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

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Date of Conference

July 18-22, 2022

Published In

"Education, Research and Leadership in Post-pandemic Engineering: Resilient, Inclusive and Sustainable Actions"

Location of Conference

Boca Raton

Authors

Zarate-Perez, Eliseo

Grados, Juan

Rubiños, Santiago

Meza, Jessica

Ortega-Rojas, Yesmi

Grados-Espinoza, Herbert

Rojas, Arcelia

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 application of forecasting wind models.

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