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Wind Power Prediction Using Artificial Neural Network Model: A Case Study

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Accelerating Discoveries in Data Science and Artificial Intelligence I (ICDSAI 2023)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 421))

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Abstract

Considering the high level of pollution that threatens our earth, energy from the wind represents a major alternative to fossil fuels, thanks to its high potential and large production. In order to benefit from this renewable energy, several methods of predicting the production of wind energy have been created and applied. In this article, a case study of wind energy forecasting for a Spanish wind installation in Sota Vento is realized. The employed methodology uses artificial neural networks based on a feed forward algorithm, a commonly applied method of artificial intelligence. The tool used is the NN-toolbox from MATLAB, and the model chosen is the Nonlinear autoregressive exogenous. After the model simulation in MATLAB, the results show that the determination coefficient R has a value close to 1 and the mean square error tends to 0. This confirms the average prediction of the model. For better performance, it is preferable to input more historical data and to combine ANNs with metaheuristic algorithms.

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References

  1. T. Haidi, B. Cheddadi, State of wind energy in the world: evolution, impacts and perspectives. Int. J. Technical Phys. Probl. Eng. 14(2), 6 (2022)

    Google Scholar 

  2. A. Tarraq, F. Elmariami, A. Belfqih, T. Haidi, Meta-heuristic optimization methods applied to renewable distributed generation planning: A review. E3S Web Conf. 234, 00086 (2021). https://doi.org/10.1051/e3sconf/202123400086

    Article  Google Scholar 

  3. T. Haidi, B. Cheddadi, Wind energy integration in Africa: Development, impacts and barriers. Int. J. Electr. Comput. Eng. IJECE 12(5), 4614 (2022). https://doi.org/10.11591/ijece.v12i5.pp4614-4622

    Article  Google Scholar 

  4. W.-Y. Chang, A literature review of wind forecasting methods. J. Power Energy Eng. 02(04), 161–168 (2014). https://doi.org/10.4236/jpee.2014.24023

    Article  Google Scholar 

  5. T. Haidi, B. Cheddadi, F. El Mariami, Z. El Idrissi, A. Tarrak, Wind energy development in Morocco: Evolution and impacts. Int. J. Electr. Comput. Eng. IJECE 11(4), 2811 (2021). https://doi.org/10.11591/ijece.v11i4.pp2811-2819

    Article  Google Scholar 

  6. D. Bouabdallaoui, T. Haidi, M. El Jaadi, Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction. Indones. J. Electr. Eng. Comput. Sci. 29(2), 626 (2023). https://doi.org/10.11591/ijeecs.v29.i2.pp626-634

    Article  Google Scholar 

  7. İ. Mert, C. Karakuş, F. Üneş, Estimating the energy production of the wind turbine using artificial neural network. Neural Comput. & Applic. 27(5), 1231–1244 (2016). https://doi.org/10.1007/s00521-015-1921-0

    Article  Google Scholar 

  8. A.S. Shekhawat, Wind power forecasting using artificial neural networks. Int. J. Eng. Res. 3(4) (2014)

    Google Scholar 

  9. H. Masrur, M. Nimol, M. Faisal, M.G. Sk, Mostafa, Short term wind speed forecasting using artificial neural network: A case study, in 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET), (IEEE, Dhaka, 2016), pp. 1–5. https://doi.org/10.1109/ICISET.2016.7856485

    Chapter  Google Scholar 

  10. V. Puri, N. Kumar, Wind energy forecasting using artificial neural network in himalayan region. Model. Earth Syst. Environ. 8(1), 59–68 (2022). https://doi.org/10.1007/s40808-020-01070-8

    Article  Google Scholar 

  11. M.N. Jyothi, V. Dinakar, Narx Based Short Term Wind Power Forecasting Model. 4(4) (2015)

    Google Scholar 

  12. M. Negnevitsky, P. Mandal, A.K. Srivastava, Machine learning applications for load, Price and wind power prediction in power systems, in 2009 15th International Conference on Intelligent System Applications to Power Systems, (IEEE, Curitiba, 2009), pp. 1–6. https://doi.org/10.1109/ISAP.2009.5352820

    Chapter  Google Scholar 

  13. S. Chai, Z. Xu, L.L. Lai, K.P. Wong, An overiew on wind power forcasting methods. Int. Conf. Mach. Learn. Cybern 2, 765–770 (2015). https://doi.org/10.1109/ICMLC.2015.7340651

    Article  Google Scholar 

  14. F. Pelletier, C. Masson, A. Tahan, Wind turbine power curve modelling using artificial neural network. Renew. Energy 89, 207–214 (2016). https://doi.org/10.1016/j.renene.2015.11.065

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory of Systems Engineering, Hassania School of Publics Works (EHTP), Casablanca, Morocco

    Doha Bouabdallaoui, Touria Haidi & Mounir Derri

  2. Laboratory of Electric Systems and Energy, National School of Electricity and Mechanics (ENSEM), Casablanca, Morocco

    Faissal Elmariami, Ali Tarraq & Meriem Majdoub

Authors
  1. Doha Bouabdallaoui
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  2. Touria Haidi
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  3. Faissal Elmariami
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  4. Mounir Derri
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  5. Ali Tarraq
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  6. Meriem Majdoub
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Corresponding author

Correspondence to Doha Bouabdallaoui .

Editor information

Editors and Affiliations

  1. Information & Decision Sciences, Jack H. Brown College of Business and Public Administration, California State University, San Bernardino, San Bernardino, CA, USA

    Frank M. Lin

  2. College of Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, USA

    Ashokkumar Patel

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  4. Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India

    Bosubabu Sambana

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Bouabdallaoui, D., Haidi, T., Elmariami, F., Derri, M., Tarraq, A., Majdoub, M. (2024). Wind Power Prediction Using Artificial Neural Network Model: A Case Study. In: Lin, F.M., Patel, A., Kesswani, N., Sambana, B. (eds) Accelerating Discoveries in Data Science and Artificial Intelligence I. ICDSAI 2023. Springer Proceedings in Mathematics & Statistics, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-031-51167-7_16

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