Selection of features from power theories to compose NILM datasets

Autor: Wesley A. Souza, Augusto M.S. Alonso, Thais B. Bosco, Fernando D. Garcia, Flavio A.S. Gonçalves, Fernando P. Marafão
Přispěvatelé: PR, Universidade Estadual de Campinas (UNICAMP), Universidade Estadual Paulista (UNESP)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Popis: Made available in DSpace on 2022-05-01T13:57:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-04-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load Monitoring (NILM) approaches. This occurs since knowledge about individualized consumption per appliance allows to create strategies striving for energy savings, improvement of energy efficiency, and creating energy awareness to consumers. Moreover, by using feature extraction to devise energy disaggregation, one can achieve accurate identification of electric appliances. However, even though several literature works propose distinct features to be utilized, no consensus exists in the literature about the most appropriate set of features that ensure high accuracy on load disaggregation. Thus, beyond presenting a critical analysis of some significant features often selected in the literature, this paper proposes identifying the most relevant ones considering collinearity and machine learning algorithms. The results show that high-performance metrics can be achieved with fewer features than usually adopted in the literature. Moreover, it is demonstrated that the Conservative Power Theory can offer the most representative features for appliance identification, leading to efficient power consumption disaggregation. Department of Electrical Engineering Federal University of Technology - Parana (UTFPR) Cornélio Procópio PR School of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas SP Institute of Science and Technology São Paulo State University (UNESP) Sorocaba SP Institute of Science and Technology São Paulo State University (UNESP) Sorocaba SP FAPESP: 2016/08645-9
Databáze: OpenAIRE
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