# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 1 Introduction
The dataset pertaining to the power grid is both extensive and intricate, rendering a straightforward comparison of data insufficient for demonstrating the rationality of the indicators. Consequently, it is imperative to employ mathematical and statistical techniques for the analysis of data, thereby reinforcing the conclusions presented in this paper. The three papers in the literature [10; 11; 12] provide further insight through NVW random comparison combination, combined evaluation, or model combination, thereby assisting decision-makers in making more informed decisions. Furthermore, by integrating multiple evaluation indicators and methods, they enhance the model's resilience under diverse conditions and uncertainties. This study introduces a line loss feature indicator screening method based on kernel principal component analysis (KPCA) to address the inherent uncertainty and diversity in power grid indicators. This method is designed to address the inherent complexity and nonlinear relationships present in the data. By mapping the line loss features from the original low-dimensional state space to a higher-dimensional space, it is possible to more effectively reveal the intrinsic connections between feature quantities. Moreover, this study puts forth a composite evaluation theory that is well-suited to line loss analysis. This paper presents a set of power grid evaluation indicators, constructed based on the DEMATEL-entropy weight-TOPSIS composite evaluation method. This method employs a comprehensive assignment approach, integrating subjective and objective factors. The system is designed to address the current challenges in power grid evaluation, where the available evaluation knowledge is rapidly expanding but remains heavily reliant on traditional storage formats, such as text. Additionally, the standardization, publicization, and intelligence levels of comprehensive evaluation work are inadequate, which constrains the ability of workers to swiftly master and apply evaluation results. In conclusion, this study presents a comprehensive evaluation system for line loss based on a knowledge graph. This system not only improves the scientific and accurate evaluation of the power grid but also enhances the efficiency of evaluation work through intelligent methods, thereby providing robust decision support for the sustainable development of the power grid. This study makes the following contributions to the field:  
* Construct a knowledge graph for the comprehensive evaluation and management of line loss. In light of the current disarray surrounding line loss indicators and the dearth of multi-dimensional indicators, a heterogeneous knowledge map pertaining to line loss has been constructed. By means of knowledge mining in the knowledge graph, the interconnections between disparate bodies of knowledge are identified, thus facilitating a comprehensive evaluation and management of line loss. By mining knowledge within the comprehensive evaluation and management knowledge graph for line loss, the heterogeneous knowledge pertaining to line loss is structured and the associations between knowledge are established. The graph describes and classifies line loss characteristics through the use of special attributes, thereby addressing the issue of the chaotic screening of line loss characteristics and facilitating the selection and business application of line loss characteristics. * Index screening based on principal component analysis. In the process of index extraction, this paper employs the method of principal component screening, which reduces the correlation between indicators through dimensionality reduction, reduces the number of indicators to be analyzed, while ensuring that as much as possible of the original indicator information is retained, thus enabling a comprehensive analysis of the screened indicator data. * A synthesis of multiple evaluation methods. In the evaluation, a combination evaluation theory is proposed that is suitable for the characteristics of line loss analysis.