RES‐AD: An AutoML‐Driven Robust Ensemble Framework for Real‐Time Anomaly Detection and Energy Forecasting in Smart Grids

ABSTRACT

Accurate load forecasting and reliable anomaly detection are critical for the stable operation of modern smart grids (SGs), which increasingly rely on cyber‐connected infrastructures. However, the integration of smart metres and two‐way communication exposes SGs to data integrity attacks that can manipulate consumption measurements, degrade forecast accuracy and compromise grid stability. This study proposes RES‐AD, a robust AutoML‐driven ensemble framework designed for real‐time detection of abnormal consumption patterns and accurate short‐term energy forecasting. RES‐AD consists of an Optimal Ensemble Classifier (OEC) for anomaly detection and an Optimal Ensemble Regressor (OER) for forecasting, both selected and optimised automatically using H2O AutoML. Five types of synthetic data integrity attacks are injected to simulate realistic adversarial conditions, whereas SMOTE and correlation‐based feature selection ensure balanced and informative training data. The revised framework further incorporates stealthy adversarial attack simulation evaluated using FGSM and demonstrates strong robustness with only a 1.7% degradation under perturbation, further mitigated through adversarial augmentation. Additionally, real‐time readiness is validated through latency and memory profiling, where the OEC ensemble requires only 23.36 ms per inference with a footprint below 0.05 MB, making it suitable for edge or SCADA‐integrated deployment. Experimental results using public residential smart metre data show that OEC achieves 99.7% (hourly) and 99.9% (daily) detection accuracy, whereas OER improves energy forecasting with a MAPE reduction of 11.11% and 11.72%, respectively. These outcomes demonstrate that RES‐AD provides a scalable, attack‐aware and deployment‐ready solution that enhances both the reliability and resilience of modern smart grid operations.