Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021

Eliseo Zarate-Perez; Juan Grados; Santiago Rubiños; Jessica Meza; Yesmi Ortega-Rojas; Herbert Grados-Espinoza; Arcelia Rojas

doi:10.18687/LACCEI2022.1.1.85

Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021

Eliseo Zarate-Perez
, Juan Grados
, Santiago Rubiños
, Jessica Meza
, Yesmi Ortega-Rojas
, Herbert Grados-Espinoza
, Arcelia Rojas

Universidad Nacional del Callao

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

The main objective of this study was to develop an evolutionary analysis of artificial neural networks (ANN) in the forecast of wind energy for the period from 2007 to 2021. The SciMat software tool was used to identify the performance and impact measures of the main research topics. For this, 250 research articles were retrieved from the Scopus and Web of Science databases. For the first evaluated period (2007-2011), there is a greater use of ANN models for the forecasting of wind energy. In the second period (2012-2016), the forecast interval approaches based on ANNs are used. Finally, in the third period (2017-2021), hybrid approaches are proposed for forecasting wind energy using ANN models and other approaches. Therefore, the results indicate that this field of research is constantly evolving, without yet reaching its stage of scientific maturity. Furthermore, results show that renewable energy source is the basic and transversal cluster of the applicationofforecasting wind models.

Título traducido de la contribución	Artificial Neural Networks in Forecasting Wind Energy: A Bibliometric Approach from 2007 to 2021
Idioma original	Español
Título de la publicación alojada	20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojada	"Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022
Editores	Maria M. Larrondo Petrie, Jose Texier, Andrea Pena, Jose Angel Sanchez Viloria
ISBN (versión digital)	9786289520705
DOI	https://doi.org/10.18687/LACCEI2022.1.1.85
Estado	Publicada - 2022
Publicado de forma externa	Sí
Evento	20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022 - Boca Raton, Estados Unidos Duración: 18 jul. 2022 → 22 jul. 2022

Serie de la publicación

Nombre	Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen	2022-July
ISSN (versión digital)	2414-6390

Conferencia

Conferencia	20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022
País/Territorio	Estados Unidos
Ciudad	Boca Raton
Período	18/07/22 → 22/07/22

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

Palabras clave

Energy forecast
SciMat
artificial neural networks (ANN)
bibliometric analysis
wind energy

Acceder al documento

10.18687/LACCEI2022.1.1.85

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Enlace a la publicación en Scopus

Citar esto

Zarate-Perez, E., Grados, J., Rubiños, S., Meza, J., Ortega-Rojas, Y., Grados-Espinoza, H., & Rojas, A. (2022). Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021. En M. M. Larrondo Petrie, J. Texier, A. Pena, & J. A. S. Viloria (Eds.), 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022 (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2022-July). https://doi.org/10.18687/LACCEI2022.1.1.85

Zarate-Perez, Eliseo ; Grados, Juan ; Rubiños, Santiago et al. / Redes Neuronales Artificiales en la Previsión de la Energía Eólica : Un Enfoque Bibliométrico de 2007 a 2021. 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022. editor / Maria M. Larrondo Petrie ; Jose Texier ; Andrea Pena ; Jose Angel Sanchez Viloria. 2022. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

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title = "Redes Neuronales Artificiales en la Previsi{\'o}n de la Energ{\'i}a E{\'o}lica: Un Enfoque Bibliom{\'e}trico de 2007 a 2021",

abstract = "The main objective of this study was to develop an evolutionary analysis of artificial neural networks (ANN) in the forecast of wind energy for the period from 2007 to 2021. The SciMat software tool was used to identify the performance and impact measures of the main research topics. For this, 250 research articles were retrieved from the Scopus and Web of Science databases. For the first evaluated period (2007-2011), there is a greater use of ANN models for the forecasting of wind energy. In the second period (2012-2016), the forecast interval approaches based on ANNs are used. Finally, in the third period (2017-2021), hybrid approaches are proposed for forecasting wind energy using ANN models and other approaches. Therefore, the results indicate that this field of research is constantly evolving, without yet reaching its stage of scientific maturity. Furthermore, results show that renewable energy source is the basic and transversal cluster of the applicationofforecasting wind models.",

keywords = "Energy forecast, SciMat, artificial neural networks (ANN), bibliometric analysis, wind energy",

author = "Eliseo Zarate-Perez and Juan Grados and Santiago Rubi{\~n}os and Jessica Meza and Yesmi Ortega-Rojas and Herbert Grados-Espinoza and Arcelia Rojas",

note = "Publisher Copyright: {\textcopyright} 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.; 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022 ; Conference date: 18-07-2022 Through 22-07-2022",

year = "2022",

doi = "10.18687/LACCEI2022.1.1.85",

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Zarate-Perez, E, Grados, J, Rubiños, S, Meza, J, Ortega-Rojas, Y, Grados-Espinoza, H & Rojas, A 2022, Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021. En MM Larrondo Petrie, J Texier, A Pena & JAS Viloria (eds.), 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, vol. 2022-July, 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022, Boca Raton, Estados Unidos, 18/07/22. https://doi.org/10.18687/LACCEI2022.1.1.85

Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021. / Zarate-Perez, Eliseo; Grados, Juan; Rubiños, Santiago et al.
20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022. ed. / Maria M. Larrondo Petrie; Jose Texier; Andrea Pena; Jose Angel Sanchez Viloria. 2022. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2022-July).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Redes Neuronales Artificiales en la Previsión de la Energía Eólica

T2 - 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2022

AU - Zarate-Perez, Eliseo

AU - Grados, Juan

AU - Rubiños, Santiago

AU - Meza, Jessica

AU - Ortega-Rojas, Yesmi

AU - Grados-Espinoza, Herbert

AU - Rojas, Arcelia

PY - 2022

Y1 - 2022

N2 - The main objective of this study was to develop an evolutionary analysis of artificial neural networks (ANN) in the forecast of wind energy for the period from 2007 to 2021. The SciMat software tool was used to identify the performance and impact measures of the main research topics. For this, 250 research articles were retrieved from the Scopus and Web of Science databases. For the first evaluated period (2007-2011), there is a greater use of ANN models for the forecasting of wind energy. In the second period (2012-2016), the forecast interval approaches based on ANNs are used. Finally, in the third period (2017-2021), hybrid approaches are proposed for forecasting wind energy using ANN models and other approaches. Therefore, the results indicate that this field of research is constantly evolving, without yet reaching its stage of scientific maturity. Furthermore, results show that renewable energy source is the basic and transversal cluster of the applicationofforecasting wind models.

AB - The main objective of this study was to develop an evolutionary analysis of artificial neural networks (ANN) in the forecast of wind energy for the period from 2007 to 2021. The SciMat software tool was used to identify the performance and impact measures of the main research topics. For this, 250 research articles were retrieved from the Scopus and Web of Science databases. For the first evaluated period (2007-2011), there is a greater use of ANN models for the forecasting of wind energy. In the second period (2012-2016), the forecast interval approaches based on ANNs are used. Finally, in the third period (2017-2021), hybrid approaches are proposed for forecasting wind energy using ANN models and other approaches. Therefore, the results indicate that this field of research is constantly evolving, without yet reaching its stage of scientific maturity. Furthermore, results show that renewable energy source is the basic and transversal cluster of the applicationofforecasting wind models.

KW - Energy forecast

KW - SciMat

KW - artificial neural networks (ANN)

KW - bibliometric analysis

KW - wind energy

UR - https://www.scopus.com/pages/publications/85140035524

U2 - 10.18687/LACCEI2022.1.1.85

DO - 10.18687/LACCEI2022.1.1.85

M3 - Contribución a la conferencia

AN - SCOPUS:85140035524

T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology

BT - 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology

A2 - Larrondo Petrie, Maria M.

A2 - Texier, Jose

A2 - Pena, Andrea

A2 - Viloria, Jose Angel Sanchez

Y2 - 18 July 2022 through 22 July 2022

ER -

Zarate-Perez E, Grados J, Rubiños S, Meza J, Ortega-Rojas Y, Grados-Espinoza H et al. Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021. En Larrondo Petrie MM, Texier J, Pena A, Viloria JAS, editores, 20th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Education, Research and Leadership in Post-Pandemic Engineering: Resilient Inclusive and Sustainable Actions", LACCEI 2022. 2022. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). doi: 10.18687/LACCEI2022.1.1.85

Redes Neuronales Artificiales en la Previsión de la Energía Eólica: Un Enfoque Bibliométrico de 2007 a 2021

Resumen

Serie de la publicación

Conferencia

ODS de las Naciones Unidas

Palabras clave

Acceder al documento

Otros archivos y enlaces

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