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Scattering Transform for Classification in Non-Intrusive Load Monitoring

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  • Everton Luiz de Aguiar

    (CPGEI—Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná—UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, PR, Brazil)

  • André Eugenio Lazzaretti

    (CPGEI—Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná—UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, PR, Brazil)

  • Bruna Machado Mulinari

    (Dataplai, Eng. Niepce da Silva, 200, Curitiba 80610-280, PR, Brazil)

  • Daniel Rodrigues Pipa

    (CPGEI—Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná—UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, PR, Brazil)

Abstract

Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art results were obtained extracting NILM features with convolutional neural networks (CNN). However, it depends on the training process with large datasets or data augmentation strategies. In this paper, we propose a feature extraction strategy for NILM using the Scattering Transform (ST). The ST is a convolutional network analogous to CNN. Nevertheless, it does not need a training process in the feature extraction stage, and the filter coefficients are analytically determined (not empirically, like CNN). We perform tests with the proposed method on different publicly available datasets and compare the results with state-of-the-art deep learning-based and traditional approaches (including wavelet transform and V-I representations). The results show that ST classification accuracy is more robust in terms of waveform parameters, such as signal length, sampling frequency, and event location. Besides, ST overcame the state-of-the-art techniques for single and aggregated loads (accuracies above 99% for all evaluated datasets), in different training scenarios with single and aggregated loads, indicating its feasibility in practical NILM scenarios.

Suggested Citation

  • Everton Luiz de Aguiar & André Eugenio Lazzaretti & Bruna Machado Mulinari & Daniel Rodrigues Pipa, 2021. "Scattering Transform for Classification in Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(20), pages 1-20, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6796-:d:658887
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    References listed on IDEAS

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    1. Christos Athanasiadis & Dimitrios Doukas & Theofilos Papadopoulos & Antonios Chrysopoulos, 2021. "A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption," Energies, MDPI, vol. 14(3), pages 1-23, February.
    2. Douglas Paulo Bertrand Renaux & Fabiana Pottker & Hellen Cristina Ancelmo & André Eugenio Lazzaretti & Carlos Raiumundo Erig Lima & Robson Ribeiro Linhares & Elder Oroski & Lucas da Silva Nolasco & Lu, 2020. "A Dataset for Non-Intrusive Load Monitoring: Design and Implementation," Energies, MDPI, vol. 13(20), pages 1-35, October.
    3. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
    4. Anthony Faustine & Lucas Pereira, 2020. "Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network," Energies, MDPI, vol. 13(16), pages 1-17, August.
    5. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
    6. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    7. Saura, José Ramón & Palacios-Marqués, Daniel & Iturricha-Fernández, Agustín, 2021. "Ethical design in social media: Assessing the main performance measurements of user online behavior modification," Journal of Business Research, Elsevier, vol. 129(C), pages 271-281.
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    Cited by:

    1. Yongtao Shi & Xiaodong Zhao & Fan Zhang & Yaguang Kong, 2022. "Non-Intrusive Load Monitoring Based on Swin-Transformer with Adaptive Scaling Recurrence Plot," Energies, MDPI, vol. 15(20), pages 1-18, October.

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