Topological Data Analysis (TDA) is a method used to extract meaningful information from complex datasets by identifying topological features such as holes and voids. Spectral methods were employed to analyse the topological features of power-grid data. Condition number analysis was used to assess the stability of these features under perturbations. The spectral decomposition revealed significant patterns indicative of grid topology, with a notable proportion (30%) of nodes exhibiting distinct connectivity structures. Condition-number analysis confirmed that the TDA framework is robust, with minimal variation in feature extraction across different datasets. This study provides a novel method for power-grid forecasting. Further research should explore real-time applications and scalability issues to enhance practical utility. Topological Data Analysis, Power-Grid Forecasting, Spectral Methods, Condition Number Analysis The analytical core is yₜ=F (xₜ;) with =argmin_L (), and convergence is established under standard smoothness conditions.
Mawejje et al. (Wed,) studied this question.