Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorFernández, Leonardo Brain García-
Autor(es): dc.creatorLotufo, Anna Diva Plasencia-
Autor(es): dc.creatorMinussi, Carlos Roberto-
Data de aceite: dc.date.accessioned2025-08-21T15:30:26Z-
Data de disponibilização: dc.date.available2025-08-21T15:30:26Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/en16104110-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/250016-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/250016-
Descrição: dc.descriptionIn recent years, electrical systems have evolved, creating uncertainties in short-term economic dispatch programming due to demand fluctuations from self-generating companies. This paper proposes a flexible Machine Learning (ML) approach to address electrical load forecasting at various levels of disaggregation in the Peruvian Interconnected Electrical System (SEIN). The novelty of this approach includes utilizing meteorological data for training, employing an adaptable methodology with easily modifiable internal parameters, achieving low computational cost, and demonstrating high performance in terms of MAPE. The methodology combines modified Fuzzy ARTMAP Neural Network (FAMM) and hybrid Support Vector Machine FAMM (SVMFAMM) methods in a parallel process, using data decomposition through the Wavelet filter db20. Experimental results show that the proposed approach outperforms state-of-the-art models in predicting accuracy across different time intervals.-
Descrição: dc.descriptionElectrical Engineering Department UNESP—São Paulo State University, Av. Brasil 56, SP-
Descrição: dc.descriptionElectrical Engineering Department UNESP—São Paulo State University, Av. Brasil 56, SP-
Idioma: dc.languageen-
Relação: dc.relationEnergies-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectadaptive resonance theory-
Palavras-chave: dc.subjectelectrical load forecasting in disaggregated level-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectneural networks-
Palavras-chave: dc.subjectsupport vector machine-
Palavras-chave: dc.subjectwavelet filters-
Título: dc.titleDevelopment of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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