Skip to main content
SpringerLink
Log in

A seasonal feedforward neural network to forecast electricity prices

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In power industry and management, given the peculiarity and high complexity of the time series, it is highly requested to make models more flexible and well adapted to the data, in order to give them the ability to capture and recognize the most complex patterns, and consequently gain more accuracy in forecasting. To fulfill this aim, we define the seasonal autoregressive neural network (SAR-NN) as a dynamic feedforward artificial neural network (ANN), essentially conceived to forecast electricity prices. The SAR-NN is an ANN-based autoregressive model that considers that autoregressors are only those that are lagged by a multiple of the period p of the dominating seasonality. Moving forward this neural network, step-by-step, allows to generate multiple-steps-ahead reliable forecasts. This model is specifically designed to robustly overcome the strong seasonal effects and the other nonlinear patterns that often harm ANNs forecasting performance. The strategy is tested and compared to a number of homologous models throughout empirical experiments. As a case study, we focus on the Nord Pool Scandinavian power market, which is one of the most mature energy markets worldwide.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. An activation function is deterministic and symmetrically nonlinear. The range of the output values of a feedforward model is controlled by N(.). If the output value is not restricted to particular intervals, such as binary or discrete results, then it can simply be set to an identity function.

  2. Denmark, Norway, Sweden and Finland.

References

  1. Chen K, Wang C (2007) A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Syst Appl 32(1):254–264

    Article  Google Scholar 

  2. Chaâbane N, Saâdaoui F, Benammou S (2012) Modelling power spot prices in deregulated European energy markets: a dual long memory approach. Global Busin Econ Rev 14(4):338–361

    Article  Google Scholar 

  3. de Lange AJP, Schavemaker PH, van der Sluis, L (2002) Electricity prices: stochastic or deterministic? Working Paper, ADL Consultancy, Netherlands

  4. Dudek G (2013) Forecasting time series with multiple seasonal cycles using neural networks with local learning. In: Artificial intelligence and soft computing, vol 7894 of the series. Lecture notes in computer science, pp 52–63

  5. Escribano A, Pena J, Villaplana P ( 2002) Modeling electricity prices: international evidence. Universidad Carlos III de Madrid, W.P. 02-27

  6. Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2(3):183–192

    Article  Google Scholar 

  7. Gould PG, Koehler AB, Ord JK, Snyder RD, Hyndman RJ, Vahid-Araghie F (2008) Forecasting time series with multiple seasonal patterns. Eur J Oper Res 191(1):207–222

    Article  MathSciNet  MATH  Google Scholar 

  8. Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251–257

    Article  MathSciNet  Google Scholar 

  9. Jablonska M, Kauranne T (2011) Multi-agent stochastic simulation for the electricity spot market price. Lect Notes Econ Math Syst 652:3–14

    Article  Google Scholar 

  10. Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236

    Article  Google Scholar 

  11. Kawauchi S, Sugihara H, Sasaki H (2004) Development of very-short-term load forecasting based on chaos theory. Electr Eng Jpn 148(2):646–653

    Article  Google Scholar 

  12. Kovacevic RM, Pflug GC, Vespucci MT (2013) Handbook of risk management in energy production and trading. Springer Science Business Media, New York

    Book  Google Scholar 

  13. Li L, Chong-Xin L (2011) Application of chaos and neural network in power load forecasting. Discrete Dyn Nat Soc 2011:1–12

    MATH  Google Scholar 

  14. Lütkepohl H, Krätzig M (2004) Applied time series econometrics. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  15. Nelson M, Hill T, Remus T, O’Connor M (1999) Time series forecasting using NNs: should the data be deseasonalized first? J Forecast 18(5):359–367

    Article  Google Scholar 

  16. Nogalas FJ, Contreras J, Conejo AJ, Espinola R (2002) Forecasting next day electricity prices by time series models. IEEE Trans Power Syst 17(2):342–348

    Article  Google Scholar 

  17. Norouzzadeh P, Dullaert W, Rahmani B (2007) Anti-correlation and multifractal features of Spain electricity spot market. Phys A Stat Mech Appl 380:333–342

    Article  Google Scholar 

  18. Nowotarski J, Tomczyk J, Weron R (2013) Robust estimation and forecasting of the long-term seasonal component of electricity spot prices. Energy Econ 39:13–27

    Article  Google Scholar 

  19. Olsson M, Soder L (2008) Modeling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Trans Power Syst 23(2):443–450

    Article  Google Scholar 

  20. Pai FP, Lin CS (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega 33(6):497–505

    Article  Google Scholar 

  21. Saâdaoui F (2010) Acceleration of the EM algorithm via extrapolation methods: review, comparison and new methods. Comput Stat Data Anal 54(3):750–766

    Article  MathSciNet  MATH  Google Scholar 

  22. Saâdaoui F (2013) The price and trading volume dynamics relationship in the EEX power market: a wavelet modeling. Comput Econ 42(1):47–69

    Article  Google Scholar 

  23. Saâdaoui F, Rabbouch H (2014) A wavelet-based multi-scale vector ANN model for econophysical systems prediction. Expert Syst Appl 41(13):6017–6028

    Article  Google Scholar 

  24. Szkuta B, Sanabria L, Dillon T (1999) Electricity price short-term forecasting using artificial neural networks. IEEE Trans Power Syst 14(13):851–857

    Article  Google Scholar 

  25. Tipping JP (2007) The analysis of spot price stochasticity in deregulated wholesale electricity markets. A thesis submitted in partial fulfillment of the requirements for the Degree of Ph.D. in Management Science in the University of Canterbury, New Zealand

  26. Unsihuay-Vila C, Zambroni de Souza AC, Marangon-Lima JW, Balestrassi PP (2010) Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model. Int J Electr Power Energy Syst 32(2):108–116

    Article  Google Scholar 

  27. Vijayalakshmi S, Girish GP (2015) Artificial neural networks for spot electricity price forecasting: a review. Int J Energy Econ Policy 5(4):1092–1097

    Google Scholar 

  28. Wang A, Ramsay B (1998) A neural network based estimator for electricity spot pricing with particular reference to weekend and public holidays. Neurocomputing 23(1–3):47–57

    Article  Google Scholar 

  29. Wang F, Liao GP, Li JH, Li XC, Zhou TJ (2013) Multifractal detrended fluctuation analysis for clustering structures of electricity price periods. Phys A Stat Mech Appl 392(22):5723–5734

    Article  Google Scholar 

  30. Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550–1560

    Article  Google Scholar 

  31. Weron R, Simonsen I, Wilman P (2004) Modeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market. In: Takayasu H (ed) The application of econophysics. Springer, Tokyo, pp 182–191

    Chapter  Google Scholar 

  32. Weron R (2014) Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int J Forecast 30(4):1030–1081

    Article  Google Scholar 

  33. Weron R (2006) Modeling and forecasting electricity loads and prices: a statistical approach. Wiley, Chichester

    Book  Google Scholar 

  34. Wong FS (1990) Time series forecasting using backpropagation neural networks. Neurocomputing 2(4):147–159

    Article  Google Scholar 

  35. Yang L, Tschernig R (2002) Non- and semiparametric identification of seasonal nonlinear autoregression models. Econom Theory 18(6):1408–1448

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Article  MATH  Google Scholar 

  37. Zhang GP, Qi M (2005) Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160(2):501–514

    Article  MATH  Google Scholar 

Download references

Acknowledgments

I would like to thank the anonymous reviewers for their insightful and constructive comments that greatly contributed to improving the paper. My many thanks go also to the editorial staff for their generous support and assistance during the review process.

Author information

Authors and Affiliations

  1. Saudi Electronic University, Prince Saud Bin Mohammed Bin Muqrin Road, Ar Rabi District, P.O. Box 93499, Riyadh, 11673, Saudi Arabia

    Foued Saâdaoui

Authors
  1. Foued Saâdaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Foued Saâdaoui.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saâdaoui, F. A seasonal feedforward neural network to forecast electricity prices. Neural Comput & Applic 28, 835–847 (2017). https://doi.org/10.1007/s00521-016-2356-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2356-y

Keywords

Search

Navigation