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A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

Author

Listed:
  • Douglas Paulo Bertrand Renaux

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Fabiana Pottker

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Hellen Cristina Ancelmo

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • André Eugenio Lazzaretti

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Carlos Raiumundo Erig Lima

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Robson Ribeiro Linhares

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Elder Oroski

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Lucas da Silva Nolasco

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Lucas Tokarski Lima

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Bruna Machado Mulinari

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • José Reinaldo Lopes da Silva

    (LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil)

  • Júlio Shigeaki Omori

    (COPEL-Companhia Paranaense de Energia, José Izidoro Biazetto, 158, Curitiba 82305-100, Brazil)

  • Rodrigo Braun dos Santos

    (COPEL-Companhia Paranaense de Energia, José Izidoro Biazetto, 158, Curitiba 82305-100, Brazil)

Abstract

A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5371-:d:428354
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Netzah Calamaro & Moshe Donko & Doron Shmilovitz, 2021. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements," Energies, MDPI, vol. 14(21), pages 1-37, November.
    2. Yan, Lei & Tian, Wei & Wang, Hong & Hao, Xing & Li, Zuyi, 2023. "Robust event detection for residential load disaggregation," Applied Energy, Elsevier, vol. 331(C).
    3. 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.
    4. Andreas Reinhardt & Lucas Pereira, 2021. "Special Issue: “Energy Data Analytics for Smart Meter Data”," Energies, MDPI, vol. 14(17), pages 1-3, August.
    5. Augustyn Wójcik & Piotr Bilski & Robert Łukaszewski & Krzysztof Dowalla & Ryszard Kowalik, 2021. "Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator," Energies, MDPI, vol. 14(3), pages 1-26, January.

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