Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Article, Other literature type English OPEN
Cerjan, Marin ; Matijaš, Marin ; Delimar, Marko (2014)
  • Publisher: Multidisciplinary Digital Publishing Institute
  • Journal: Energies (issn: 1996-1073, vol: 7, pp: 1-15)
  • Related identifiers: doi: 10.3390/en7053304
  • Subject: 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.
  • References (25)
    25 references, page 1 of 3

    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.

  • Metrics
    No metrics available
Share - Bookmark