MILOF-TCN: A Hierarchical Edge–Fog Framework for Monitoring Abnormal and Missing Patterns in Electric Vehicle Charging Data

The rapid growth of electric vehicle (EV) charging infrastructures has introduced new challenges in monitoring abnormal load behaviors under strict latency and resource constraints. Conventional anomaly detection approaches either rely on centralized processing or incur excessive false alarms, limiting their practical applicability in large-scale deployments. This paper proposes a hierarchical edge-fog anomaly detection framework that integrates lightweight edge-level filtering with a fog-level Temporal Convolutional Network (TCN) detector. The edge component suppresses non-informative patterns, while the fog layer performs temporal modeling on selectively forwarded data. This design enables controllable reduction of fog-level processing load. Under corrected end-to-end evaluation on real-world EV charging load data, the hierarchical pipeline should be interpreted as a system operating point rather than a uniformly superior detector. Relative to fog-only TCN-AE inference, the selected routing policy reduces fog workload by 34.9% and shortens average detection delay from 93.6 to 75.6 h, but increases false alarms per day from 0.88 to 7.29 and lowers F1 from 0.547 to 0.455. Sensitivity experiments over routing thresholds reveal a consistent trade-off among fog workload, alert burden, detection delay, and retained anomaly evidence. Additional routing diagnostics show that the primary source of performance degradation is information loss induced by filtering, rather than weakness of the fog detector on the forwarded subset. These findings suggest that hierarchical edge intelligence is a practical but calibration-sensitive direction for scalable anomaly monitoring in EV charging infrastructures.