Improving energy disaggregation performance using appliance-driven sampling rates

Pascal A. Schirmer, Iosif Mporas

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)

    Abstract

    This paper proposes a new appliance-driven selection of sampling frequencies for improving the energy disaggregation performance in non-intrusive load monitoring. Specifically, the methodology uses a machine learning model with parallel device detectors and optimized device dependent sampling rates in order to improve device identification. The performance of the proposed methodology was evaluated on a state-of-the-art baseline system and a set of publicly available databases increasing performance up to 6.7% in terms of estimation accuracy when compared to the baseline energy disaggregation setup without device dependent sampling rates.
    Original languageEnglish
    Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
    PublisherEuropean Signal Processing Conference, EUSIPCO
    ISBN (Electronic)9789082797039
    DOIs
    Publication statusPublished - Sep 2019
    Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
    Duration: 2 Sep 20196 Sep 2019

    Publication series

    NameEuropean Signal Processing Conference
    Volume2019-September
    ISSN (Print)2219-5491

    Conference

    Conference27th European Signal Processing Conference, EUSIPCO 2019
    Country/TerritorySpain
    CityA Coruna
    Period2/09/196/09/19

    Keywords

    • Device Classification
    • Energy Disaggregation
    • Non-intrusive load monitoring (NILM)

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