Busca avançada
Ano de início
Entree


A Comparative Study of Machine Learning Classifiers for Electric Load Disaggregation based on an extended NILM dataset

Texto completo
Autor(es):
Bosco, Thais Berrettini ; Serrao Goncalves, Flavio Alessandro ; de Souza, Wesley Angelino ; Tsuzuki, MDG ; Pessoa, MAD
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: 2021 14TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRY APPLICATIONS (INDUSCON); v. N/A, p. 8-pg., 2021-01-01.
Resumo

The 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%. (AU)

Processo FAPESP: 16/08645-9 - Pesquisas interdisciplinares em redes inteligentes de energia elétrica
Beneficiário:João Bosco Ribeiro do Val
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 18/24331-0 - Inversores multifuncionais empregando redes de impedância para geradores distribuídos
Beneficiário:Flávio Alessandro Serrão Gonçalves
Modalidade de apoio: Auxílio à Pesquisa - Regular