Forecast of wind speed based on MLP network model using Levenberg Marquardt and gradient descent algorithms in Tetouan city, Northern Morocco

Abstract

This study aims to find the most powerful algorithm between LM and GD, applying them to the multilayer neural network (MLP) to predict the wind speed of the city of Tetouan. To achieve this we will use the meteorological data of this city from 31/07/2017 to 31/08/2022. The MLP adopted for our study is composed of two hidden layers, 30 neurons in the first layer and 15 in the second, 7 inputs and one output. The data is divided into 80% for training and 20% for testing. The results obtained showed that the Levenberg-Marquardt (LM) algorithm is more efficient than the gradient descent (GD) algorithm with a correlation coefficient R = 0.988102 and a mean square error MSE = 0. 0458. These results will allow us to accurately predict the wind speed of August for the year 2022 in this city.