Short-term wind speed prediction based on MLP and NARX network models

Yousra Amellas (University of Kenitra)
Outman El bakkali (University of Kenitra)
Abdelouahed Djebli (University of Tetouan)
Adil Echchelh (University of Kenitra)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 18, No 1: April 2020

The article aims to predict the wind speed by two artificial neural network’s models. The first model is a multilayer perceptron (MLP) treated by back-propagation algorithm and the second one is a recurrent neuron network type, processed by the NARX model. The two models having the same Network’s structure, which they are composed by 4 Inputs layers (Wind Speed, Pressure Temperature and Humidity), an intermediate layer defined by 20 neurons and an activation function, as well as a single output layer characterized by wind speed and a linear function. NARX shows the best results with a regression coefficient R = 0.984 et RMSE = 0.314.