Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Forecasting Day-ahead Electricity Price Using a Hybrid Improved Approach

  • Hu, Jian-Ming (School of Economics and Statistics, Guangzhou University) ;
  • Wang, Jian-Zhou (School of Statistics, Dongbei University of Finance and Economics)
  • Received : 2016.01.28
  • Accepted : 2017.08.09
  • Published : 2017.11.01
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Abstract

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

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References

  1. Catalao JPS, Pousinho HMI, Mendes VMF. Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Trans Power System; vol. 22, no. 1, pp. 137-44, Feb. 2011.
  2. Lin W-M, Gow H-J, Tsai M-T. Electricity price forecasting using enhanced probability neural network. Energy Converison and Management,vol 51, no. 12, pp. 2707-14 , Dec. 2010. https://doi.org/10.1016/j.enconman.2010.06.006
  3. Catalao JPS, Pousinho HMI, Mendes VMF. Shortterm electricity prices forecasting in a competitive market by a hybrid intelligent approach. Energy Converison and Management, vol. 52, no. 2, pp. 1061-5, Feb. 2011. https://doi.org/10.1016/j.enconman.2010.08.035
  4. Shayeghi H, Ghasemi A. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVR based scheme. Energy Converison and Management, vol. 74, pp. 482-91, Oct. 2013. https://doi.org/10.1016/j.enconman.2013.07.013
  5. Li XR, Yu CW, Ren SY, Chiu CH, Meng K. Dayahead electricity price forecastingbased on panel cointegration and particle filter. Electron Power System Research, vol. 95, pp. 66-76, Feb. 2013. https://doi.org/10.1016/j.epsr.2012.07.021
  6. Conejo AJ, Plazas MA, Espinola R, Molina AB. Day-ahead electricity price forecasting using wavelet transform and ARIMA models. IEEE Trans power systems, vol. 20, no. 2, pp. 1035-42, May 2005. https://doi.org/10.1109/TPWRS.2005.846054
  7. Contreras J, Espinola R, Nogales FJ, Conejo AJ. ARIMA models to predict nextday electricity prices. IEEE Trans Power Systems, vol. 18, no. 3, Aug. 2003.
  8. Najeh Chaabane. A hybrid ARFIMA and neural network model for electricity price prediction. Electrical Power and Energy Systems, vol. 55, pp. 187-194, Feb. 2014. https://doi.org/10.1016/j.ijepes.2013.09.004
  9. G.P.Girish. Spot electricity price forecasting in Indian electricity market using autoregressive-GARCH models. Energy Strategy Reviews, vol. 11-12, pp. 52-57, June 2016. https://doi.org/10.1016/j.esr.2016.06.005
  10. Grzegorz Dudek. Multilayer perceptron for GEF Com2014 probabilistic electricity price forecasting. International Journal of Forecasting, vol. 32, no. 3, pp. 1057-1060, July-Sept. 2016,. https://doi.org/10.1016/j.ijforecast.2015.11.009
  11. Deyun,Wang et al.Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Applied Energy, vol. 190, no. 15 pp. 390-407, Mar. 2017.
  12. Jiang et al. Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Systems with Applications; vol. 82, no.1, pp. 216-230,Oct. 2017. https://doi.org/10.1016/j.eswa.2017.04.017
  13. Amjady N. Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Systems, vol. 21, no. 2, pp. 887-896, May, 2006. https://doi.org/10.1109/TPWRS.2006.873409
  14. Y.Y. Hong, C.F. Lee. A neuro-fuzzy price forecasting approach in deregulated electricity markets, Electric Power System. Research. vol. 73, no. 2, pp. 151-157 Feb. 2005. https://doi.org/10.1016/j.epsr.2004.07.002
  15. Pindoriya NM, Singh SN, Singh SK. An adaptive wavelet neural network-based energy price forecasting, in electricity market. IEEE Trans Power Systems, vol. 23, no. 3, pp. 1423-1432, Aug. 2008:. https://doi.org/10.1109/TPWRS.2008.922251
  16. Anbazhagan S, Kumarappan N. Day-ahead deregulated electricity market price forecasting using recurrent neural network. IEEE Systems Journal, vol. 7, no. 4, pp. 866-872, Dec. 2013. https://doi.org/10.1109/JSYST.2012.2225733
  17. Jianzhou Wang et al. A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system. Applied Soft Computing, vol. 48, pp. 281-297, Nov. 2016. https://doi.org/10.1016/j.asoc.2016.07.011
  18. Xing Yan, Nurul A. Chowdhury. Mid-term electricity market clearing price forecasting: A multiple SVM approach. Electrical Power and Energy Systems, vol. 58, pp. 206-214, Jun. 2014. https://doi.org/10.1016/j.ijepes.2014.01.023
  19. Yongbao Chen et al. Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, vol. 195, pp. 659-670, Jun. 2017. https://doi.org/10.1016/j.apenergy.2017.03.034
  20. Huang NE, Shen Z, Long SR. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. The Royal Society, vol. 454. no. 1971, Mar.1998.
  21. Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method, centre for ocean-land-atmosphere studies. Advances in adaptive data analysis, vol. 1, no. 1, pp. 1-41, Jan. 2009. https://doi.org/10.1142/S1793536909000047
  22. Samuel Xavier-de-Souza, Johan A. K. Suykens, Joos Vandewalle, and Desire Bolle. Coupled Simulated Annealing. IEEE trans systems, man, and cyberneticspart b(cybernetics), vol. 40, no. 2, pp. 320-335, Apr. 2010. https://doi.org/10.1109/TSMCB.2009.2020435
  23. Hossam Mohammad Khalil, Mohammad El-Bardini. Implementation of speed controller for rotary hydraulic motor based on LS-SVM. Expert Systems with Applications, vol. 38, pp. 14249-14256, Oct. 2011.
  24. Jianming Hu, Jianzhou Wang, Guowei Zeng. A hybrid forecasting approach applied to wind speed time series Renewable Energy, vol. 60, pp. 185-194 , Dec. 2013. https://doi.org/10.1016/j.renene.2013.05.012
  25. http://www.aemo.com.au/