Neuron Network Prediction Feed-For wad Wind Speed Network on Mauritania’s North Coast: Ballawack Case
Soukeyna Mohamed¹, Diene Ndiaye², Sidi Mohamed Mustapha³, Abdel Kader Mahmoud^4
1Soukeyna Mohamed, Assistant Professor, Department of physic Nouakchott, (Applied Research Laboratory for Renewable Energies (LRAER), University de Nouakchott, Al-Aasriya (UNA), Mauritanie.
²Diene Ndiaye, Associate Professor, Department of physic, saint-louis, (Laboratory of Electronic, Computing, Telecommunication and Renewable Energies (LEITER), UGB, Saint-Louis, Senegal.), Senegal.
³Sidi Mohamed Mustapha, Assistant Professor, Department of physic Nouakchott, (Applied Research Laboratory for Renewable Energies (LRAER), University de Nouakchott, Al-Aasriya (UNA), Mauritanie.
⁴Abdel Kader Mahmoud, Assistant Professor, Department of physic Nouakchott, (Applied Research Laboratory for Renewable Energies (LRAER), University de Nouakchott, Al-Aasriya (UNA), Mauritanie.
Manuscript received on September 16, 2020. | Revised Manuscript received on September 24, 2020. | Manuscript published on October 10, 2020. | PP: 369-377 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.K78530991120 | DOI: 10.35940/ijitee.K7853.1091220
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Abstract: The assessment of the suitability of a wind system depends largely on the prediction of the wind potential. Indeed, the variability and uncertainty inherent in renewable energy sources can have a significant impact on accurate and reliable prediction of the power produced. Wind sources are needed at different time stages and at different altitudes. Thus, putting in place tools for predicting these wind resources is essential for their effective integration in the frame of electricity generation. In this context, the paper of this study is to propose a short-term wind energy prediction method through the formation of historical wind velocity data based on neural networks. This assessment involves modelling wind speed using ANN through the feed-forwad network. So, ANN are at the basis of adaptive identification methods and intelligent command laws. In this sense, first, the process of forecasting wind energy involves the creation of a raw data base, which is then filtered by probabilistic neural network. More concretely, the contribution of the work can be given in the form of technical results. These results start with a proposal of the theoretical models, then it is given the approach method that is used, then it is proposed the design of the system and the whole is closed by a performance evaluation. As far as performance evaluation is concerned, it is presented in the form of the results of analysed simulations of the forecast model. In practical terms, it should be noted that the proposed model also provides a high degree of accuracy for the measured data. In the end, normalized average absolute errors were recorded between 4.7% and 4.9%. As, it was found a regression factor R (measures the correlation between output-Target) between 91% and 96% for the site of the northern Mauritanian coast. This is largely acceptable for similar calculations.
Keywords: Artificial Neural Networks (ANN), MATLAB, Mathematical model, Mauritanian north coast, Wind speed prediction, Wind power prediction Abbreviations: ANN, Artificial Neural Networks; GA, Genetic Algorithm.
Scope of the Article: Artificial Neural Networks