dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversity of Porto
dc.date.accessioned2021-06-25T10:27:44Z
dc.date.accessioned2022-12-19T22:13:50Z
dc.date.available2021-06-25T10:27:44Z
dc.date.available2022-12-19T22:13:50Z
dc.date.created2021-06-25T10:27:44Z
dc.date.issued2020-11-01
dc.identifierIEEE Latin America Transactions, v. 18, n. 11, p. 2011-2018, 2020.
dc.identifier1548-0992
dc.identifierhttp://hdl.handle.net/11449/206172
dc.identifier10.1109/TLA.2020.9398643
dc.identifier2-s2.0-85103941905
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5386769
dc.description.abstractThe 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.
dc.languagepor
dc.relationIEEE Latin America Transactions
dc.sourceScopus
dc.subjectLong-Term
dc.subjectMachine learning
dc.subjectNortheastern Brazil
dc.subjectRegression
dc.subjectWind power
dc.subjectWind speed forecasting
dc.titlePredicting long-term wind speed in wind farms of northeast Brazil: A comparative analysis through machine learning models
dc.typeArtículos de revistas


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