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Deep Learning Techniques for Smart Meter Data Analytics: A Review

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Abstract

The field of smart meter data analytics is a relatively young field that recently grew due to the wealth of data generated from smart meters. Recent progress in high-performance computing made it possible to efficiently use deep neural networks to extract more actionable knowledge and patterns from massive data collected within the smart grid. Examining the available literature, there is a need for a comprehensive survey that covers all aspects of smart meter data analytics. This review paper provides a broad overview of the current research spectrum within smart meter analysis, while identifying future challenges for smart meter data analytic through a detailed taxonomy. The proposed taxonomy identifies the main domains within the field, as well as the various functions that are conducted within each domain. To show the strength of deep learning techniques in smart meter analytics, an experiment is conducted comparing various classical load profiling techniques with deep clustering. The results of the experiment conducted on an open large dataset show the performance of deep clustering to be better than classical techniques. Furthermore, the review is performed from the perspective of data science; emphasizing the data tasks such as data reprocessing, analytic algorithms, and evaluation methods for each domain. Furthermore, the paper discusses deep neural network techniques used for each domain of smart meter analysis and identifies gaps within each domain, including obstacles and potential opportunities for research.

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We thank Michael Hutton of the ōbex project for language editing support.

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  1. Department of the Computer Science, University of Bahrain, P.o. Box 32038, Zallaq, Bahrain

    Elham Eskandarnia, Hesham Al-Ammal, Riadh Ksantini & Mustafa Hammad

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  1. Elham Eskandarnia
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Eskandarnia, E., Al-Ammal, H., Ksantini, R. et al. Deep Learning Techniques for Smart Meter Data Analytics: A Review. SN COMPUT. SCI. 3, 243 (2022). https://doi.org/10.1007/s42979-022-01161-6

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