Predicting long-term wind speed in wind farms of northeast Brazil: A comparative analysis through machine learning models

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Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniversity of Porto-
Autor(es): dc.creatorde Paula, M. [UNESP]-
Autor(es): dc.creatorColnago, M. [UNESP]-
Autor(es): dc.creatorFidalgo, J.-
Autor(es): dc.creatorCasaca, W. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:46:21Z-
Data de disponibilização: dc.date.available2022-02-22T00:46:21Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2020-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TLA.2020.9398643-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/206172-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/206172-
Descrição: dc.descriptionThe rapid growth of wind generation in northeast Brazil has led to multiple benefits to many different stakeholders of energy industry, especially because the wind is a renewable resource - an abundant and ubiquitous power source present in almost every state in the northeast region of Brazil. Despite the several benefits of wind power, forecasting the wind speed becomes a challenging task in practice, as it is highly volatile over time, especially when one has to deal with long-term predictions. Therefore, this paper focuses on applying different Machine Learning strategies such as Random Forest, Neural Networks and Gradient Boosting to perform regression on wind data for long periods of time. Three wind farms in the northeast Brazil have been investigated, whose data sets were constructed from the wind farms data collections and the National Institute of Meteorology (INMET). Statistical analyses of the wind data and the optimization of the trained predictors were conducted, as well as several quantitative assessments of the obtained forecast results.-
Descrição: dc.descriptionDepartment of Energy Engineering São Paulo State University (UNESP), Av. dos Barrageiros, 1881-
Descrição: dc.descriptionThe Power Systems Unit of INESC TEC The Faculty of Engineering University of Porto, Rua Dr Roberto Frias, s/n-
Descrição: dc.descriptionDepartment of Energy Engineering São Paulo State University (UNESP), Av. dos Barrageiros, 1881-
Formato: dc.format2011-2018-
Idioma: dc.languagept_BR-
Relação: dc.relationIEEE Latin America Transactions-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectLong-Term-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectNortheastern Brazil-
Palavras-chave: dc.subjectRegression-
Palavras-chave: dc.subjectWind power-
Palavras-chave: dc.subjectWind speed forecasting-
Título: dc.titlePredicting long-term wind speed in wind farms of northeast Brazil: A comparative analysis through machine learning models-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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