the Supplementary Materials DEMATEL-TOPSIS evaluation method employs the DEMATEL approach to ascertain the direct correlation between indicators. Subsequently, the TOPSIS method is utilized for the ultimate evaluation, disregarding the weight distribution determined by the entropy weight method. As can be observed from the weighted normalized matrix, the weights of certain indicators (such as C4 and C12) may be elevated, indicating that these indicators exert a greater influence within the evaluation model and contribute to the final evaluation results to a greater extent. The Entropy-TOPSIS evaluation method detailed in Tables S6 and S7 of the Supplementary Materials represents a contrasting approach to that described in the preceding section. This method employs the entropy weighting approach solely for determining the weight of the indicators, subsequently evaluating them using the TOPSIS method, eschewing the DEMATEL method. Both methods construct corresponding weighted and normalized matrices and are applied to the same data set of power losses. The matrices illustrate the distribution of weights for each indicator across the various methods. A comparison of the evaluation results obtained from the three methods allows for the observation of the specific impact of each component on the accuracy of the evaluation. The results of the ablation experiments serve to further confirm the effectiveness of the DEMATEL method in elucidating the intricate relationships between indicators, as well as the role of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target** & **Impact f.** & **Affected e.** & **Center Cloud** & **Reason y.** & **Factor Attributes** \\ \hline Main variable capacity-to-load ratioU11 & 6.6453 & 5.7049 & 12.3502 & 0.9404 & Cause factors \\ The pass rate of reactive power configuration of substationU12 & 6.3282 & 7.7028 & 14.0310 & -1.3746 & Result factors \\ Unplanned outage rate of distribution linesU13 & 4.9426 & 4.8259 & 9.7685 & 0.1167 & Cause factors \\ Public change outage rateU14 & 7.1778 & 7.6720 & 14.8498 & \(-\)0.4942 & Cause factors \\ The proportion of old low-voltage energy meters/21 & 7.5627 & 8.0082 & 15.5709 & -0.4455 & Result factors \\ The proportion of automatic meter readingU22 & 7.1885 & 8.1637 & 15.3522 & \(-\)0.9752 & Result factors \\ Data acquisition integrity rate of four types of terminalsU23 & 7.4075 & 7.5046 & 14.9121 & \(-\)0.0972 & Cause factors \\ The rate of emergency repair orders for hundreds of householdsU24 & 6.4469 & 7.1053 & 13.5522 & \(-\)0.6585 & Cause factors \\ Electricity sales growth rateU25 & 7.1007 & 6.8508 & 13.9515 & 0.2499 & Result factors \\ The success rate of automatic execution of fee controlU26 & 6.6915 & 5.7572 & 11.8647 & 0.3184 & Result factors \\ Power factor pass rateU31 & 6.4481 & 5.1844 & 11.6325 & 1.2636 & Result factors \\ Availability rate of substation reactive power compensation deviceU41 & 6.2500 & 4.7670 & 11.0170 & 1.4830 & Result factors \\ Energy-saving main variable ratioU2 & 7.7616 & 8.0884 & 15.8500 & \(-\)0.3269 & Cause factors \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influencing factors of fuzzy DEMATEL analysis.