# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Research Discussion  
This study proposes a comprehensive evaluation system for line loss based on a knowledge graph, which integrates kernel principal component analysis (KPCA) with the DEMATEL-Entropy Weight-TOPSIS combined evaluation method through a knowledge graph. In comparison to traditional deep learning models, this model integrates domain expert knowledge with statistical analysis techniques, thereby demonstrating notable advantages, particularly in the context of nonlinear and high-dimensional data. Despite their efficacy in feature extraction and classification, deep learning models have inherent limitations in terms of model interpretability, as evidenced by the literature [20]. In contrast, this study enhances both the prediction accuracy and the interpretability of the results by incorporating a knowledge graph. As discussed in literature [22; 23] regarding hybrid systems, this study also employs a mixed methodology, underscoring the significance of knowledge graphs in integrating multi-source data and reducing data redundancy. In comparison to the hybrid models based on fuzzy logic and rough sets referenced in literature [24; 25], this model utilizes more sophisticated techniques, such as the Gaussian kernel function, to achieve enhanced performance in power system data analysis. Concurrently, this model may encounter difficulties in terms of computational resources when dealing with large-scale and intricate power grid systems, a challenge analogous to that identified in literature [26]. The subsequent introduction of algorithm optimization and efficient data processing technologies can effectively mitigate this issue. In comparison to the models discussed in literature [27; 28], this model may exhibit certain limitations with regard to real-time data processing and dynamic environmental adaptability. Further research will investigate the incorporation of more sophisticated machine learning techniques to augment the model's capacity for real-time performance and adaptability. From a more profound perspective, the comprehensive evaluation system for line loss based on a knowledge graph strives to furnish a comprehensive array of line loss management solutions for power grid operators, policymakers, and engineers. Nevertheless, in practical applications, it is incumbent upon different customers to ensure the practical application of the proposed system based on clear and feasible recommendations. 1. It is recommended that operators integrate existing power grid data resources, including equipment operation data, user electricity usage data, and geographical information data, and establish unified data standards to facilitate the construction and application of knowledge graphs. It is recommended that knowledge graphs and real-time data processing technologies be employed to establish dynamic monitoring systems, which will enable the real-time analysis of the operational status of the power grid and the timely detection and response to line loss anomalies. The development of predictive maintenance models based on knowledge graphs and machine learning technologies will facilitate the prediction of equipment failures and line loss trends, thereby reducing the incidence of unexpected power outages and associated maintenance costs. * It is recommended that policymakers formulate relevant policies to encourage power grid companies to adopt advanced line loss management technologies, such as knowledge graphs and artificial intelligence, with the aim of improving the operational efficiency and reliability of the power grid. It is recommended that cooperation between the power industry and other relevant industries, such as information technology and data analysis, be promoted in order to facilitate joint development and application of innovative power grid management solutions. * It is recommended that engineers receive training in the construction of knowledge graphs, data analysis, and the applications of artificial intelligence, in order to enhance their ability to apply new technologies.