A comparative study of machine learning classifiers for electric load disaggregation based on an extended nilm dataset

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUTFPR - Federal University of Technology-
Autor(es): dc.creatorBosco, Thais Berrettini-
Autor(es): dc.creatorGonçalves, Flavio Alessandro Serrão-
Autor(es): dc.creatorde Souza, Wesley Angelino-
Data de aceite: dc.date.accessioned2025-08-21T23:42:46Z-
Data de disponibilização: dc.date.available2025-08-21T23:42:46Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2021-08-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/INDUSCON51756.2021.9529824-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233587-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233587-
Descrição: dc.descriptionThe appliance evaluation and the power consumption consciousness are becoming essential for improving demand management and power grid enhancement. Load disaggregation becomes a promising engine for this goal, and some researches efforts have been made in the last years. In this sense, achieving the load characterization is essential to the technique's success; moreover, the proper feature extraction becomes essential. In this way, this paper presents a comparative study of machine learning classifiers for electric load disaggregation using an enhanced version of a household appliance dataset proposed by Souza et al. of Brazilian appliances (NILMbr). The load characterization is performed through the Conservative Power Theory, a recent power theory that extracts appliance signatures by means of power quantities. Then, it is proposed three machine learning models to validate proper load identification, being: classification algorithms - kNearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF). These algorithms were used to assess computational time and performance metrics. Subsequently, the RF algorithm presented the best performance, with an accuracy of 99.5%.-
Descrição: dc.descriptionICTS - Institute of Science and Technology of Sorocaba UNESP - Sao Paulo State University-
Descrição: dc.descriptionDAELE - Department of Electrical Engineering UTFPR - Federal University of Technology-
Descrição: dc.descriptionICTS - Institute of Science and Technology of Sorocaba UNESP - Sao Paulo State University-
Formato: dc.format270-277-
Idioma: dc.languageen-
Relação: dc.relation2021 14th IEEE International Conference on Industry Applications, INDUSCON 2021 - Proceedings-
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Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectLoad disaggregation-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectPower meter-
Título: dc.titleA comparative study of machine learning classifiers for electric load disaggregation based on an extended nilm dataset-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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