| Summary: | The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for power systems, particularly in understanding and forecasting charging behavior. This paper presents a two-stage framework to segment and forecast electric-vehicle (EV) charging behavior using spatio-temporal features extracted from one year of 15-minute telematics for 264 EVs in Saskatchewan, Canada. The feature set combines temporal usage (power/energy levels and variability) with spatial mobility (home-charging ratio, location diversity, travel distance and angular dispersion). We compare three complementary clustering families including Fuzzy C-Means (soft membership), Gaussian Mixtures (probabilistic partitions), and Hierarchical clustering (multiscale structure) and using algorithm-appropriate selection criteria (Xie–Beni, BIC, elbow/Calinski–Harabasz) to ensure a fair assessment. The framework identifies five behaviorally meaningful groups ranging from home-dominant commuters to high-mobility, DC fast-charging users. Hierarchical clustering provides the most balanced and interpretable segmentation (lowest Davies–Bouldin; highest Calinski–Harabasz), while GMM attains the highest Silhouette (0.585) with coarser partitions. In the supervised stage, ensemble classifiers forecast user-segment membership from features, with XGBoost achieving 98.1% accuracy. The outputs translate directly into planning inputs: expected residential feeder peaks for home-centric segments, DC fast-charging demand for high-mobility users, and demand-response potential for managed charging. The results indicate that spatio-temporal features coupled with hierarchical segmentation and gradient boosting provide actionable and scalable inputs for siting, tariff design, and grid operations.
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