A brief comparison of different learning methods for wind speed forecasting

Obtaining clean energy through wind farms is considered viable because of its low operating cost. However, the wind speed behavior is not constant, it has a chaotic behavior, and it is highly data-dependent. The present work aims to carry out a comparison of several short-term wind forecasts using Artificial Intelligence models such as Artificial Neural Networks (ANN), Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), and Large Short-Term Memory Networks (LSTM). We discuss several scenarios where the models are contrasted, analyzing the advantages and disadvantages of using these strategies to decide the viability of building a wind farm in the analyzed place.