Unveiling the Power of Granular Data: Enhancing Holistic Analysis in Utility Management

Authors

  • Satyaveda Somepalli, Dayakar Siramgari

Keywords:

granular data, utility management, real-time analytics, smart grids, artificial intelligence, machine learning, predictive maintenance, energy consumption, data privacy, resource optimization, advanced metering infrastructure.

Abstract

Granular data plays a critical role in modern utility management by providing detailed, actionable insights that drive operational efficiency, enhance decision making, and improve customer engagement. This paper explores the significance of granular data in the utility sector, examines how it enables real-time analytics, supports predictive maintenance, and facilitates resource optimization. Through a review of current trends and technologies, this study highlights the transformative potential of granular data, particularly in the context of smart grids, advanced metering infrastructure, and AI-driven analytics. As the utility sector increasingly embraces data-driven decision making, the ability to leverage granular insights will be essential in shaping the future of energy management and ensuring more sustainable and efficient resource use.

Downloads

Download data is not yet available.

References

Dyson, M. E. H., Borgeson, S. D., Tabone, M. D., & Callaway, D. S. (2014). Using smart meter data to estimate demand response potential, with application to solar energy integration. Energy Policy, 73, 607–619. https://doi.org/10.1016/j.enpol.2014.05.053

Jain, R. K., Qin, J., & Rajagopal, R. (2017). Data-driven planning of distributed energy resources amidst socio-technical complexities. Nature Energy, 2(8), 1-11.

Julio Romero Aguero, Amin Khodaei, & Masiello, R. (2016). The Utility and Grid of the Future: Challenges, Needs, and Trends. IEEE Power and Energy Magazine, 14(5), 29–37. https://doi.org/10.1109/mpe.2016.2577899

Kumar, V. S., Dhillipan, J., & Shanmugam, D. B. (2017). Survey of recent research in granular computing. Int. J. Emerg. Technol. Comput. Sci. Electron, 24(3), 976-1353.

Lu, N., Du, P., Guo, X., & Greitzer, F. L. (2012). Smart meter data analysis. https://doi.org/10.1109/tdc.2012.6281612

S. Massoud Amin, & Wollenberg, B. F. (2005). Toward a smart grid: power delivery for the 21st century. IEEE Power and Energy Magazine, 3(5), 34–41. https://doi.org/10.1109/mpae.2005.1507024

Inmon, W. H., & Linstedt, D. (2015). Data Marts. Data Architecture: A Primer for the Data Scientist, 115–119. https://doi.org/10.1016/b978-0-12-802044-9.00018-0

Tudor, V., Almgren, M., & Papatriantafilou, M. (2013, November). Analysis of the impact of data granularity on privacy for the smart grid. In Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society (pp. 61-70).

Downloads

Published

30.12.2020

How to Cite

Satyaveda Somepalli. (2020). Unveiling the Power of Granular Data: Enhancing Holistic Analysis in Utility Management. International Journal of Intelligent Systems and Applications in Engineering, 8(4), 284–289. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7107

Issue

Section

Research Article