Machine Learning Applications in Predictive Maintenance of Energy Storage Facilities

Abstract

The increasing reliance on energy storage facilities to balance renewable energy integration and grid stability necessitates robust maintenance strategies. Predictive maintenance (PdM), powered by machine learning (ML), has emerged as a transformative approach to minimize unplanned downtime, reduce operational costs, and enhance the lifespan of energy storage systems. This manuscript explores the intersection of ML techniques and predictive maintenance for energy storage facilities, detailing their applications, methodologies, and results from recent case studies. The study demonstrates how ML models, leveraging sensor data and advanced analytics, provide actionable insights for system maintenance. This research underscores the potential of ML to revolutionize predictive maintenance, paving the way for resilient energy storage systems.