Calculation method of line loss in low-voltage substation based on K-Means++ and Elman neural network

This paper introduces a method for calculating line loss that leverages K-Means++ clustering and an Elman Neural Network (ENN), with the goal of enhancing energy efficiency and lowering operational expenses. Initially, we conduct a thorough analysis to identify key factors influencing line loss, leading to the development of a set of characteristic indices encompassing line parameters, load characteristics, environmental conditions, equipment status, and operational management. The Pearson correlation coefficient is employed to assess the relationship between these characteristic indices and line loss, while Principal Component Analysis (PCA) is applied to diminish data dimensions, thereby streamlining the data structure and eliminating superfluous information. Subsequently, an enhanced K-Means++ clustering algorithm is utilized to efficiently categorize the dataset and refine the model training procedure. Building on this, the ENN is employed to learn from and predict the clustered data, enabling precise line loss calculations in low-voltage station areas. Experimental outcomes indicate that the ENN model, when optimized with a combination of K-Means++ clustering and the Particle Swarm Optimization (PSO) algorithm, outperforms traditional methods in terms of both training efficiency and prediction accuracy. This approach demonstrates a robust capability in handling intricate data structures and enhancing the model's predictive power. Consequently, it offers substantial support for the detailed management of line losses in low-voltage environments and serves as a valuable reference for related studies.