Energy Efficiency

, Volume 12, Issue 8, pp 2099–2122 | Cite as

A hybrid approach to model and forecast the electricity consumption by NeuroWavelet and ARIMAX-GARCH models

  • Mehdi ZolfaghariEmail author
  • Bahram Sahabi
Original Article


Today, electrical energy plays a major role in production and consumption and is of special importance in economic decision-making process. Being aware of electrical energy demand for each period is necessary to correct planning. Therefore, the forecasting of electricity consumption is important among several economic sections. Besides the traditional models, in this paper, we offer a hybrid forecast approach that combines the adaptive wavelet neural network with the ARIMA-GARCH family models and uses the effective exogenous variables on electricity consumption. Based on this approach, two hybrid models are proposed. To assess the ability of the proposed models, we forecasted the daily electricity consumption by the hybrid and benchmark models for 60 days ahead in two separate seasons (summer and winter). The empirical results showed that the proposed models have more prediction accuracy compared with the other benchmark forecast models including neural network, adaptive wavelet neural network, and ARIMAX-GARCH family models.


Forecasting Electricity consumption ARIMAX-GARCH Adaptive wavelet neural Network wavelet Hybrid models 



  1. Abedinia, O., & Amjady, N. (2015). Day-ahead price forecasting of electricity markets by a new hybrid forecast method. Modeling and Simulation in Electrical and Electronics Engineering, 1(1), 1–7.Google Scholar
  2. Amjady, N., & Keynia, F. (2008). Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method. International Journal of Electrical Power & Energy Systems, 30(9), 533–546.CrossRefGoogle Scholar
  3. Bashir, Z. A., & El-Hawary, M. E. (2009). Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Transactions on Power Systems, 24(1), 20–27.CrossRefGoogle Scholar
  4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.MathSciNetzbMATHCrossRefGoogle Scholar
  5. Bouzari, H., Šramek, M., Mistelbauer, G., & Bouzari, E. (2011). Robust adaptive wavelet neural network control of Buck converters. In Recent advances in robust control-novel approaches and design methods. IntechOpen.Google Scholar
  6. Box, G. E., & Jenkins, G. M. (1976). Time series analysis: forecasting and control, revised ed. Holden-Day.Google Scholar
  7. Chaâbane, N. (2014). A hybrid ARFIMA and neural network model for electricity price prediction. International Journal of Electrical Power & Energy Systems, 55, 187–194.CrossRefGoogle Scholar
  8. Chand, S., Kamal, S., & Ali, I. (2012). Modelling and volatility analysis of share prices using ARCH and GARCH models. World Applied Sciences Journal, 19(1), 77–82.Google Scholar
  9. Chen, Y., Luh, P. B., Guan, C., Zhao, Y., Michel, L. D., Coolbeth, M. A., … & Rourke, S. J. (2010). Short-term load forecasting: similar day-based wavelet neural networks. IEEE Transactions on Power Systems, 25(1), 322–330.Google Scholar
  10. Chen, H., Wan, Q., & Wang, Y. (2014). Refined Diebold-Mariano test methods for the evaluation of wind power forecasting models. Energies, 7(7), 4185–4198.CrossRefGoogle Scholar
  11. Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005). Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power Systems, 20(2), 1035–1042.CrossRefGoogle Scholar
  12. Ding, S., Hipel, K. W., & Dang, Y. G. (2018). Forecasting China’s electricity consumption using a new grey prediction model. Energy, 149, 314–328.CrossRefGoogle Scholar
  13. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50, 987–1007.MathSciNetzbMATHCrossRefGoogle Scholar
  14. Erdogdu, E. (2010). Natural gas demand in Turkey. Applied Energy, 87(1), 211–219.CrossRefGoogle Scholar
  15. Hickey, E., Loomis, D. G., & Mohammadi, H. (2012). Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs. Energy Economics, 34(1), 307–315.CrossRefGoogle Scholar
  16. Hong, T. (2010). Short term electric load forecasting.Google Scholar
  17. Hong, T., Pinson, P., & Fan, S. (2014). Global energy forecasting competition 2012.Google Scholar
  18. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259.MathSciNetCrossRefGoogle Scholar
  19. Kavaklioglu, K., Ceylan, H., Ozturk, H. K., & Canyurt, O. E. (2009). Modeling and prediction of Turkey’s electricity consumption using artificial neural networks. Energy Conversion and Management, 50(11), 2719–2727.CrossRefGoogle Scholar
  20. Kim, K. H., Park, J. K., Hwang, K. J., & Kim, S. H. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems, 10(3), 1534–1539.CrossRefGoogle Scholar
  21. Kristjanpoller, W., & Minutolo, M. C. (2018). A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert Systems with Applications, 109, 1–11.CrossRefGoogle Scholar
  22. Liu, H., & Shi, J. (2013). Applying ARMA–GARCH approaches to forecasting short-term electricity prices. Energy Economics, 37, 152–166.MathSciNetCrossRefGoogle Scholar
  23. Mandal, P., Haque, A. U., Meng, J., Srivastava, A. K., & Martinez, R. (2013). A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting. IEEE Transactions on Power Systems, 28(2), 1041–1051.CrossRefGoogle Scholar
  24. May, R., Dandy, G., & Maier, H. (2011). Review of input variable selection methods for artificial neural networks. In Artificial neural networks-methodological advances and biomedical applications. InTech.Google Scholar
  25. Morales-Acevedo, A. (2014). Forecasting future energy demand: electrical energy in Mexico as an example case. Energy Procedia, 57, 782–790.CrossRefGoogle Scholar
  26. Nury, A. H., Hasan, K., & Alam, M. J. B. (2017). Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. Journal of King Saud University-Science, 29(1), 47–61.CrossRefGoogle Scholar
  27. Pindoriya, N. M., Singh, S. N., & Singh, S. K. (2008). An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Transactions on Power Systems, 23(3), 1423–1432.CrossRefGoogle Scholar
  28. Pindoriya, N. M., Singh, S. N., & Singh, S. K. (2010). Forecasting of short-term electric load using application of wavelets with feed-forward neural networks. International Journal of Emerging Electric Power Systems, 11(1).Google Scholar
  29. Rahman, S., & Hazim, O. (1993). A generalized knowledge-based short-term load-forecasting technique. IEEE Transactions on Power Systems, 8(2), 508–514.CrossRefGoogle Scholar
  30. Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118–132.CrossRefGoogle Scholar
  31. Reis, A. R., & Da Silva, A. A. (2005). Feature extraction via multiresolution analysis for short-term load forecasting. IEEE Transactions on Power Systems, 20(1), 189–198.CrossRefGoogle Scholar
  32. Reston Filho, J. C., Affonso, C. D. M., & de Oliveira, R. C. (2014). Energy price prediction multi-step ahead using hybrid model in the Brazilian market. Electric Power Systems Research, 117, 115–122.CrossRefGoogle Scholar
  33. Sánchez, J. M. B., Lugilde, D. N., de Linares Fernández, C., de la Guardia, C. D., & Sánchez, F. A. (2007). Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications, 32(4), 1218–1225.CrossRefGoogle Scholar
  34. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. Scholar
  35. Shafie-Khah, M., Moghaddam, M. P., & Sheikh-El-Eslami, M. K. (2011). Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Conversion and Management, 52(5), 2165–2169.CrossRefGoogle Scholar
  36. Sumer, K. K., Goktas, O., & Hepsag, A. (2009). The application of seasonal latent variable in forecasting electricity demand as an alternative method. Energy Policy, 37(4), 1317–1322.CrossRefGoogle Scholar
  37. Szoplik, J. (2015). Forecasting of natural gas consumption with artificial neural networks. Energy, 85, 208–220.CrossRefGoogle Scholar
  38. Tan, Z., Zhang, J., Wang, J., & Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Applied Energy, 87(11), 3606–3610.CrossRefGoogle Scholar
  39. Thomas, S., & Mitchell, H. (2005). GARCH modeling of high-frequency volatility in Australia’s National Electricity Market. System, 1–39.Google Scholar
  40. Valenzuela, O., Rojas, I., Rojas, F., Pomares, H., Herrera, L. J., Guillén, A., … & Pasadas, M. (2008). Hybridization of intelligent techniques and ARIMA models for time series prediction. Fuzzy Sets and Systems, 159(7), 821–845.Google Scholar
  41. Wu, L., & Shahidehpour, M. (2010). A hybrid model for day-ahead price forecasting. IEEE Transactions on Power Systems, 25(3), 1519–1530.CrossRefGoogle Scholar
  42. Yu, H., & Wilamowski, B. M. (2011). Chapter 12. In Levenberg–Marquardt training industrial electronics handbook (pp. 12–11). CRC.Google Scholar
  43. Zhai, M.-Y. (2015). A new method for short-term load forecasting based on fractal interpretation and wavelet analysis. International Journal of Electrical Power & Energy Systems, 69, 241–245.CrossRefGoogle Scholar
  44. Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.zbMATHCrossRefGoogle Scholar
  45. Zhang, J., Tan, Z., & Yang, S. (2012). Day-ahead electricity price forecasting by a new hybrid method. Computers & Industrial Engineering, 63(3), 695–701.CrossRefGoogle Scholar
  46. Zolfaghari, M., & Sahabi, B. (2017). Impact of foreign exchange rate on oil companies risk in stock market: a Markov-switching approach. Journal of Computational and Applied Mathematics, 317, 274–289.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Faculty of Management and EconomicsTarbiat Modares UniversityTehranIran

Personalised recommendations