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        Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

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        Author
        Schirmer, Pascal
        Mporas, Iosif
        Sheikh-Akbari, Akbar
        Attention
        2299/22648
        Abstract
        A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.
        Publication date
        2020-05-01
        Published in
        Energies
        Published version
        https://doi.org/10.3390/en13092148
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/22648
        Relations
        School of Physics, Engineering & Computer Science
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