Big Data and Data Mining for Efficient Energy Storage and Management - Mustafa Nazar; Zaid Ghanim Ali; Kahtan Mohammed Adnan; Ibraheem Mohammed Khalil; Waleed Nassar; Siti Sarah Maidin (Malikussaleh University, Aceh, Indonesia, 2025)
The rapid expansion of decentralized and renewable energy systems necessitates intelligent strategies for energy storage and management. This paper presents a comprehensive framework that leverages big data analytics and data mining to optimize energy storage systems within smart grid architectures. By integrating high-frequency data from IoT-enabled Li-Ion batteries, flow batteries, supercapacitor arrays, and hybrid systems, our methodology enhances storage efficiency, predictive accuracy, and fault detection. The approach uniquely combines an ensemble forecasting model (Random Forest and XGBoost), which achieved a 97% R² score in predicting energy demand, with Gaussian Mixture Models for consumer pattern clustering and canonical correlation analysis to model the impact of environmental variables. Validation on real-world datasets demonstrates significant performance gains without additional hardware. For instance, algorithmic optimization improved the round-trip efficiency of a Hybrid Battery Energy Storage System from 86.7% to 93.3% and a Li-Ion battery by 7%. The study underscores the critical influence of contextual variables like temperature and humidity on state-of-charge stability. Furthermore, the analytical framework demonstrated a 50% increase in system throughput (from 34 to 51 tasks/sec) after optimization. This research provides a replicable, data-driven model for deploying intelligent analytics in both microgrid and industrial-scale settings, paving the way for more adaptive and resilient energy infrastructures. Future work will explore edge computing and reinforcement learning to further enhance scalability and autonomy.