OCPP integrated artificial intelligence for forecasting scheduling and anomaly detection in city scale electric...

Urban EV uptake is raising feeder peaks and energy costs in city networks. We present an AI framework integrated with the open charge point protocol (OCPP), a standard communication protocol for EV charging systems that combines short-horizon demand forecasting with tariff-aware and fairness-aware scheduling and runtime anomaly detection from charger telemetry. Decisions are encoded with OCPP 1.6 and 2.0.1 operations including SetChargingProfile, ClearChargingProfile, GetCompositeSchedule, RemoteStartTransaction and RemoteStopTransaction. The system runs in rolling 15-minute control on commodity hardware. Using a two-year multi-station tariff-aware urban dataset with 1,553 operational days and 455 days with tariff coverage, the scheduler delivers average reductions of 5.0% for GA and 8.2% for a hybrid scheduling approach combining genetic algorithms (GA) and Q-learning (a reinforcement learning method) in both feeder peak and charging cost relative to the baseline. The policy conserves daily energy and applies a fairness mechanism that limits excessive delays for individual users limiting the concentration of deferrals. Under fixed caps and posted tariffs, the pipeline is deterministic, so day-level confidence intervals collapse to a point, and small parameter jitter leaves the mean unchanged within measurement noise. For anomaly detection, a CNN reaches ROC AUC 0.914 and the Autoencoder and Isolation Forest reach 0.735 and 0.636. A latency budget covering ingestion, forecasting, scheduling and OCPP round trip confirms near real-time feasibility with optimization overhead below 0.1 s. We provide a minimal reproducibility package with code, data splits, and scripts. The results show that standards-compliant and OCPP-aware AI can deliver measurable grid and cost benefits at the city scale and offer a practical path for sustainable and equitable EV charging in smart city contexts.