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
N1 - Publisher Copyright:
© 2022 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
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 - http://www.scopus.com/inward/record.url?scp=85140035524&partnerID=8YFLogxK
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 -
