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Cerjan, Marin; Matijaš, Marin; Delimar, Marko (2014)
Publisher: Multidisciplinary Digital Publishing Institute
Journal: Energies
Languages: English
Types: Article
Subjects: electricity price, data mining, short term electricity price forecasting, Technology, neural network, forecasting techniques, T, data mining; neural network; price volatility; short term electricity price forecasting; forecasting techniques; spot market; electricity price, price volatility, spot market
jel: jel:Q0, jel:Q, jel:Q4, jel:Q47, jel:Q49, jel:Q48, jel:Q43, jel:Q42, jel:Q41, jel:Q40
Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Hachmeister, A. Informed Traders as Liquidity Providers: Evidence from the German Equity Market; Springer: Wiesbaden, Germany, 2007; Volume 66, p. 179.
    • 2. Andor, M.; Flinkerbusch, K.; Janssen, M.; Liebau, B.; Wobben, M. Negative Strompreise und der Varrang Erneurbarer Energien. Z. Energiewirtsch 2010, 34, 91-99. (In German)
    • 3. European Energy Exchange. Available online: http://www.eex.com (accessed on 10 October 2011).
    • 4. Abraham, A.; Baikunth, N.; Mahanti, P.K. Hybrid Intelligent Systems for Stock Market Analysis. In Computational Science-ICCS 2001; Alexandrev, V.N., Dongarra, J.J., Juliano, B.A., Renner, R.S., Kenneth Tan, C.J., Eds.; Springer-Verlag: Berlin, Germany, 2001; pp. 337-345.
    • 5. Contreras, J.; Espínola, R.; Nogales, F.J.; Conejo, A.J. ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 2003, 18, 1014-1020.
    • 6. Nogales, F.J.; Contreras, J.; Conejo, A.J.; Espínola, R. Forecasting next-day electricity prices by time series models. IEEE Trans. Power Syst. 2002, 17, 342-348.
    • 7. Zhao, J.H.; Dong, Z.Y.; Xu, Z.; Wong, K.P. A statistical approach for interval forecasting of the electricity price. IEEE Trans. Power Syst. 2008, 23,267-276.
    • 8. Garcia, R.C.; Contreras, J.; van Akkeren, M.; Garcia, J.B.C. A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. 2005, 20, 867-874.
    • 9. Huisman, R.; Huurman, C.; Mahieu, R. Hourly Electricity Prices in Day-ahead Markets. Energy Econ. 2007, 29, 240-248.
    • 10. Pindoriya, N.M.; Singh, S.N.; Singh, S.K. An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans. Power Syst. 2008, 23, 1423-1432.
    • 11. Amjady, N.; Dareaeepour, A. Design of input vector for day-ahead price forecasting of electricity markets. Expert Syst. Appl. 2009, 36, 12281-12294.
    • 12. Unsihuay-Villa, C.; Zambroni de Souza, A.C.; Marangon-Lima, J.W.; Balestrassi, P.P. Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model. Int. J. Electr. Power Energy Syst. 2010, 32, 108-116.
    • 13. Catalão, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M. Short-term electricity prices forecasting in a competitive market: A neural network approach. Electr. Power Syst. Res. 2007, 77, 1297-1304.
    • 14. Shahidehpour, M.; Yamin, H.; Li, Z. Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management; Wiley-IEEE Press: New York, NY, USA, 2002; p. 552.
    • 15. Haugom, E.; Wesgaard, S.; Solibakke, P.B.; Lien, G. Realized volatility and the influence of market measure on predictability: Anaysis of Noed Pool forward electricity data. Energy Econ. 2011, 33, 1206-1215.
    • 16. Data of Historical Electricity Prices from European Energy Exchange. Available online: http://www.eex.com/en/Download (accessed on 2 July 2011).
    • 17. Zareipour, H.; Bhattacherya, K.; Canizared, C.A. Electricity market price volatility: The case of Ontario. Energy Policy 2007, 35, 4739-4748.
    • 18. Urllich, C. Realized Volatility and Price Spikes in Electricity Markets: The Influence of Observation Frequency. Available online: http://www.efa2009.org/papers/SSRN-id1342586.pdf (accessed on 13 May 2014).
    • 19. Data of Historical and Forecasted Consumption and Wind Production. Available online: http://www.pointcarbon.com/trading/pmteex/resources/downloads/latest/ (accessed on 3 July 2011).
    • 20. Širjaev, A.N. Essentials of Stochastic Finance Facts, Models, Theory; Advanced Series on Statistical Science and Applied Probability; World Scientific: Singapore, 1999; Volume 3, p. 834.
    • 21. Mandal, P.; Senjyo, T.; Urasaki, N.; Funabashi, T.; Srivastava, A.K. A novel approach to forecast electricity price for PJM using neural network and similar days method. IEEE Trans. Power Syst. 2008, 22, 2058-2065.
    • 22. Mandal, P.; Seoul, Y.U.; Senjyu, T.; Yona, A.; Park, J.W.; Srivastava, A.K. Sensitivity Analysis of Similar Days Parameters for Predicting Short-Term Electricity Price. In Proceedings of the 39th North American Power Symposium (NAPS'07), Las Cruces, NM, USA, 30 September-2 October 2007; pp. 568-574.
    • 23. Effect of Wind Energy on Electricity Market Prices. Available online: http://www.ewea.org/ index.php?id=1640 (accessed on 15 October 2011).
    • 24. Mileta, D.; Simic, Z.; Skok, M. Forecasting prices of electricity on HUPX. In Proceedings of the 2011 8th International Conference on the European Energy Market (EEM), Zagreb, Croatia, 25-27 May 2011; pp. 204-208.
    • 25. Areekul, P.; Senjyu, T.; Toyama, H.; Yona, A. A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulared Market. IEEE Trans. Power Syst. 2010, 25, 524-530.
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