Automated Anomaly Detection and Predictive Maintenance in Cloud-Based ESS Management Platforms Utilizing Dynamic Bayesian Networks and Federated Learning

**Abstract:** This research proposes an automated anomaly detection and predictive maintenance framework for cloud-based Energy Storage System (ESS) management platforms. Unlike traditional rule-based monitoring, our system incorporates Dynamic Bayesian Networks (DBNs) trained through federated learning to capture temporal dependencies and distributed data characteristics of ESS deployments. This approach allows for real-time anomaly identification, proactive component failure prediction, and optimized maintenance scheduling, leading to significant cost reductions and improved grid stability. The dynamism of the DBNs allows adaptation to varying operational conditions and system degradation, while federated learning preserves data privacy across multiple ESS installations. This provides a commercially viable solution addressing the growing need for autonomous ESS management.