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Studies on Conventional and Advanced Machine Learning Algorithm Towards Framing of Robust Data Analytics for the Smart Grid Application

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Data Analytics for Smart Grids Applications—A Key to Smart City Development

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 247))

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Abstract

Data analytics has recently become very promising area for data management like storing, analysis and transferring of data in real time scenario and maintaining overall security of concerned data and information. Smart grid is the next generation power system which is efficiently used for pursuing robust analytics of data with the help of electrical sensor and other smart meters connected with conventional communication of electricity and distribution networks. The advent of machine learning and IOT has contributed a lot to the data-oriented mechanism. Multidisciplinary nature of data with the myriad dynamic features can be the candidate for advanced data analytics. The presence of huge volume of data can prompt to the implementation of advanced data analytics in smart grids. Inclusion of optimization concepts to the data grid performs drastic reduction of the data transfusion and in shortest possible time increasing the results of overall distribution and consumption of data. The advent of AI, machine learning and IOT technology rejuvenated the grid architecture by increasing potential performance of the structure leading to the far efficient outcomes outweighing the performances of other traditional grid architectures. The big data acquisition strategies, load efficiency prediction and maintenance of constant cost effectiveness have become the three chief contributions as a consequence of progress in the artificial intelligence and machine learning paradigms. The main objective of this review paper is to highlight in depth exploration of grid structure and its advancement towards the updated data analytics for fast, secured and efficient computing and distribution in other station.

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Authors and Affiliations

  1. Department of Computational Sciences, Brainware University, Kolkata, WB, India

    Gunjan Mukherjee, Sandip Roy, Sayak Konar & Rajesh Bose

  2. Institute for Manufacturing, University of Cambridge, Cambridge, UK

    Anandarup Mukherjee

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  1. Gunjan Mukherjee
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  2. Sandip Roy
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  3. Sayak Konar
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Correspondence to Gunjan Mukherjee .

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Editors and Affiliations

  1. Delhi - NCR Campus, SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. Delhi - NCR Campus, SRM Institute of Science and Technology, Ghaziabad, India

    Rohit Sharma

  3. Dept. of Embedded Systems Engineering, Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

  4. Computer Science&Engineering, GIET University, Gunupur, Gunupur, India

    Raghvendra Kumar

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Mukherjee, G., Roy, S., Konar, S., Bose, R., Mukherjee, A. (2023). Studies on Conventional and Advanced Machine Learning Algorithm Towards Framing of Robust Data Analytics for the Smart Grid Application. In: Kumar Sharma, D., Sharma, R., Jeon, G., Kumar, R. (eds) Data Analytics for Smart Grids Applications—A Key to Smart City Development. Intelligent Systems Reference Library, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-031-46092-0_5

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