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

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dc.contributor.authorFernández, Leonardo Brain García [UNESP]
dc.contributor.authorLotufo, Anna Diva Plasencia [UNESP]
dc.contributor.authorMinussi, Carlos Roberto [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T16:15:28Z
dc.date.available2023-07-29T16:15:28Z
dc.date.issued2023-05-01
dc.description.abstractIn 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.en
dc.description.affiliationElectrical Engineering Department UNESP—São Paulo State University, Av. Brasil 56, SP
dc.description.affiliationUnespElectrical Engineering Department UNESP—São Paulo State University, Av. Brasil 56, SP
dc.identifierhttp://dx.doi.org/10.3390/en16104110
dc.identifier.citationEnergies, v. 16, n. 10, 2023.
dc.identifier.doi10.3390/en16104110
dc.identifier.issn1996-1073
dc.identifier.scopus2-s2.0-85160643236
dc.identifier.urihttp://hdl.handle.net/11449/250016
dc.language.isoeng
dc.relation.ispartofEnergies
dc.sourceScopus
dc.subjectadaptive resonance theory
dc.subjectelectrical load forecasting in disaggregated level
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectsupport vector machine
dc.subjectwavelet filters
dc.titleDevelopment of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategyen
dc.typeArtigo
unesp.author.orcid0000-0002-1372-6180[1]
unesp.author.orcid0000-0002-0192-2651[2]
unesp.author.orcid0000-0001-7540-6572[3]
unesp.departmentEngenharia Elétrica - FEISpt

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Ilha Solteira - FEIS - Faculdade de Engenharia