Wind Speed Forecasting Using Statistical and Machine Learning Methods: A Case Study in the UAE

  • Khawla Al Dhaheri
  • Wei Lee Woon
  • Zeyar Aung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10691)


Wind energy is a source of sustainable energy which is developing very quickly all over the world. Forecasting wind speed is a global concern and a critical issue for wind power conversion systems as it has a great influence in the scheduling of power systems as well as on the dynamic control of wind turbines. In this research, we deploy and study four forecasting models in order to forecast wind speeds in the city of Abu Dhabi, United Arab Emirates (UAE). Two of these models are conventional statistical methods, namely, (i) Auto Regression Integrated Moving Average (ARIMA) and (ii) Seasonal Auto Regression Integrated Moving Average (SARIMA) models, and the other two are drawn from the field of machine learning, namely, (i) Artificial Neural Networks (ANN) and (ii) Singular Spectrum Analysis (SSA) models. We compare the performances of these four models in order to determine the model which is most effective for forecasting wind speed data. The results show that the forecasting model SSA provides, on average, the most accurate forecasted values compared to the other three models. However, those three models, ARIMA, SARIMA and ANN, offer better results for the first few hours (around 24 h), which indicates that ARIMA, SARIMA, and ANN models are suitable for short-term forecasting, while SSA is suitable for long-term forecasting. The findings of our research could contribute in defining the fitting forecasting model in terms of short-term forecasting or long-term forecasting.


Wind speed Forecasting Statistical methods Machine learning 



The authors would like to acknowledge and appreciate the support of Professor Taha Ouarda in providing the Abu Dhabi wind data set, and also thank Dr. Aamna Mohammed Al Shehhi for her support and assistance with the statistical models.


  1. 1.
    Perera, K.S., Aung, Z., Woon, W.L.: Machine learning techniques for supporting renewable energy generation and integration: a survey. In: Woon, W.L., Aung, Z., Madnick, S. (eds.) DARE 2014. LNCS (LNAI), vol. 8817, pp. 81–96. Springer, Cham (2014). Google Scholar
  2. 2.
    Al Dhaheri, K.M.A.M.: Forecasting wind speed in Abu Dhabi using statistical modelling and machine learning approaches. Master’s thesis, Masdar Institute of Science and Technology, Abu Dhabi, UAE (2016)Google Scholar
  3. 3.
    Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications: with R Examples, 3rd edn. Springer, New York (2011)CrossRefMATHGoogle Scholar
  4. 4.
    Montgomery, D.C., Jennings, C.L., Kulahci, M.: Introduction to Time Series Analysis and Forecasting, 1st edn. Wiley, Somerset (2008)MATHGoogle Scholar
  5. 5.
    Wei, W.W.S.: Time Series Analysis - Univariate and Multivariate Methods, 2nd edn. Pearson Addison Wesley, Boston (2006)MATHGoogle Scholar
  6. 6.
    Palomares-Salas, J.C., et al.: Comparison of models for wind speed forecasting. In: Proceedings of the 9th International Conference on Computational Science, pp. 1–5 (2009)Google Scholar
  7. 7.
    Liu, H., Tian, H.Q., Li, Y.F.: Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl. Energy 98, 415–424 (2012)CrossRefGoogle Scholar
  8. 8.
    Yunus, K., Thiringer, T., Chen, P.: ARIMA-based frequency-decomposed modeling of wind speed time series. IEEE Trans. Power Syst. 31, 2546–2556 (2016)CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Wei, Y., Tan, Z.F., Wang, K., Tian, W.: A hybrid method for short-term wind speed forecasting. Sustainability 9 (2017). Article number 596Google Scholar
  10. 10.
    Chen, K.Y., Wang, C.H.: A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Syst. Appl. 32, 254–264 (2007)CrossRefGoogle Scholar
  11. 11.
    Ruiz-Aguilar, J.J., Turias, I.J., Jiménez-Come, M.J.: Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting. Transp. Res. Part E Log. Transp. Rev. 67, 1–13 (2014)CrossRefGoogle Scholar
  12. 12.
    Gocheva-Ilieva, S.G., Ivanov, A.V., Voynikova, D.S., Boyadzhiev, D.T.: Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach. Stochast. Environ. Res. Risk Assess. 28, 1045–1060 (2014)CrossRefGoogle Scholar
  13. 13.
    Jeong, K., Koo, C., Hong, T.: An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network). Energy 71, 71–79 (2014)CrossRefGoogle Scholar
  14. 14.
    Bontempi, G., Ben Taieb, S., Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  15. 15.
    Khashei, M., Bijari, M.: An artificial neural network (\(p\), \(d\), \(q\)) model for timeseries forecasting. Expert Syst. Appl. 37, 479–489 (2010)CrossRefMATHGoogle Scholar
  16. 16.
    Cadenas, E., Rivera, W.: Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renew. Energy 34, 274–278 (2009)CrossRefGoogle Scholar
  17. 17.
    Fadare, D.A.: The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria. Appl. Energy 87, 934–942 (2010)CrossRefGoogle Scholar
  18. 18.
    Filik, U.B., Filik, T.: Wind speed prediction using artificial neural networks based on multiple local measurements in Eskisehir. Energy Procedia 107, 264–269 (2017)CrossRefGoogle Scholar
  19. 19.
    Allende, H., Moraga, C., Salas, R.: Artificial neural networks in time series forecasting: a comparative analysis. Kybernetika 38, 685–707 (2002)MATHMathSciNetGoogle Scholar
  20. 20.
    Samsudin, R., Shabri, A., Saad, P.: A comparison of time series forecasting using support vector machine and artificial neural network model. J. Appl. Sci. 10, 950–958 (2010)CrossRefGoogle Scholar
  21. 21.
    Mohammed, A.A., Aung, Z.: Ensemble learning approach for probabilistic forecasting of solar power generation. Energies 9 (2016). Article number 1017Google Scholar
  22. 22.
    Neupane, B., Perera, K.S., Aung, Z., Woon, W.L.: Artificial neural network-based electricity price forecasting for smart grid deployment. In: Proceedings of the 2012 IEEE International Conference on Computer Systems and Industrial Informatics, pp. 1–6 (2012)Google Scholar
  23. 23.
    Rocco, S.C.M.: Singular spectrum analysis and forecasting of failure time series. Reliability Eng. Syst. Safety 114, 126–136 (2013)CrossRefGoogle Scholar
  24. 24.
    Afshar, K., Bigdeli, N.: Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA). Energy 36, 2620–2627 (2011)CrossRefGoogle Scholar
  25. 25.
    Hassani, H., Thomakos, D.: A review on singular spectrum analysis for economic and financial time series. Stat. Interface 3, 377–397 (2010)CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    Marques, C.A.F., et al.: Singular spectrum analysis and forecasting of hydrological time series. Phys. Chem. Earth Parts A/B/C 31, 1172–1179 (2006)CrossRefGoogle Scholar
  27. 27.
    Burnham, K.P., Anderson, D.R.: Multimodel inference: understanding AIC and BIC in model selection. Sociol. Meth. Res. 33, 261–304 (2004)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceKhalifa University of Science and Technology, Masdar InstituteAbu DhabiUAE

Personalised recommendations