Artificial Neural Networks in Forecasting Wind Energy: A Bibliometric Approach from 2007 to 2021

Zarate-Perez E.; Grados J.; Rubiños S.; Meza J.; Ortega-Rojas Y.; Grados-Espinoza H.; Rojas A. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2022) · DOI: 10.18687/laccei2022.1.1.85

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.

Instituciones: Universidad Nacional del Callao