Energy Disaggregation Using Elastic Matching Algorithms

Pascal Schirmer; Iosif Mporas; Michael Paraskevas

doi:10.3390/e22010071

Energy Disaggregation Using Elastic Matching Algorithms

Pascal Schirmer, Iosif Mporas, Michael Paraskevas

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

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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.

Original language	English
Article number	71
Journal	Entropy
Volume	22
Issue number	1
DOIs	https://doi.org/10.3390/e22010071
Publication status	Published - 6 Jan 2020

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entropy-22-00071
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
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title = "Energy Disaggregation Using Elastic Matching Algorithms",

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.",

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Energy Disaggregation Using Elastic Matching Algorithms

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