Short-term electric load forecasting in Tunisia using artificial neural networks

  • Rim Houimli
  • Mourad Zmami
  • Ousama Ben-SalhaEmail author
Original Paper


The accuracy of short-term electricity load forecasting is of great interest since it allows avoiding unexpected blackouts and lowering operating costs. In this paper, we aim to implement the artificial neural networks to model and forecast the half-hourly electric load demand in Tunisia over the period 2000–2008. To improve the quality of forecasts, the proposed artificial neural network model uses not only past electric load values as inputs, but also climatic and calendar variables. To determine the optimal structure of the neural network model, this paper employs the pattern search algorithm. Moreover, the neural network model is equipped with the Levenberg–Marquardt learning algorithm. Our findings confirm the performance of this algorithm to the view of evaluation indicators since the mean absolute percentage error values range between 1.1 and 3.4%. The analysis also shows the superiority of the Levenberg–Marquardt algorithm compared to the resilient back propagation algorithm and the conjugate gradient algorithm. In the light of the current research, we stress the aptness of the proposed artificial neural network model in forecasting short-term electricity demand.


Short-term load forecasting Artificial neural network Levenberg–Marquardt algorithm Pattern search Tunisia 



The authors are grateful to the Editor-in-Chief, Professor Q.P. Zheng, and two anonymous referees for their constructive comments on earlier versions of the manuscript. They also acknowledge the Tunisian Company of Electricity and Gas for providing data used in this research.


  1. 1.
    Zhou, C., Chen, X.: China’s energy consumption prediction considering error correction based on decompose–ensemble method. Energy Syst. (2018). Google Scholar
  2. 2.
    Bunn, D., Farmer, E.: Economic and operational context of electric load prediction. In: Bunn, D., Farmer, E. (eds.) Comparative Models for Electrical Load Forecasting, pp. 3–11. Wiley, Hoboken (1985)Google Scholar
  3. 3.
    Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., Young II, W.A.: An overview of energy demand forecasting methods published in 2005–2015. Energy Syst. 8, 411–447 (2017)CrossRefGoogle Scholar
  4. 4.
    Hong, W.C.: Modeling for energy demand forecasting. In: Hong, W.C. (ed.) Intelligent Energy Demand Forecasting, pp. 21–40. Springer, New York (2013)CrossRefGoogle Scholar
  5. 5.
    Chow, T.W.S., Leung, C.T.: Neural networks based short term load forecasting using weather compensation. IEEE Trans. Power Syst. 11, 1736–1742 (1996)CrossRefGoogle Scholar
  6. 6.
    Taylor, J.W., Buizza, R.: Using weather ensemble predictions in electricity demand forecasting. Int. J. Forecast. 19, 57–70 (2003)CrossRefGoogle Scholar
  7. 7.
    Oliveira, M.O., Marzec, D.P., Bordin, G., Bretas, A.S., Bernaedon, D.: Climate change effect on very short-term electric load forecasting. Proceedings of the 2011 IEEE Trondheim PowerTech, pp. 944–950 (2011)Google Scholar
  8. 8.
    Hussain, A., Rahman, M., Memon, J.A.: Forecasting electricity consumption in Pakistan: the way forward. Energy Policy 90, 73–80 (2016)CrossRefGoogle Scholar
  9. 9.
    Taylor, J.W.: Short-term electricity demand forecasting using double seasonal exponential smoothing. J. Oper. Res. Soc. 54, 799–805 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Mbamalu, G.A.N., El-Hawary, M.E.: Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation. IEEE Trans. Power Syst. 8, 343–348 (1993)CrossRefGoogle Scholar
  11. 11.
    Clements, A.E., Hurn, A.S., Li, Z.: Forecasting day-ahead electricity load using a multiple equation time series approach. Eur. J. Oper. Res. 251, 522–530 (2016)CrossRefzbMATHGoogle Scholar
  12. 12.
    Weron, F.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30, 1030–1081 (2014)CrossRefGoogle Scholar
  13. 13.
    Hamzacebi, C.: Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy 35, 2009–2016 (2007)CrossRefGoogle Scholar
  14. 14.
    Dutta, G., Jha, P., Laha, A.K., Mohan, N.: Artificial neural network models for forecasting stock price index in the Bombay Stock Exchange. J. Emerg. Mark. Finance 5, 283–295 (2006)CrossRefGoogle Scholar
  15. 15.
    Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural Network models in stock market index prediction. Expert Syst. Appl. 38, 10389–10397 (2011)CrossRefGoogle Scholar
  16. 16.
    Nag, A.K., Mitra, A.: Forecasting daily foreign exchange rates using genetically optimized neural networks. J. Forecast. 21, 501–511 (2002)CrossRefGoogle Scholar
  17. 17.
    Panda, C., Narasimhan, V.: Forecasting exchange rate better with artificial neural network. J. Policy Model. 29, 227–236 (2007)CrossRefGoogle Scholar
  18. 18.
    Sehgal, N., Pandey, K.K.: Artificial intelligence methods for oil price forecasting: a review and evaluation. Energy Syst. 6, 479–506 (2015)CrossRefGoogle Scholar
  19. 19.
    Karimi, H., Dastranj, J.: Artificial neural network-based genetic algorithm to predict natural gas consumption. Energy Syst. 5, 571–581 (2014)CrossRefGoogle Scholar
  20. 20.
    Hsu, C.C., Chen, C.Y.: Regional load forecasting in Taiwan-Applications of artificial neural networks. Energy Convers. Manage. 44, 1941–1949 (2003)CrossRefGoogle Scholar
  21. 21.
    Mandal, P., Senjyu, T., Urasaki, N., Funabashi, T.: A neural network based several hour-ahead electric load forecasting using similar days approach. Int. J. Electr. Power Energy Syst. 28, 367–373 (2006)CrossRefGoogle Scholar
  22. 22.
    Kandananond, K.: Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach. Energies 4, 1246–1257 (2011)CrossRefGoogle Scholar
  23. 23.
    Gürbüz, F., Öztürk, C., Pardalos, P.: Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study. Energy Syst 4, 289–300 (2013)CrossRefGoogle Scholar
  24. 24.
    Panapakidis, I.P.: Application of hybrid computational intelligence models in short-term bus load forecasting. Expert Syst. Appl. 54, 105–120 (2016)CrossRefGoogle Scholar
  25. 25.
    Gonzalez-Romera, E., Jaramillo-Moran, M.A., Carmona-Fernandez, D.: Monthly electric energy demand forecasting based on trend extraction. IEEE Trans. Power Syst. 21, 1946–1953 (2006)CrossRefGoogle Scholar
  26. 26.
    Bakirtzis, A., Petridis, V., Klartzis, S., Alexiadis, M., Maissis, A.: A neural network short-term load forecasting model for the Greek power system. IEEE Trans. Power Syst. 11, 858–863 (1996)CrossRefGoogle Scholar
  27. 27.
    Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6, 442–449 (1991)CrossRefGoogle Scholar
  28. 28.
    Darbellay, G.A., Slama, M.: Forecasting the short-term demand for electricity—do neural networks stand a better chance? Int. J. Forecast. 16, 71–83 (2000)CrossRefGoogle Scholar
  29. 29.
    Khotanzad, A., Afkhami-Rohani, R., Lu, T.L., Abaye, A., Davis, M., Maratukulam, D.J.: ANNSTLF—a neural-network-based electric load forecasting system. IEEE Trans. Neural Netw. 8, 835–846 (1997)CrossRefGoogle Scholar
  30. 30.
    Hippert, H.S., Bunn, D.W., Souza, R.C.: Large neural networks for electricity load forecasting: are they overfitted? Int. J. Forecast. 21, 425–434 (2005)CrossRefGoogle Scholar
  31. 31.
    Jain, A., Satish, B.: Clustering based short term load forecasting using artificial neural network. IEEE/PES Power Syst. Conf. Expos. 10, 1109 (2009). Google Scholar
  32. 32.
    Al-Zayer, J., Al-Ibrahim, A.A.: Modeling the impact of temperature on electricity consumption in the Eastern Province of Saudi Arabia. J. Forecast. 15, 97–106 (1996)CrossRefGoogle Scholar
  33. 33.
    Liu, J.M., Chen, R., Liu, L.M., Harris, J.L.: A semi-parametric time series approach in modeling hourly electricity loads. J. Forecast. 25, 537–559 (2006)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Madić, J.M., Radovanović, M.R.: Optimal selection of ANN training and architectural parameters using taguchi method: a case study. FME Trans. 39, 79–86 (2011)Google Scholar
  35. 35.
    Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network Toolbox User’s Guide. The Mathworks Inc, Massachusetts (2015)Google Scholar
  36. 36.
    Amjady, N., Keynia, F.: A new neural network approach to short term load forecasting of electrical power systems. Energies 4, 488–503 (2011)CrossRefGoogle Scholar
  37. 37.
    Tanoto, Y., Ongsakul, W., Marpaung, C.O.P.: Levenberg–Marquardt recurrent networks for long term electricity peak load forecasting. Telkomnika 9, 257–266 (2011)CrossRefGoogle Scholar
  38. 38.
    Rodrigues, F., Cardeira, C., Calado, J.M.F.: The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Procedia 62, 220–229 (2014)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Tunis El ManarTunisTunisia
  2. 2.Northern Border UniversityArarSaudi Arabia
  3. 3.University of TunisTunisTunisia
  4. 4.University of SousseSousseTunisia
  5. 5.Economic Research ForumGizaEgypt

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