{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T13:53:38Z","timestamp":1785938018871,"version":"3.56.0"},"reference-count":72,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund of the European Union and Greek national funds","award":["T1EDK-00343(95699)"],"award-info":[{"award-number":["T1EDK-00343(95699)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance power disaggregation model. The proposed architecture is a multi-target regression neural network consisting of two main parts. The first part is a variational encoder with convolutional layers, and the second part has multiple regression heads which share the encoder\u2019s parameters. Considering the total consumption of an installation, the multi-regressor outputs the individual consumption of all the target appliances simultaneously. The experimental setup includes a comparative analysis against other multi- and single-target state-of-the-art models.<\/jats:p>","DOI":"10.3390\/s23042051","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T02:14:11Z","timestamp":1676254451000},"page":"2051","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Variational Regression for Multi-Target Energy Disaggregation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3043-9220","authenticated-orcid":false,"given":"Nikolaos","family":"Virtsionis Gkalinikis","sequence":"first","affiliation":[{"name":"School of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7398-5862","authenticated-orcid":false,"given":"Christoforos","family":"Nalmpantis","sequence":"additional","affiliation":[{"name":"School of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Vrakas","sequence":"additional","affiliation":[{"name":"School of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1109\/5.192069","article-title":"Nonintrusive appliance load monitoring","volume":"80","author":"Hart","year":"1992","journal-title":"Proc. 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