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Short-Term Load Forecasting Using Hybrid ARIMA and Artificial Neural Network Model

  • Rahul Singhal
  • Niraj Kumar Choudhary
  • Nitin SinghEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 587)

Abstract

Load forecasting is basic for building up a power supply strategy to enhance the reliability of the power line and gives optimal load scheduling to numerous developing nations where the demand can be expanded with high development rate. Short-Term Electric Load Forecast (STLF) is very important because it can be used to preserve optimum behaviour in daily operations of electrical system. For this purpose, Autoregressive Integrated Moving Average Model (ARIMA) is utilised which is a linear prediction procedure. Neural networks have capability to model complex and nonlinear relationship. The aim of this paper is to explain how neural network is able to change linear ARIMA model to create short-term load forecasts. The hybrid methodology, combining ARIMA and ANN model, will purposely take advantages of the unique power of ARIMA and ANN models in linear and nonlinear domains, respectively.

Keywords

Load forecasting ARIMA Artificial neural network Hybrid technique 

References

  1. 1.
    Alkhathami, M.: Introduction to Electric Load Forecasting Methods Received 1 April 2015; Accepted 9 May 2015; Published online 26 May 2015Google Scholar
  2. 2.
    Singh, A.K., Ibraheem, Khatoon, S., Muazzam, M., Chaturvedi, D.K.: Load Forecasting Techniques and Methodologies: A Review. In: 2012 2nd International Conference on Power, Control and Embedded SystemsGoogle Scholar
  3. 3.
    Gross, G., Galiana, F.D.: Short-term load forecasting. Proc. IEEE 75, 1558–1573 (1987)CrossRefGoogle Scholar
  4. 4.
    Kyriakides, E., Polycarpou, M.: Short Term Electric Load Forecasting: A Tutorial, vol. 35 of Studies in Computational Intelligence. Springer, Berlin, Heidelberg (2007)Google Scholar
  5. 5.
    Srivastava, A.K., Pandey, A.S., Singh, D.: Short-Term Load Forecasting Methods: A Review. IEEE. 978-1-5090-2118-5/16 ©2016Google Scholar
  6. 6.
    Montgomery, D.C., Jennings, C.L., Kulahci, Murat: Introduction to Time Series Analysis and Forecasting. Wiley-Interscience, Hoboken, NJ (2008)zbMATHGoogle Scholar
  7. 7.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken, NJ (2016)Google Scholar
  8. 8.
    de Andrade, L.C.M., da Silva, I.N.: Very short-term load forecasting based on ARIMA model and intelligent systems. IEEE. 978-1-4244-5098-5/09 ©2009Google Scholar
  9. 9.
    Singh, S., Singh, R.: ARIMA based short term load forecasting for Punjab region. Int. J. Sci. Res. (IJSR) 4(6) (2015). ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14|Impact Factor (2013): 4.438Google Scholar
  10. 10.
    Peng, T.M., Hubele, N.F., Karady, G.G: Advancement in the application of neural networks for short term load forecasting. IEEE Trans. Power Syst. 7, 250–257 (1992)CrossRefGoogle Scholar
  11. 11.
    Senjyu, T., Takara, H., Uezato, K., Funabashi, T.: One-hour-ahead forecasting using neural network. IEEE Trans. Power Syst. 17, 113–118 (2002)CrossRefGoogle Scholar
  12. 12.
    Fidalgo, J.N., Matos, M.A.: Forecasting portugal global load with artificial neural networks. In: Proceedings of the 17th International Conference on Artificial Neural Networks. Proceeding ICANN 2007, pp. 728–737 (2007)Google Scholar
  13. 13.
    Tsekouras, G.J., Hatziargyriou, N.D., Dialynas, E.N.: An optimized adaptive neural network for annual midterm energy forecasting. IEEE Trans. Power Syst. 21, 385–391 (2006)CrossRefGoogle Scholar
  14. 14.
    Peter Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model Received 16 July 1999; accepted 23 November 2001Google Scholar
  15. 15.
    EI Desouky, A.A., EI Kateb, M.M.: Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA. IET Proc.—Gener. Transm. Distrib. (2000)Google Scholar
  16. 16.
    Nguyen, H., Hansen, C.K.: Short-Term Electricity Load Forecasting with Time Series Analysis. IEEE. 978-1-5090-0382-2/16 ©2017Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rahul Singhal
    • 1
  • Niraj Kumar Choudhary
    • 1
  • Nitin Singh
    • 1
    Email author
  1. 1.Department of Electrical EngineeringMNNIT AllahabadPrayagrajIndia

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