Using Artificial Intelligence Models to Predict the Wind Power to be fed into the Grid
by Sambalaye Diop 
, Papa Silly Traore , Mamadou Lamine Ndiaye and Issa Zerbo
¹Water, Energy, Environment, Industrial process (LE3PI) Polytechnic school of Dakar (ESP -UCAD) Dakar, Senegal
²Water, Energy, Environment, Industrial process (LE3PI) Polytechnic school of Dakar (ESP -UCAD) Dakar, Senegal
³Water, Energy, Environment, Industrial process (LE3PI) Polytechnic school of Dakar (ESP -UCAD) Dakar, Senegal
4Joseph KI-ZERBO University; Renewable Thermal Energy Laboratory (L.E.T.RE); Ouagadougou, Burkina Faso
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 9, Page # 1-9, 2024; DOI: 10.55708/js0306001
Keywords: Taïba Ndiaye, Wind power, SENELEC grid, forecast, machine learning, artificial intelligence models
Received: 05 April, 2024, Revised: 29 April, 2024, Accepted: 16 May, 2024, Published Online: 23 June, 2024
APA Style
Diop, S., Traore, P. S., Ndiaye, M. L., & Zerbo, I. (2024). Using artificial intelligence models to predict the wind power to be fed into the grid. Journal of Engineering Research and Sciences, 3(6), 1-09. https://doi.org/10.55708/js0306001
Chicago/Turabian Style
Diop, Sambalaye, Papa Silly Traore, Mamadou Lamine Ndiaye, and Issa Zerbo. “Using Artificial Intelligence Models to Predict the Wind Power to be Fed into the Grid.” Journal of Engineering Research and Sciences 3, no. 6 (2024): 1-09. https://doi.org/10.55708/js0306001.
IEEE Style
The Taïba Ndiaye wind farm, connected to the SENELEC grid, plays a key role in offsetting shortfalls in electricity consumption, with an installed capacity of 158.7 MW. Moreover, as an intermittent power station, its production is highly dependent on the environmental conditions in the region. Bad weather can disrupt the electricity network, requiring forecasting methods to anticipate its production. This will make it easier to decide how much fossil energy to bring on stream to meet demand. The aim of this paper is to provide forecasts of wind generation at Taïba Ndiaye, subdividing the data into 80% for model training and 20% to assess its robustness to generalization to other situations. The aim is to quantify the energy produced and facilitate an optimal transition between intermittent and fossil energy sources. Two artificial intelligence models classified as machine learning (decision tree and random forest) are proposed in the study, with respective coefficients of determination of 0.92 and 0.938. The results, compared with the literature, demonstrate the reliability of the approach using only production data. These results promise significant benefits in terms of resource management.
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