Big data analytics for efficient energy management systems

Edi Leksono, Justin Pradipta

Abstract


This paper explores the application of big data analytics in energy management systems. Traditional systems face challenges in data availability and real-time processing. Big data analytics addresses these issues by leveraging advanced techniques such as data fusion, cleansing, cloud-based architectures, and distributed databases. It also applies data mining, predictive modeling, and optimization algorithms for energy forecasting, anomaly detection, and demand response optimization. The benefits include improved energy efficiency, cost reduction, and overall system performance. Challenges like data privacy, scalability, and standardization are discussed. Future research areas include integrating renewable energy sources, developing intelligent algorithms, and incorporating real-time data analytics for dynamic energy management. Big data analytics holds promise in transforming energy management, enabling informed decisions and contributing to sustainability.

References


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