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Non-intrusive Load Monitoring Based on Regularized ResNet with Multivariate Control Chart

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

With the development of industry and the spread of the Smart Home, the need for power monitoring solution technologies for effective energy management systems is increasing. Of these, non-intrusive load monitoring (NILM), is an efficient way to solve the electricity consumption monitoring problem. NILM is a technique to measure the power consumption of individual devices by analyzing the power data collected through smart meters and commercial devices. In this paper, we propose a deep neural network (DNN)-based NILM technique that enables energy disaggregation and power consumption monitoring simultaneously. Energy disaggregation is performed by learning a deep residual network for performing multilabel regression. Real-time monitoring is performed using a multivariate control chart technique using latent variables extracted through weights of the trained model. The energy disaggregation and monitoring performance of the proposed method is verified using the public NILM Electricity Consumption and Occupancy (ECO) data set.

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Acknowledgments

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01417) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

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Authors and Affiliations

  1. Department of Smart Factory Convergence, Sungkyunkwan University, Suwon, Jangan-gu, Gyeonggi-do, 16419, Republic of Korea

    Cheolhwan Oh & Jongpil Jeong

Authors
  1. Cheolhwan Oh
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  2. Jongpil Jeong
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Corresponding author

Correspondence to Jongpil Jeong .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Oh, C., Jeong, J. (2020). Non-intrusive Load Monitoring Based on Regularized ResNet with Multivariate Control Chart. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_47

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  • DOI: https://doi.org/10.1007/978-3-030-58802-1_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58801-4

  • Online ISBN: 978-3-030-58802-1

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