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Electricity Price Forecast for Futures Contracts with Artificial Neural Network and Spearman Data Correlation

  • João Nascimento
  • Tiago Pinto
  • Zita Vale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

Futures contracts are a valuable market option for electricity negotiating players, as they enable reducing the risk associated to the day-ahead market volatility. The price defined in these contracts is, however, itself subject to a degree of uncertainty; thereby turning price forecasting models into attractive assets for the involved players. This paper proposes a model for futures contracts price forecasting, using artificial neural networks. The proposed model is based on the results of a data analysis using the spearman rank correlation coefficient. From this analysis, the most relevant variables to be considered in the training process are identified. Results show that the proposed model for monthly average electricity price forecast is able to achieve very low forecasting errors.

Keywords

Artificial neural networks Electricity price Forecasting Futures contracts Spearman correlation 

References

  1. 1.
    Sioshansi, F.P.: Evolution of Global Electricity Markets: New Paradigms, New Challenges, New Approaches (2013)Google Scholar
  2. 2.
    Geng, Z., et al.: Electricity production scheduling under uncertainty: max social welfare vs. min emission vs. max renewable production. Appl. Energy 193, 540–549 (2017)CrossRefGoogle Scholar
  3. 3.
    Mohsenian-Rad, A.H., Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans. Smart Grid. 1, 120–133 (2010)CrossRefGoogle Scholar
  4. 4.
    Nowotarski, J., Weron, R.: Recent advances in electricity price forecasting: a review of probabilistic forecasting. Renew. Sustain. Energy Rev. 81, 1548–1568 (2018)CrossRefGoogle Scholar
  5. 5.
    Al-Musaylh, et al.: Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland. Australia. Adv. Eng. Informatics. 35, 1–16 (2018)CrossRefGoogle Scholar
  6. 6.
    Corrêa, J.M., Neto, A.C., Teixeira Júnior, L.A., Franco, E.M.C., Faria, A.E.: Time series forecasting with the WARIMAX-GARCH method. Neurocomputing 216, 805–815 (2016)CrossRefGoogle Scholar
  7. 7.
    Wang, S., et al.: Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew. Energy. 94, 629–636 (2016)CrossRefGoogle Scholar
  8. 8.
    Pinto, T., Sousa, T.M., Vale, Z.: Dynamic artificial neural network for electricity market prices forecast (2012)Google Scholar
  9. 9.
    MIBEL - Mercado Ibérico de la Electricidad. http://www.mibel.com
  10. 10.
    Zhang, W., et al.: Measuring mixing patterns in complex networks by Spearman rank correlation coefficient. Phys. A Stat. Mech. its Appl. 451, 440–450 (2016)CrossRefGoogle Scholar
  11. 11.
    Mu, Y., Liu, X., Wang, L.: A Pearson’s correlation coefficient based decision tree and its parallel implementation. Inf. Sci. (Ny) 435, 40–58 (2018)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Mammadli, S.: Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm. Procedia Comput. Sci. 120, 602–607 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Energia SimplesPortoPortugal
  2. 2.GECAD – Research GroupInstitute of Engineering, Polytechnic of Porto (ISEP/IPP)PortoPortugal

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