Wind power forecast using neural networks: Tuning with optimization techniques and error analysis

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Lisbon-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorNazaré, Gonçalo-
Autor(es): dc.creatorCastro, Rui-
Autor(es): dc.creatorGabriel Filho, Luís R.A. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:24:02Z-
Data de disponibilização: dc.date.available2022-02-22T00:24:02Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1002/we.2460-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198254-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198254-
Descrição: dc.descriptionThe increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power forecast is imperative for systems operators, aiming at an efficient and economical wind power operation and integration into the power system. This work addresses the issue of forecasting short-term wind speed and wind power for 1 hour ahead, combining artificial neural networks (ANNs) with optimization techniques on real historical wind speed and wind power data. Levenberg-Marquardt (LM) and particle swarm optimization (PSO) are used as training algorithms to update the weights and bias of the ANN applied to wind speed predictions. The forecasting performance produced by the proposed models are compared with each other, as well as with the benchmark persistence model. Test results show higher performance for ANN-LM wind speed forecasting model, outperforming both ANN-PSO and persistence. The application of ANN-LM to wind power forecast revealed also a good performance, with an average improvement of 2.8% in relation to persistence. An innovative analysis of mean absolute percentage error (MAPE) behaviour in time and in typical days is finally offered in the paper.-
Descrição: dc.descriptionFundação para a Ciência e a Tecnologia-
Descrição: dc.descriptionIST—Instituto Superior Técnico University of Lisbon-
Descrição: dc.descriptionINESC-ID/IST University of Lisbon-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionFundação para a Ciência e a Tecnologia: UID/CEC/50021/2019-
Formato: dc.format810-824-
Idioma: dc.languageen-
Relação: dc.relationWind Energy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial neural network-
Palavras-chave: dc.subjectLevenberg-Marquardt-
Palavras-chave: dc.subjectparticle swarm optimization-
Palavras-chave: dc.subjectshort-term wind forecast-
Título: dc.titleWind power forecast using neural networks: Tuning with optimization techniques and error analysis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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