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        Energy Disaggregation Using Elastic Matching Algorithms

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        entropy_22_00071.pdf (PDF, 384Kb)
        Author
        Schirmer, Pascal
        Mporas, Iosif
        Paraskevas, Michael
        Attention
        2299/22235
        Abstract
        In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.
        Publication date
        2020-01-06
        Published in
        Entropy
        Published version
        https://doi.org/10.3390/e22010071
        Other links
        http://hdl.handle.net/2299/22235
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