Impact of Big Data Analytics on Smart Meter Data Management in Power Distribution
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Abstract
The integration of big data analytics into power distribution systems has become increasingly crucial, particularly with the advent of smart meters. This research investigates the impact of big data analytics on managing smart meter data, with a specific focus on low-latency data handling for rapid decision-making within financial systems. As smart meters generate vast amounts of data, traditional data management practices often fall short in providing the real-time insights needed for effective operational strategies. This study employs a mixed-methods approach, combining quantitative data analysis and qualitative insights from industry practitioners. The findings reveal significant improvements in both data processing times and decision-making speeds following the implementation of big data analytics solutions. Specifically, the research shows that the use of advanced analytics tools reduces the average decision-making time by over 50%, thereby enhancing operational efficiency and customer satisfaction. Additionally, the study highlights key challenges faced by utilities in adapting to these technologies and offers practical recommendations for overcoming these hurdles. By addressing the intersection of big data analytics and smart meter data management, this research contributes valuable insights for both academic and practical applications in the energy sector. The implications of this study extend beyond technical considerations, as they also address strategic management and policy decisions necessary for leveraging big data analytics effectively. This research serves as a foundational exploration into how utilities can harness the power of big data analytics to optimize resource allocation, improve customer service, and ultimately drive business growth. The study concludes with recommendations for future research directions, emphasizing the need for continued investigation into the ethical considerations and regulatory frameworks surrounding the use of big data in the energy sector.
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References
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