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A bibliometric survey on incremental clustering algorithm for electricity smart meter data analysis

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Abstract

Smart meters (SMs) are an electronic device for recording customer energy consumption in the time intervals of an hour or less. The use of SMs incurs benefits to people in various aspects such as environmental, social, and economical. SMs frequently communicate with utility companies for monitoring and management of energy usage as well as with customers for observing their energy consumption. It generates a considerable amount of electricity smart meter data incrementally. In the clustering task, instead of re-clustering all data from scratch on the influx of new data, it is better to update clustering result incrementally based on new as well as old data. Thus, an incremental clustering approach is an essential way to overcome the issue related to clustering with growing data. The purpose of the paper is to dig out all the researches in smart meter data analytics and incremental clustering to make the concept clear for future researchers. This bibliometric analysis is implemented using the repositories such as Scopus, Google Scholar, ResearchGate, and the tools like Gephi, Table2Net, and GPS Visualizer, etc. The study revealed that the maximum number of the reviews on smart meter and incremental clustering had explored very recently.

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Source: https://www.scopus.com/ (accessed on 19 Nov 2018)

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Source: https://www.scopus.com/ (accessed on 19 Nov 2018)

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Source: https://www.scopus.com/ (accessed on 19 Nov 2018)

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Acknowledgements

This research was supported by “Microsoft Azure: AI for earth”. We would like to thank “Sakal India Foundation” for research scholarship (Grant).

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  1. Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), Pune, India

    Archana Chaudhari & Preeti Mulay

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  1. Archana Chaudhari
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Chaudhari, A., Mulay, P. A bibliometric survey on incremental clustering algorithm for electricity smart meter data analysis. Iran J Comput Sci 2, 197–206 (2019). https://doi.org/10.1007/s42044-019-00043-0

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