Exploring the Role of Data Analytics in Smart Grid Resource Management

  • Authors

    • Dr. Karen Lewis Associate Professor, Ohio State University, USA Author
    • Dr. Rebecca Green Associate Professor, Arizona State University, USA Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2019PII7M9C

    Published 07-05-2019

  • Smart Grid, Data Analytics, Resource Management, Predictive Analytics, Machine Learning, Optimization Algorithms, Energy Distribution, Demand Response, Fault Detection, Big Data, Energy Efficiency, Renewable Energy Integration

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    K. Lewis and R. Green, “Exploring the Role of Data Analytics in Smart Grid Resource Management”, IJETMR, vol. 2, no. 2, pp. 01–15, Jul. 2019, doi: 10.67228/30715636/IJETMR-2019PII7M9C.

  • Abstract

    The efficient management of resources within smart grids is essential for ensuring sustainable, reliable, and cost-effective energy distribution. Data analytics plays a pivotal role in optimizing resource management in these grids by leveraging real-time data and advanced computational techniques. This paper explores the significance of data analytics in improving the operational efficiency, reliability, and sustainability of smart grids. It examines the use of various data analytics techniques, including machine learning, predictive analytics, and optimization algorithms, to forecast energy demand, detect faults, and manage resources effectively. Additionally, the paper discusses the benefits, challenges, and limitations of applying data analytics in smart grid systems, along with case studies that demonstrate successful applications. As smart grid technologies evolve, data analytics will continue to be at the forefront of driving innovation in resource management, paving the way for smarter, greener, and more resilient energy systems

  • References

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