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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.<\/jats:p>","DOI":"10.3390\/e22010071","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T10:34:46Z","timestamp":1578306886000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Energy Disaggregation Using Elastic Matching Algorithms"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5434-4739","authenticated-orcid":false,"given":"Pascal A.","family":"Schirmer","sequence":"first","affiliation":[{"name":"Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iosif","family":"Mporas","sequence":"additional","affiliation":[{"name":"Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Paraskevas","sequence":"additional","affiliation":[{"name":"Computer Technology Institute and Press \u201cDiophantus\u201d, Dept of Electrical and Computer Engineering, University of Peloponnese, 221 00 Tripoli, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.energy.2017.05.110","article-title":"Energy consumption in machining: Classification, prediction, and reduction strategy","volume":"133","author":"Zhao","year":"2017","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.energy.2019.05.230","article-title":"Predicting residential energy consumption using CNN-LSTM neural networks","volume":"182","author":"Kim","year":"2019","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.enbuild.2007.03.007","article-title":"A review on buildings energy consumption information","volume":"40","author":"Ortiz","year":"2008","journal-title":"Energy Build."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mostafavi, S., and Cox, R.W. 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