A Hybrid Time Series Forecasting Method Based on Supervised Machine Learning Program

  • Ganesh Prasad Khuntia
  • Ritesh DashEmail author
  • Sarat Chandra Swain
  • Prashant Bawaney
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)


Clean and inexhaustible source of energy is the requirement of the entire world with respect to the present scenario. Among the different types of energy sources, wind energy is the cleanest energy and inexhaustible source of energy. In order to ensure the production of clean energy, it is required to forecast the level of wind energy from a day ahead. Forecasting of wind energy not only forecasts the level of wind but also predicts the type of wind energy, density, and other important variables. This paper describes the short-term forecasting based on Machine Learning algorithm. This paper compares the different Machine Learning Algorithm and its behavior in predicting or forecasting the day-ahead data for the wind energy system. Machine learning based on Python is formulated in this paper.


Wind energy Python Forecasting Training set Testing set 



The authors would like to thank the Department of Electrical Engineering, CCCET, Bhilai for providing necessary laboratory facilities during the entire research activities.


  1. 1.
    G.E. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung, Time series analysis: forecasting and control (Wiley, Hoboken, NJ, USA, 2015)zbMATHGoogle Scholar
  2. 2.
    C. Chatfield, The analysis of time series: an introduction (CRC Press, Boca Raton, FL, USA, 2016)zbMATHGoogle Scholar
  3. 3.
    D. Marelli, K. You, M. Fu, Identification of ARMA models using intermittent and quantized output observations. Automatica 49, 360–369 (2013). [CrossRef]MathSciNetCrossRefGoogle Scholar
  4. 4.
    G.P. Zhang, Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003). [CrossRef]CrossRefGoogle Scholar
  5. 5.
    M. Bigovi´c, Demand forecasting within Montenegrin tourism using Box-Jenkins methodology for seasonal ARIMA models. Tour. Hosp. Manag. 18, 1–18 (2012)Google Scholar
  6. 6.
    Z.J. Kovaˇci´c, Analiza Vremenskih Serija, vol. 47. Ekonomski Fakultet Beograd, Beograd, Serbia (1995). S. Haykin, Network, neural: a comprehensive foundation. Neural Netw. 2, 41 (2004)Google Scholar
  7. 7.
    Z. Olaofe, K. Folly, Wind power estimation using recurrent neural network technique, in Proceedings of the 2012 IEEE Power Engineering Society Conference and Exposition in Africa (PowerAfrica), Johannesburg, South Africa, 9–13 July 2012Google Scholar
  8. 8.
    A. De Pádua Braga, A.C.P. de Leon Ferreira, T.B Ludermir, Redes Neurais Artificiais: Teoria e Aplicações. LTC Editora, Rio de Janeiro, Brazil (2007)Google Scholar
  9. 9.
    A.H. Nury, K. Hasan, M.J.B. Alam, Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. J. King Saud Univ. Sci. 29, 47–61 (2017). [CrossRef]CrossRefGoogle Scholar
  10. 10.
    F. Bonanno, G. Capizzi, L. Sciuto, C. Napoli, Wavelet recurrent neural network with semi-parametric input data preprocessing for micro-wind power forecasting in integrated generation Systems, in Proceedings of the 2015 International Conference on Clean Electrical Power (ICCEP), Taormina, Italy, 16–18 June 2015Google Scholar
  11. 11.
    L. Wu, M. Shahidehpour, A hybrid model for day-ahead price forecasting. IEEE Trans. Power Syst. 25, 1519–1530 (2010)CrossRefGoogle Scholar
  12. 12.
    S. Schlüter, C. Deuschle, Using Wavelets for Time Series Forecasting: Does It Pay Off? IWQW Discussion Paper Series; No. 04/2010. University of Erlangen-Nuremberg, Erlangen, Germany (2010)Google Scholar
  13. 13.
    Atlas Do Potencial Eólico Brasileiro. Ministério de Minas e Energia (2001). Available online: Accessed on 23 Feb 2017
  14. 14.
    U. Firat, Wind speed forecasting based on second order blind identification and autoregressive model, in Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications (ICMLA), Washington, DC, USA, 12–14 Dec 2010Google Scholar
  15. 15.
    R. Adhikari, R. Agrawal, An Introductory Study on Time Series Modeling and Forecasting (Lambert Academic Publishing, Saarbrücken, Germany, 2013)Google Scholar
  16. 16.
    T. Raicharoen, C. Lursinsap, P Sanguanbhokai, Application of critical support vector machine to time series prediction, in Proceedings of the 2003 International Symposium on Circuits and Systems, Bangkok, Thailand, 25–28 May 2003Google Scholar
  17. 17.
    R.S. Ehlers, Análise de Séries Temporais (Universidade Federal do Paraná, Curitiba, Brazil, 2007)Google Scholar
  18. 18.
    P.A. Morettin, C. Toloi, Análise de Séries Temporais (São Paulo, Brazil, Editora Edgar Blucher, 2006)Google Scholar
  19. 19.
    D.N. Gujarati, D.C. Porter, Econometria Básica-5. Mc Grow Hill, New York, NY, USA (2011)Google Scholar
  20. 20.
    F.M. Souza, Modelos Box & Jenkins Aplicados a Previsão de Demanda de Leitos Hospitalares; Programa de Pós-Graduação em Estatística e Modelagem Quantitativa, Universidade Federal de Santa Maria (UFSM). Santa Maria, Brazil, (2006)Google Scholar
  21. 21.
    C.J. Figueredo, Previsão de Séries Temporais Utilizando a Metodologia Box & Jenkins e Redes Neurais para Inicialização de Planejamento e Controle de Produção. Master’s Thesis, Universidade Federal do Paraná, Curitiba, Brazil (2008)Google Scholar
  22. 22.
    K.W. Hipel, A.I. McLeod, Time Series Modelling of Water Resources and Environmental Systems, vol. 45. Elsevier, Amsterdam, The Netherlands (1994)Google Scholar
  23. 23.
    S. Makridakis, S.C. Wheelwright, R.J. Hyndman, Forecasting Methods and Applications (Wiley, Hoboken, NJ, USA, 2008)Google Scholar
  24. 24.
    J.J. Heckman, A. Serletis, Introduction to econometrics with theory: a special issue Honoring William A. Barnett. Econ. Rev. 34, 1–5 (2015). [CrossRef]MathSciNetCrossRefGoogle Scholar
  25. 25.
    M. Pokorny, An Introduction to Econometrics; Blackwell, Oxford, UK (1987). 38. H. Akaike, Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21, 243–247 (1969). [CrossRef] 39. D. Creal, S.J. Koopman, LucasGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ganesh Prasad Khuntia
    • 1
  • Ritesh Dash
    • 2
    Email author
  • Sarat Chandra Swain
    • 1
  • Prashant Bawaney
    • 2
  1. 1.School of Electrical EngineeringKIIT Deemed to be UniversityBhubaneswarIndia
  2. 2.CCETBhilaiIndia

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