{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:27Z","timestamp":1760144307640,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Companhia Paranaense de Energia","award":["ANEEL-PD-02866-0508\/2019"],"award-info":[{"award-number":["ANEEL-PD-02866-0508\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Efficient energy management in residential environments is a constant challenge, in which Home Energy Management Systems (HEMS) play an essential role in optimizing consumption. Load recognition allows the identification of active appliances, providing robustness to the HEMS. The precise identification of household appliances is an area not completely explored. Gaps like improving classification performance through techniques dedicated to separability between classes and models that achieve enhanced reliability remain open. This work improves several aspects of load recognition in HEMS applications. In this research, we adopt Neighborhood Component Analysis (NCA) to extract relevant characteristics from the data, seeking the separability between classes. We also employ the Regularized Extreme Learning Machine (RELM) to identify household appliances. This pioneering approach achieves performance improvements, presenting higher accuracy and weighted F1-Score values\u201497.24% and 97.14%, respectively\u2014surpassing state-of-the-art methods and enhanced reliability according to the Kappa index, i.e., 0.9388, outperforming competing classifiers. Such evidence highlights the promising potential of Machine Learning (ML) techniques, specifically NCA and RELM, to contribute to load recognition and energy management in residential environments.<\/jats:p>","DOI":"10.3390\/s24072274","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:58:38Z","timestamp":1712105918000},"page":"2274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Load Recognition in Home Energy Management Systems Based on Neighborhood Components Analysis and Regularized Extreme Learning Machine"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-6440","authenticated-orcid":false,"given":"Thales W.","family":"Cabral","sequence":"first","affiliation":[{"name":"Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando B.","family":"Neto","sequence":"additional","affiliation":[{"name":"Companhia Paranaense de Energia, Curitiba 81200-240, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo R.","family":"de Lima","sequence":"additional","affiliation":[{"name":"Department of Hardware Design, Instituto de Pesquisa Eldorado, Campinas 13083-898, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-1496","authenticated-orcid":false,"given":"Gustavo","family":"Fraidenraich","sequence":"additional","affiliation":[{"name":"Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5782-9608","authenticated-orcid":false,"given":"Lu\u00eds G. P.","family":"Meloni","sequence":"additional","affiliation":[{"name":"Department of Communications, School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Rashid, H., and Singh, P. (2018, January 18\u201320). Monitor: An abnormality detection approach in buildings energy consumption. 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