Short-term wind speed prediction based on MLP and NARX network models - Yousra Amellas; Outman El bakkali; Abdelouahed Djebli; Adil Echchelh (Institute of Advanced Engineering and Science, 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.