ISODATA-Based Clustering and Profiling of EV Charging Load Behaviors

With the rapid increase in electric vehicle (EV) ownership, charging loads have an increasingly significant impact on the operation of urban distribution networks. Due to the high randomness, volatility, and heterogeneity of EV charging behavior, traditional load modeling methods struggle to fully capture its characteristics. To address this issue, this paper proposes an EV charging load behavior analysis method based on the integration of ISODATA clustering and feature selection. First, to overcome the limitation of requiring a predefined number of clusters in traditional K-means, an adaptive Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm is introduced to automatically identify typical charging behavior patterns. Then, a multi-dimensional feature indicator system is constructed, and the Spearman correlation coefficient method is employed to select the optimal subset of features. Finally, quantitative user profiling is performed based on the selected features, and visualized using radar charts. Case studies demonstrate that the proposed method effectively improves clustering accuracy, feature representativeness, and profile interpretability, providing a valuable reference for EV load modeling, classification forecasting, and differentiated control strategies.