DOI: https://doi.org/10.61435/ijred.2025.61086

Multi-temporal forecasting of wind energy production using artificial intelligence models

Laboratory LAGES, Ecole Hassania des Travaux Publics (EHTP), Casablanca, 20000, Morocco

Received: 8 Jan 2025; Revised: 17 Mar 2025; Accepted: 29 Mar 2025; Available online: 4 Apr 2025; Published: 1 May 2025.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
In response to changing energy demands, electricity suppliers are increasingly turning to sustainable energy sources, with wind power emerging as a promising solution. This study aims to predict wind energy production over four time horizons: hourly, daily, weekly, and monthly, for a 12,300 kW wind farm located in Northamptonshire, UK. We employed three artificial intelligence (AI) techniques: an ensemble of bagged decision trees, artificial neural networks (ANNs), and support vector machines (SVMs). The paper provides a comparative evaluation of AI-based forecasting techniques for wind energy prediction, highlighting differences in model performance across time horizons while emphasizing the strengths and limitations of each method in addressing the temporal variability of wind energy production. The models were tested over various times using important performance measures, such as the correlation coefficient (R), the coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), and bias. The results indicate that support vector machines achieve the highest accuracy for medium-term forecasts, with a coefficient of determination of 0.9722 and a mean absolute error of 44.91 kW. Artificial neural networks perform best in short-term forecasting, particularly at the daily level, with a coefficient of determination of 0.948 and a mean absolute error of 36.04 kW. In contrast, long-term predictions exhibit greater variability across models, with the coefficient of determination decreasing to 0.778, reflecting the increased complexity of extended forecasting. The ensemble of bagged decision trees demonstrates strong predictive capability but with slightly higher error margins compared to support vector machines. The obtained results could serve as a reference for selecting the most suitable models based on forecasting objectives and time constraints. Future improvements in forecasting accuracy could happen by combining these models with optimization algorithms, especially for medium- and long-term predictions, where making accurate forecasts is still very difficult.
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Keywords: Artificial Neural Networks; Ensemble of bagged decision trees; Machine learning; Renewable Energy; Support Vector Machines; Wind Energy Forecasting.

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Section: Original Research Article
Language : EN
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  1. Abedinia, O., & Amjady, N. (2015). Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization. International Journal of Precision Engineering and Manufacturing-Green Technology, 2(3), Article 3. https://doi.org/10.1007/s40684-015-0029-4
  2. Abedinia, O., Bagheri, M., Naderi, M. S., & Ghadimi, N. (2020). A New Combinatory Approach for Wind Power Forecasting. IEEE Systems Journal, 14(3), 4614–4625. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2019.2961172
  3. Acikgoz, H., Yildiz, C., & Sekkeli, M. (2020). An extreme learning machine based very short-term wind power forecasting method for complex terrain. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(22), Article 22. https://doi.org/10.1080/15567036.2020.1755390
  4. Ahmadi, A., Nabipour, M., Mohammadi-Ivatloo, B., Amani, A. M., Rho, S., & Piran, Md. J. (2020). Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms. IEEE Access, 8, 151511–151522. https://doi.org/10.1109/ACCESS.2020.3017442
  5. Alghamdi, A. A., Ibrahim, A., El-Kenawy, E.-S. M., & Abdelhamid, A. A. (2023). Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm. Energies, 16(3), Article 3. https://doi.org/10.3390/en16031370
  6. Alvarez, L. F. J., Gonzalez, S. R., Lopez, A. D., Delgado, D. A. H., Espinosa, R., & Gutierrez, S. (2020). Renewable Energy Prediction through Machine Learning Algorithms. 2020 IEEE ANDESCON, 1–6. https://doi.org/10.1109/ANDESCON50619.2020.9272029
  7. Andrade, J. R., & Bessa, R. J. (2017). Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions. IEEE Transactions on Sustainable Energy, 8(4), 1571–1580. https://doi.org/10.1109/TSTE.2017.2694340
  8. Ateş, K. T. (2023). Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence. Sustainability, 15(18), Article 18. https://doi.org/10.3390/su151813572
  9. Bouabdallaoui, D., Haidi, T., & El Jaadi, M. (2023). Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction. Indonesian Journal of Electrical Engineering and Computer Science, 29(2), 626. https://doi.org/10.11591/ijeecs.v29.i2.pp626-634
  10. Bouabdallaoui, D., Haidi, T., Elmariami, F., Derri, M., & Mellouli, E. M. (2023). Application of four machine-learning methods to predict short-horizon wind energy. Global Energy Interconnection, 6(6), 726–737. https://doi.org/10.1016/j.gloei.2023.11.006
  11. Buturache, A.-N., & Stancu, S. (2021). Wind Energy Prediction Using Machine Learning. Low Carbon Economy, 12(01), 1–21. https://doi.org/10.4236/lce.2021.121001
  12. Cai, L., Gu, J., Ma, J., & Jin, Z. (2019). Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees. Energies, 12(1), 159. https://doi.org/10.3390/en12010159
  13. Cali, U., & Sharma, V. (2019). Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection. International Journal of Smart Grid and Clean Energy, 103–110. https://doi.org/10.12720/sgce.8.2.103-110
  14. Cumming, G. (2013). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge. https://doi.org/10.4324/9780203807002
  15. Gu, Q., Chang, Y., Li, X., Chang, Z., & Feng, Z. (2021). A novel F-SVM based on FOA for improving SVM performance. Expert Systems with Applications, 165, 113713. https://doi.org/10.1016/j.eswa.2020.113713
  16. Haidi, T., & Cheddadi, B. (2022a). State of wind energy in the world: Evolution, impacts and perspectives. International Journal on Technical and Physical Problems of Engineering, 14(2), 347-352
  17. Haidi, T., & Cheddadi, B. (2022b). Wind energy integration in Africa: Development, impacts and barriers. International Journal of Electrical and Computer Engineering (IJECE), 12(5), 4614. https://doi.org/10.11591/ijece.v12i5.pp4614-4622
  18. Hassoine, M. A., Lahlou, F., Addaim, A., & Madi, A. A. (2022). Improved Evaluation of The Wind Power Potential of a Large Offshore Wind Farm Using Four Analytical Wake Models. International Journal of Renewable Energy Development, 11(1), 35–48. https://doi.org/10.14710/ijred.2022.38263
  19. Heinermann, J., & Kramer, O. (2014). Precise Wind Power Prediction with SVM Ensemble Regression. In S. Wermter, C. Weber, W. Duch, T. Honkela, P. Koprinkova-Hristova, S. Magg, G. Palm, & A. E. P. Villa (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2014 (pp. 797–804). Springer International Publishing. https://doi.org/10.1007/978-3-319-11179-7_100
  20. Hu, S., Xiang, Y., Zhang, H., Xie, S., Li, J., Gu, C., Sun, W., & Liu, J. (2021). Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction. Applied Energy, 293, 116951. https://doi.org/10.1016/j.apenergy.2021.116951
  21. Idrissi, Z. E., Haidi, T., Elmariami, F., & Belfqih, A. (2022). Optimal coordination of docrs using GA with integration of DGS in distribution networks. International Journal on Technical and Physical Problems of Engineering , 14(1), https://www.iotpe.com/IJTPE/IJTPE-2022/IJTPE-Issue50-Vol14-No1-Mar2022/2-IJTPE-Issue50-Vol14-No1-Mar2022-pp10-17.pdf
  22. Jamii, J., Mansouri, M., Trabelsi, M., Mimouni, M. F., & Shatanawi, W. (2022). Effective artificial neural network-based wind power generation and load demand forecasting for optimum energy management. Frontiers in Energy Research, 10, 898413. https://doi.org/10.3389/fenrg.2022.898413
  23. Khan, M., He, C., Liu, T., & Ullah, F. (2021). A New Hybrid Approach of Clustering Based Probabilistic Decision Tree to Forecast Wind Power on Large Scales. Journal of Electrical Engineering & Technology, 16(2), 697–710. https://doi.org/10.1007/s42835-020-00616-1
  24. Khosravi, A., Koury, R. N. N., Machado, L., & Pabon, J. J. G. (2018). Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustainable Energy Technologies and Assessments, 25, 146–160. https://doi.org/10.1016/j.seta.2018.01.001
  25. Kumar, V., Pal, Y., & Tripathi, M. M. (2020). PSO-Tuned ANN-Based Prediction Technique for Penetration of Wind Power in Grid. In P. K. Singh, A. K. Kar, Y. Singh, M. H. Kolekar, & S. Tanwar (Eds.), Proceedings of ICRIC 2019 (pp. 485–494). Springer International Publishing. https://doi.org/10.1007/978-3-030-29407-6_35
  26. Kuriakose, A. M., Kariyalil, D. P., Augusthy, M., Sarath, S., Jacob, J., & Antony, N. R. (2020). Comparison of Artificial Neural Network, Linear Regression and Support Vector Machine for Prediction of Solar PV Power. 2020 IEEE Pune Section International Conference (PuneCon), 1–6. https://doi.org/10.1109/PuneCon50868.2020.9362442
  27. Li, Z., Luo, X., Liu, M., Cao, X., Du, S., & Sun, H. (2022). Wind power prediction based on EEMD-Tent-SSA-LS-SVM. Energy Reports, 8, 3234–3243. https://doi.org/10.1016/j.egyr.2022.02.150
  28. Lian, L., & He, K. (2022). Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine. Wind Engineering, 46(3), 866–885. https://doi.org/10.1177/0309524X211056822
  29. Mat Daut, M. A., Hassan, M. Y., Abdullah, H., Rahman, H. A., Abdullah, M. P., & Hussin, F. (2017). Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews, 70, 1108–1118. https://doi.org/10.1016/j.rser.2016.12.015
  30. Memarzadeh, G., & Keynia, F. (2020). A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Conversion and Management, 213, 112824. https://doi.org/10.1016/j.enconman.2020.112824
  31. Meng, A., Zhu, Z., Deng, W., Ou, Z., Lin, S., Wang, C., Xu, X., Wang, X., Yin, H., & Luo, J. (2022). A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine. Energy, 260, 124957. https://doi.org/10.1016/j.energy.2022.124957
  32. Miettinen, J., Holttinen, H., & Hodge, B.-M. (2020). Simulating wind power forecast error distributions for spatially aggregated wind power plants. Wind Energy, 23(1), 45–62. https://doi.org/10.1002/we.2410
  33. Pinto, T., Ramos, S., Sousa, T. M., & Vale, Z. (2014). Short-term wind speed forecasting using Support Vector Machines. 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 40–46. https://doi.org/10.1109/CIDUE.2014.7007865
  34. Plumley, C. (2022). Kelmarsh wind farm data (Version 0.0.3) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.5841834
  35. Soman, S. S., Zareipour, H., Malik, O., & Mandal, P. (2010). A review of wind power and wind speed forecasting methods with different time horizons. North American Power Symposium 2010, 1–8. https://doi.org/10.1109/NAPS.2010.5619586
  36. Taghinezhad, J., & Sheidaei, S. (2022). Prediction of operating parameters and output power of ducted wind turbine using artificial neural networks. Energy Reports, 8, 3085–3095. https://doi.org/10.1016/j.egyr.2022.02.065
  37. Taheri, S. M., & Hesamian, G. (2013). A generalization of the Wilcoxon signed-rank test and its applications. Statistical Papers, 54(2), 457–470. https://doi.org/10.1007/s00362-012-0443-4
  38. Tarek, Z., Shams, M., Elshewey, A., El-kenawy, E.-S., Ibrahim, A., Abdelhamid, A., & El-dosuky, M. (2023). Wind Power Prediction Based on Machine Learning and Deep Learning Models. Computers, Materials and Continua, 74, 715–732. https://doi.org/10.32604/cmc.2023.032533
  39. Torres-Barrán, A., Alonso, Á., & Dorronsoro, J. R. (2019). Regression tree ensembles for wind energy and solar radiation prediction. Neurocomputing, 326–327, 151–160. https://doi.org/10.1016/j.neucom.2017.05.104
  40. Tyass, I., Khalili, T., Rafik, M., Abdelouahed, B., Raihani, A., & Mansouri, K. (2023). Wind Speed Prediction Based on Statistical and Deep Learning Models. International Journal of Renewable Energy Development, 12(2), 288–299. https://doi.org/10.14710/ijred.2023.48672
  41. Verma, M., Ghritlahre, H. K., & Chandrakar, G. (2023). Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study. Annals of Data Science, 10(4), 851–873. https://doi.org/10.1007/s40745-021-00332-1
  42. Wang, Z., Li, G., Yao, L., Cai, Y., Lin, T., Zhang, J., & Dong, H. (2023). Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine. ISA Transactions, 138, 582–602. https://doi.org/10.1016/j.isatra.2023.03.022
  43. You, H., Bai, S., Wang, R., Li, Z., Xiang, S., & Huang, F. (2022). New PSO-SVM Short-Term Wind Power Forecasting Algorithm Based on the CEEMDAN Model. Journal of Electrical and Computer Engineering, 2022, 1–9. https://doi.org/10.1155/2022/7161445
  44. Yuan, X., Chen, C., Yuan, Y., Huang, Y., & Tan, Q. (2015). Short-term wind power prediction based on LSSVM–GSA model. Energy Conversion and Management, 101, 393–401. https://doi.org/10.1016/j.enconman.2015.05.065
  45. Zheng, J., Du, J., Wang, B., Klemeš, J. J., Liao, Q., & Liang, Y. (2023). A hybrid framework for forecasting power generation of multiple renewable energy sources. Renewable and Sustainable Energy Reviews, 172, 113046. https://doi.org/10.1016/j.rser.2022.113046
  46. Zheng, Y., Ge, Y., Muhsen, S., Wang, S., Elkamchouchi, D. H., Ali, E., & Ali, H. E. (2023). New ridge regression, artificial neural networks and support vector machine for wind speed prediction. Advances in Engineering Software, 179, 103426. https://doi.org/10.1016/j.advengsoft.2023.103426

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