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
²Joseph 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.
- W. Zappa, M. Junginger, and M. van den Broek, “Is a 100% renewable European power system feasible by 2050?” Applied Energy, vol. 233–234, pp. 1027–1050, 2019.
- A. Jamil, M. Imran, S. H. Ahmed, and A. Anpalagan, “Challenges and opportunities in wind power integration,” IEEE Access, vol. 6, pp. 63211-63233, 2018.
- A. Fischer, L. Montuelle, M. Mougeot, and D. Picard, “Statistical learning for wind power: A modeling and stability study towards forecasting,” Wind Energy, vol. 20, no. 12, pp. 2037-2047, 2017.
- M. Dieng, A. Ndoye, M. S. Sow, and A. Wane, “Energy consumption forecasting using artificial neural networks: A case study of Senegal,” 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 2015, pp. 2432-2437.
- K. Jha, & A. G. Shaik, “A comprehensive review of power quality mitigation in the scenario of solar PV integration into utility grid,” E-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 3, 100103, 2023. https://doi.org/10.1016/j.prime.2022.100103.
- A.S. Ba, (2018). “The energy policy of the Republic of Senegal.”.
- S. Diop, P. S. Traore, & M. L. Ndiaye, “Power and Solar Energy Predictions Based on Neural Networks and Principal Component Analysis with Meteorological Parameters of Two Different Cities: Case of Diass and Taïba Ndiaye,” 2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), vol. 4, pp. 1-6, IEEE, October 2022.
- S. Diop, P. S. Traore, and M. L. Ndiaye, “Wind Power Forecasting Using Machine Learning Algorithms,” 2021 9th International Renewable and Sustainable Energy Conference (IRSEC), 2021, pp. 1-6, doi: 10.1109/IRSEC53969.2021.9741109.
- M. Shafiullah, S. D. Ahmed, & F. A. Al-Sulaiman, “Grid Integration Challenges and Solution Strategies for Solar PV Systems: A Review,” IEEE Access, vol. 10, pp. 52233–52257, 2022. https://doi.org/10.1109/ACCESS.2022.3174555.
- A. Kaur, L. Nonnenmacher, and C. F. M. Coimbra, “Net load forecasting for a hybrid renewable energy system based on receding horizon optimization,” Energy, vol. 150, pp. 617-630, May 2018.
- J. Maldonado-Correa, M. Valdiviezo, J. Solano, M. Rojas, and C. Samaniego-Ojeda, “Wind energy forecasting with artificial intelligence techniques: A review,” International Conference on Applied Technologies, December 2019, pp. 348-362. Springer, Cham.
- S. Noman, A. A. Roya, K. Lauri, N. I. Muhammad, and R. Argo, “Forecasting Short Term Wind Energy Generation using Machine Learning,” Conference Paper, October 2019, PSG 142. DOI: 10.1109/RTUCON48111.2019.8982365.
- T. Mahmoud, Z. Y. Dong, and J. Ma, “An advanced approach for optimal wind power generation prediction intervals by using self adaptive evolutionary extreme learning machine,” Renewable Energy, vol. 126, pp. 254–269, 2018.
- G. Notton, M. L. Nivet, C. Voyant, C. Paoli, C. Darras, F. Motte, and A. Fouilloy, “Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting,” Renewable and Sustainable Energy Reviews, vol. 87, pp. 96-105, 2018.
- Y.-J. Ma, & M.-Y. Zhai, “A Dual-Step Integrated Machine Learning Model for 24h-Ahead Wind Energy Generation Prediction Based on Actual Measurement Data and Environmental Factors,” Applied Sciences, vol. 9, no. 10, 2125, 2019. doi:10.3390/app9102125.
- W. M. Nkounga, M. F. Ndiaye, M. L. Ndiaye, O. Cisse, M. Bop, & A. Sioutas, “Short-term forecasting for solar irradiation based on the multi-layer neural network with the Levenberg-Marquardt algorithm and meteorological data: application to the Gandon site in Senegal,” 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018. doi:10.1109/icrera.2018.8566850.
- A. Clifton, L. Kilcher, J. K. Lundquist, & P. Fleming, “Using machine learning to predict wind turbine power output,” Environmental Research Letters, vol. 8, no. 2, 024009, 2013.
doi:10.1088/1748-9326/8/2/024009. - M. F. Ndiaye, “Supervision de système multi-sources d’énergie,” Thèse Doctoral, Université Cheikh Anta Diop de Dakar- École Supérieure Polytechnique, 2019.
- F. Pelletier, “Conception d’un site d’évaluation des performances d’éoliennes hors réseau en milieu complexe,” Doctoral dissertation, École de technologie supérieure, 2003.
- R. Baïle, “Analyse et modélisation multifractales de vitesses de vent. Application à la prévision de la ressource éolienne,” Doctoral dissertation, Université Pascal Paoli, 2010.
- S.A.A. Niang, M.S. Drame, A. Gueye, A. Sarr, M.D. Toure, D. Diop, S.O. Ndiaye, & K. Talla, “Temporal dynamics of energy production at the Taïba Ndiaye wind farm in Senegal,” Discov Energy, vol. 3, art. 6, 2023. https://doi.org/10.1007/s43937-023- 00018-0.
- T. R. Prajwala, “A Comparative Study on Decision Tree and Random Forest Using R Tool,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 1, January 2015.
- C. Voyant, G. Notton, S. Kalogirou, M. L. Nivet, C. Paoli, F. Motte, and A. Fouilloy, “Machine learning methods for solar radiation forecasting: A review,” Renewable Energy, vol. 105, pp. 569–582, 2017. doi: 10.1016/j.renene.2016.12.095.
- H. M. Diagne, M. David, P. Lauret, J. Boland, and N. Schmutz, “Review of solar irradiance forecasting methods and a proposition for small-scale insular grids,” Renewable and Sustainable Energy Reviews, vol. 27, pp. 65-76, 2013.
- T. Brahimi, “Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia,” Energies, vol. 12, no. 24, 4669, 2019. doi:10.3390/en12244669.
- E. F. Alsina, M. Bortolini, M. Gamberi, and A. Regattieri, “Artificial neural network optimization for monthly average daily global solar radiation prediction,” Energy Conversion and Management, vol. 120, pp. 320–329, July 2016.
