Scalable Data Lake Architecture with Apache Spark for Predictive Maintenance and Optimization in Energy Storage Systems

Abstract

The fast development of energy storage power stations in current smart grids has resulted in massive and complicated datasets that require effective management and analysis to maintain battery performance, optimize maintenance schedules, and reduce energy expenditures. Existing centralized systems suffer with scalability and latency, resulting in delayed analysis, lower prediction accuracy, and ineffective pricing schemes. This study presents DCDL-ESM (Distributed Computing over Data Lake for Energy Storage Management), a scalable architecture that combines a centralized data lake and an Apache Spark-based distributed computing framework. The system collects raw data from 100 energy storage power plants (a total of 2.8 TB over 12 months), performs distributed preprocessing, data purification, normalization, and trend analysis, and uses predictive modeling for maintenance scheduling and charge optimization. Validation was carried out using historical operational datasets (80% training, 20% testing split), resulting in a 42% reduction in processing time, 91.3% predictive maintenance accuracy, 24.7% energy cost savings, 38% improvement in CPU and memory utilization, and 31% storage efficiency gains through partitioning and compression. Results were tested for robustness using cross-validation and bias analysis. The suggested solution closes scalability and performance gaps while setting the framework for future integration of real-time analytics and edge computing to minimize latency and support autonomous control.