An Explainable Deep Learning Framework with Multi-Head Attention and SHAP for Power Stability Monitoring in IoE-Enabled Smart Cities

The growing reliance of Internet of Energy (IoE)-enabled smart city infrastructures has significantly intensified the complexity of modern urban energy environments due to the integration of renewable resources, electric vehicles, and interconnected IoT devices. These highly dynamic environments introduce critical challenges related to power stability, operational reliability, and intelligent energy management. Therefore, developing accurate, adaptive, and explainable monitoring frameworks has become essential for ensuring resilient urban energy infrastructures. This paper proposes an Explainable Artificial Intelligence (XAI)-driven deep learning framework integrating Multi-Head Attention and SHapley Additive exPlanations (SHAP) for intelligent power stability monitoring in IoE-enabled smart cities. The proposed framework employs an attention-based deep learning architecture to classify stable and unstable operational conditions using multivariate operational power parameters. Furthermore, SHAP-based explainability analysis is incorporated to improve model transparency and identify influential operational factors affecting stability behavior. Using the Electrical Grid Stability Simulated Dataset, the proposed framework achieved 97.35% accuracy and 0.997 ROC-AUC, outperforming several traditional machine learning and deep learning baselines. The explainability results show that temporal response parameters have the strongest impact on stability decisions. This work offers not only high predictive performance but also valuable interpretability, which is essential for practical deployment in real-world smart city energy systems.