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Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning

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Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

Abstract

The utilization of smart meters is increasing with the technical developments. A proper collection and analysis of metering data is important to better serve various smart grid stakeholders. The classical sensing mechanism is known to be time-variant, which results in vast amounts of excessive data to be collected, distributed, processed, and stored. It causes an unnecessary increase of processing activity and consumption. In this context, this research uses the event-driven sensing and processing methods for an effective processing and collection of smart meter data. New adaptive rate techniques for data acquisition, processing, segmentation, and extraction of features are suggested. The extracted features, related to consumption patterns of appliances, are then used to classify these appliances. The classification is carried out by using robust algorithms, namely, k-nearest neighbor (KNN), artificial neural network (ANN), and Naïve Bayes. The findings showed more than a threefold benefit in compression and computational efficiency while attaining 94.4% highest classification accuracy for a six-class dataset.

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Acknowledgments

This project is funded by the Effat University of Jeddah, under the grant number UC#9/29 April.2020/7.1-22(2)2.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, College of Engineering, Effat University, Jeddah, Saudi Arabia

    Saeed Mian Qaisar & Futoon Alsharif

  2. Communication & Signal Processing Lab, Energy & Technology Center, Effat University, Jeddah, Saudi Arabia

    Saeed Mian Qaisar

Authors
  1. Saeed Mian Qaisar
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  2. Futoon Alsharif
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Corresponding author

Correspondence to Saeed Mian Qaisar .

Editor information

Editors and Affiliations

  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

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Mian Qaisar, S., Alsharif, F. (2021). Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77695-4

  • Online ISBN: 978-3-030-77696-1

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