On predicting wind power series by using BEA modified neural networks-based approach

Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina.

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Detalles Bibliográficos
Autores principales: Rodriguez Rivero, Cristian, Pucheta, Julian, Túpac, Yván, Laboret, Sergio, Gorrostieta, Efren, Otaño, Paula
Formato: conferenceObject
Lenguaje:Inglés
Publicado: 2025
Materias:
Time series forecasting
Ingeniería Electrónica
BEAmod
Ingeniería en Computación
Acceso en línea:http://hdl.handle.net/11086/555243
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-555243
record_format dspace
spelling I10-R141-11086-5552432025-03-25T13:52:16Z On predicting wind power series by using BEA modified neural networks-based approach Rodriguez Rivero, Cristian Pucheta, Julian Túpac, Yván Laboret, Sergio Gorrostieta, Efren Otaño, Paula Time series forecasting Ingeniería Electrónica BEAmod Ingeniería en Computación Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina. Fil: Pucheta, Julian. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina. Fil: Túpac, Yván. Universidad Católica San Pablo Arequipa. Escuela de Ciencias de la Computación; Perú. Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina. Fil: Gorrostieta, Efren. Universidad Autónoma de Querétaro. Laboratorio de Mecatrónica; Argentina. Fil: Otaño, Paula. Universidad Nacional de Córdoba. Departamento de Ingeniería en Sistema; Argentina. In 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. Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina. Fil: Pucheta, Julian. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina. Fil: Túpac, Yván. Universidad Católica San Pablo Arequipa. Escuela de Ciencias de la Computación; Perú. Fil: Laboret, Sergio. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina. Fil: Gorrostieta, Efren. Universidad Autónoma de Querétaro. Laboratorio de Mecatrónica; Argentina. Fil: Otaño, Paula. Universidad Nacional de Córdoba. Departamento de Ingeniería en Sistema; Argentina. Sistemas de Automatización y Control 2025-03-25T13:51:01Z 2025-03-25T13:51:01Z 2017 conferenceObject 978-987-544-754-7 http://hdl.handle.net/11086/555243 eng Attribution-NonCommercial-ShareAlike 4.0 International http://creativecommons.org/licenses/by-nc-sa/4.0/ Impreso
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Time series forecasting
Ingeniería Electrónica
BEAmod
Ingeniería en Computación
spellingShingle Time series forecasting
Ingeniería Electrónica
BEAmod
Ingeniería en Computación
Rodriguez Rivero, Cristian
Pucheta, Julian
Túpac, Yván
Laboret, Sergio
Gorrostieta, Efren
Otaño, Paula
On predicting wind power series by using BEA modified neural networks-based approach
topic_facet Time series forecasting
Ingeniería Electrónica
BEAmod
Ingeniería en Computación
description Fil: Rodriguez Rivero, Cristian. Universidad Nacional de Córdoba. Departamento de ingeniería electrónica; Argentina.
format conferenceObject
author Rodriguez Rivero, Cristian
Pucheta, Julian
Túpac, Yván
Laboret, Sergio
Gorrostieta, Efren
Otaño, Paula
author_facet Rodriguez Rivero, Cristian
Pucheta, Julian
Túpac, Yván
Laboret, Sergio
Gorrostieta, Efren
Otaño, Paula
author_sort Rodriguez Rivero, Cristian
title On predicting wind power series by using BEA modified neural networks-based approach
title_short On predicting wind power series by using BEA modified neural networks-based approach
title_full On predicting wind power series by using BEA modified neural networks-based approach
title_fullStr On predicting wind power series by using BEA modified neural networks-based approach
title_full_unstemmed On predicting wind power series by using BEA modified neural networks-based approach
title_sort on predicting wind power series by using bea modified neural networks-based approach
publishDate 2025
url http://hdl.handle.net/11086/555243
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