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Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

Author

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  • Anthony Faustine

    (Ireland’s National Centre for Applied Data Analytics (CeADER), University College Dublin, Dublin 4, Ireland
    ITI, LARSyS, Técnico Lisboa, 1049-001 Lisboa, Portugal)

  • Lucas Pereira

    (ITI, LARSyS, Técnico Lisboa, 1049-001 Lisboa, Portugal)

Abstract

Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features.

Suggested Citation

  • Anthony Faustine & Lucas Pereira, 2020. "Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks," Energies, MDPI, vol. 13(13), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3374-:d:379045
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    References listed on IDEAS

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    1. Darío Baptista & Sheikh Shanawaz Mostafa & Lucas Pereira & Leonel Sousa & Fernando Morgado-Dias, 2018. "Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory," Energies, MDPI, vol. 11(9), pages 1-18, September.
    2. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
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    Cited by:

    1. Dirk Deschrijver, 2021. "Special Issue: “Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization”," Energies, MDPI, vol. 14(6), pages 1-3, March.
    2. Dadiana-Valeria Căiman & Toma-Leonida Dragomir, 2020. "A Novel Method for Obtaining the Signature of Household Consumer Pairs," Energies, MDPI, vol. 13(22), pages 1-20, November.
    3. 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.
    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. Muhammad Asif Ali Rehmani & Saad Aslam & Shafiqur Rahman Tito & Snjezana Soltic & Pieter Nieuwoudt & Neel Pandey & Mollah Daud Ahmed, 2021. "Power Profile and Thresholding Assisted Multi-Label NILM Classification," Energies, MDPI, vol. 14(22), pages 1-18, November.
    6. 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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