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A Multi-Agent NILM Architecture for Event Detection and Load Classification

Author

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  • 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)

  • 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)

  • Carlos Raimundo 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)

  • 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)

  • 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)

  • 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)

  • Fabiana Pöttker

    (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)

  • 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)

  • 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 multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

Suggested Citation

  • André Eugenio Lazzaretti & Douglas Paulo Bertrand Renaux & Carlos Raimundo Erig Lima & Bruna Machado Mulinari & Hellen Cristina Ancelmo & Elder Oroski & Fabiana Pöttker & Robson Ribeiro Linhares & Luc, 2020. "A Multi-Agent NILM Architecture for Event Detection and Load Classification," Energies, MDPI, vol. 13(17), pages 1-35, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4396-:d:404219
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    References listed on IDEAS

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    1. Hsueh-Hsien Chang, 2012. "Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses," Energies, MDPI, vol. 5(11), pages 1-21, November.
    2. 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. 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.

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