Short-Term Multinodal Load Forecasting Using a Fuzzy-ARTMAP Neural Network

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorScience and Technology-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorAbreu, T.-
Autor(es): dc.creatorMoreira, J. R. [UNESP]-
Autor(es): dc.creatorMinussi, C. R. [UNESP]-
Autor(es): dc.creatorLotufo, A. D.P. [UNESP]-
Autor(es): dc.creatorLopes, M. L.M. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:23:54Z-
Data de disponibilização: dc.date.available2022-02-22T00:23:54Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ISGT-LA.2019.8895486-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198214-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198214-
Descrição: dc.descriptionThe prediction of electric charges is essential in the electric power system, because it establishes when and how much of generation, transmission and distribution capacity must be arranged to meet the expected load without interruptions in supply. Therefore, the more accurate, reliable and fast the results, the better quality the forecast will be. This paper proposes an approach that performs the forecast considering several points of the electricity network (multinodal forecast), where different types of consumers are considered (industrial, commercial and residential). In this problem is used an ARTMAP Fuzzy artificial neural network , that is based in the theory of resonance adaptative (ART). The main characteristic of neural networks of the ART family is the stability and plasticity that provide results quickly and accurately. In order to test the proposed forecast system, results of 24 hours (48 points) ahead are presented for nine substations of a New Zealand Electrical Company.-
Descrição: dc.descriptionFederal Institute of Education Science and Technology Campus Hortolândia-
Descrição: dc.descriptionUNESP - São Paulo State University-
Descrição: dc.descriptionUNESP - Universidade Estadual Paulista Júlio de Mesquita Filho-
Descrição: dc.descriptionUNESP - São Paulo State University-
Descrição: dc.descriptionUNESP - Universidade Estadual Paulista Júlio de Mesquita Filho-
Idioma: dc.languageen-
Relação: dc.relation2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAggregate Load-
Palavras-chave: dc.subjectElectrical System Distribution-
Palavras-chave: dc.subjectFuzzy ARTMAP Neural Network-
Palavras-chave: dc.subjectLoad Forecasting-
Título: dc.titleShort-Term Multinodal Load Forecasting Using a Fuzzy-ARTMAP Neural Network-
Aparece nas coleções:Repositório Institucional - Unesp

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