Analysis of Neural Networks Used by Artificial Intelligence in the Energy Transition with Renewable Energies

Abstract

Artificial neural networks (ANNs) have become key methods for achieving global climate goals. The aim of this review is to carry out a detailed analysis of the applications of ANNs to the energy transition all over the world. Thus, the applications of ANNs to renewable energies such as solar, wind, and tidal energy or for the prediction of greenhouse gas emissions were studied. This review was conducted through keyword searches and research of publishers and research platforms such as Science Direct, Research Gate, Google Scholar, IEEE Xplore, Taylor and Francis, and MDPI. The dates of the most recent research were 2018 for wind energy, 2022 for solar energy, 2021 for tidal energy, and 2021 for the prediction of greenhouse gas emissions. The results obtained were classified according to the type of structure and the architecture used, the inputs/outputs used, the region studied, the activation function used, and the algorithms used as the main methods for synthesizing the results. To carry out the present review, 96 investigations were used, and among them, the predominant structure was that of the multilayer perceptron, with Purelin and Sigmoid as the most used activation functions.