Open AccessStatic and Dynamic Evaluation of Wind Potential in the Kara Region of Togo Using Artificial Neural Networks
Author(s) -
Arafat Fousseni,
Koffi Mawugno Kodjo,
Assiongbon Adanlété Adjanoh
Publication year - 2021
Publication title -
th wildau engineering and natural sciences proceedings
Language(s) - English
Resource type - Journals
ISSN - 2748-8829
DOI - 10.52825/thwildauensp.v1i.26
Subject(s) - wind power , wind speed , weibull distribution , sigmoid function , artificial neural network , perceptron , turbine , energy (signal processing) , meteorology , environmental science , computer science , mean squared error , artificial intelligence , statistics , mathematics , engineering , geography , electrical engineering , mechanical engineering
Togo's energy situation is characterized by a low rate of access to electricity (38.07 % in 2017). In the Kara region, there is certainly a wind potential whose study is necessary for the production of electricity. Thus, from the data recorded each day at intervals of one hour, we used Weibull distribution to evaluate the wind energy potential at 10m and then at 25m, 50m, 75m and 100m. However, the promotion of this source requires not only the knowledge of its potential but also the evolution of its quantity over time because in reality wind energy is confronted with the random nature of the wind. Thus, for the prediction of the wind potential in the region of Kara, we used artificial neural networks. The neural architecture used is a multilayer perceptron with a single neuron under the hidden layer whose activation function is a sigmoid function while the output layer uses a linear function. The prediction results obtained with an average squared error of 0.005 and a correlation of 0.96 show that the prediction results using this tool are acceptable and can be generalized under the same conditions on other sites. The evaluation of the wind potential in the region of Kara has enabled us to determine the amount of total energy available in the wind at different altitudes. Through the average values of wind speeds determined, we could make an optimal choice of wind turbine to convert this kinetic energy of the wind into electrical energy.