Mi DSpace
Usuario
Contraseña
Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15773
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRodriguez Rivero, Cristian-
dc.contributor.authorPucheta, Julian-
dc.contributor.authorOtano, Paula-
dc.contributor.authorTúpac Valdivia, Yván Jesús-
dc.contributor.authorGorrostieta, Efren-
dc.contributor.authorLaboret, Sergio-
dc.date.accessioned2019-01-29T22:19:50Z-
dc.date.available2019-01-29T22:19:50Z-
dc.date.issued2017-
dc.identifier.isbnurn:isbn:9789875447547es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15773-
dc.description.abstractIn this paper, wind power series prediction using BEA modified (BEAmod.) neural networks-based approach is presented. Wind power forecasting is a complex, multidimensional, and highly non-linear system. Neural network is able to learn the relationship between system inputs and outputs without mathematical conversion, and perform complex nonlinear mapping, data classification, prediction, and is also suitable for wind power forecasting. The purpose of this paper is to use neural network to design a wind power forecasting system. The focus, with particularly interest in short-term prediction, is by using the data model selected, in which the Bayesian enhanced modified approach (BEAmod.) is used to extract information to make prediction. The efficiency analysis of the proposed forecasting method is examined through the underlying dynamical system, in which the nonlinear and temporal dependencies span long time intervals (long memory process). The conducted results show that this method can be used to improve the predictability of short-term wind time series with a suitable number of hidden units compared to that of reported in the literature. © 2017 Comisión Permanente RPIC.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046491666&doi=10.23919%2fRPIC.2017.8214355&partnerID=40&md5=386e57fee0bb809de58f5007410326c2es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectComplex networkses_PE
dc.subjectDynamical systemses_PE
dc.subjectLinear systemses_PE
dc.subjectTime serieses_PE
dc.subjectTime series analysises_PE
dc.subjectWind poweres_PE
dc.subjectBayesianes_PE
dc.subjectData classificationes_PE
dc.subjectExtract informationses_PE
dc.subjectForecasting methodses_PE
dc.subjectMathematical conversiones_PE
dc.subjectShort term predictiones_PE
dc.subjectTime series forecastinges_PE
dc.subjectWind power forecastinges_PE
dc.subjectWeather forecastinges_PE
dc.titleOn predicting wind power series by using Bayesian Enhanced modified based-neural networkes_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.identifier.doihttps://doi.org/10.23919/RPIC.2017.8214355es_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
Appears in Collections:Artículos - Ciencia de la computación

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.