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ABSTRACT
Now, Smart Energy Meters (SEMs) have become very popular in controlling and managing electricity consumption in residential, commercial, industrial sectors, to name a few. SEMs collects the fine-grained energy-consumption data at a regular time interval (every hour). Such a low sampling data rate can be useful in research, evaluating the higher consumption of electricity, etc. However, existing research work cannot adequately assess the qualitative, quantitative, and logical properties of the data that could theoretically improve the performance of classification and analytics. The purpose of this bibliometric analysis is to understand the reach of SEMs and its data analysis worldwide and across the various applications to make the concept clear for future researchers. The Scopus, Semantic Scholar and Crossref databases are used for performing the bibliometric analysis. This research study revealed that the insights of the SEMs data-enabled significant advancement in many fields such as energy consumption pattern analysis and prediction, demand response, load profiling, and direct-indirect phone charging analysis.
Acknowledgments
This research was supported by “Microsoft Azure: AI for earth”. We would like to thank “Sakal India Foundation” for research scholarship (Grant).