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Dynamic electricity price forecasting using local linear wavelet neural network

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Abstract

Price forecasting has become one of the main focuses of electric power market research efforts as price is the key index to evaluate the market competition efficiency and reflects the operation condition of electricity market decision making. The work presented in this paper makes use of local linear wavelet neural networks to find the market clearing price for a given period, which is based on similar days approach. The results obtained through simulation are compared to other evolutionary optimization techniques surfaced in the recent state-of-the-art literature, including wavelet neural network model. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness for electricity price forecasting.

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References

  1. Amjady N, Hemmati M (2006) Energy price forecasting-problems and proposals for such predictions. IEEE Power Energy Mag 4(2):20–29

    Article  Google Scholar 

  2. Ranjbar M, Soleymani S, Sadati N, Ranjbar AM (2006) Electricity price forecasting using artificial neural network. IEEE International Conference on Power Electronics, Drives and Energy System. December 2006:1–5

  3. Pao H-T (2007) Forecasting electricity market pricing using artificial neural network. Energy Convers Manag 48:907–912

    Article  Google Scholar 

  4. Gareta R, Romeo LM, Gil A (2006) Forecasting electricity prices with neural networks. Energy Convers Manag 47:1770–1778

    Article  Google Scholar 

  5. Mandal P, Senjyu T, Urasaki N, Funabashi T, Srivastrava AK, Short-term price forecasting for competitive electricity market. 38th North American Power Symposium, Sept. 2006: 137–141

  6. Catalao JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM (2007) Short-term price forecasting in a competitive market: a neural network approach. Electr Power Syst Res 21:1297–1304

    Article  Google Scholar 

  7. Amjady N (2006) Day-ahead price forecasting of electricity markets by fuzzy neural networks. IEEE Trans Power Syst 21:887–896

    Article  Google Scholar 

  8. Gao C, Bomparb E, Napoli R, Zhenz H (2007) Price forecast in the competitive electricity market by support vector machine. Phys A 382:98–113

    Article  Google Scholar 

  9. Fan S, Mao C, Chen L (2007) Next-day electricity price forecasting using a hybrid network. IEEE Gener Transm Distrib 1:176–182

    Article  Google Scholar 

  10. Gonzalez AM, Roque AMS, Garcia-Gonzalez J (2005) Modeling and forecasting electricity price with input/output hidden Markov models. IEEE Trans Power Syst 20(1):13–24

    Article  Google Scholar 

  11. Contreras J, Espinola R, Nogales FJ, Conejo AJ (2003) ARIMA models to predict next day electricity prices. IEEE Trans Power Syst 18(3):1014–1020

    Article  Google Scholar 

  12. Nogales 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 

  13. Garcia RC, Contreras J, van Akkeren M, Garcia JBC (2005) A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20(2):867–874

    Article  Google Scholar 

  14. Zhang Q, Benveniste A (1992) Wavelet networks. IEEE Trans Power Syst 3(6):889–898

    Google Scholar 

  15. Murthy GGP, Sedidi V, Panda AK, Rath BN (2014) Forecasting electricity price in deregulated wholesale spot electricity market: a review. Int J Energy Econ Policy 4(1):32–42

    Google Scholar 

  16. Navani JP, Sapra S (2012) Power market design in India. J Technol 2(1):34–37

    Google Scholar 

  17. Aggarwal SK, Mohan L, Kumar A (2008) Electricity price forecasting in Ontario electricity market using wavelet transform in artificial neural network based model. Int J Control Autom Syst 6(5):639–650

    Google Scholar 

  18. Anders GJ, Rodriguez C (2005) Energy price forecasting and Bidding Strategy in the Ontario Power System Market, Power Tech. Conference, June 2005

  19. Rodriguez CP, Anders GJ (2004) Energy price forecasting in the Ontario competitive power system market. IEEE Trans Power Syst 19(1):366–374

    Article  Google Scholar 

  20. Conejo AJ, Plazas MA, Espinola R, Molina AB (2005) Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst 20(2):1035–1042

    Article  Google Scholar 

  21. www.iexindia.com/marketdata/maeket_snapshot.aspx

  22. Peter SE, Raglend IJ, Simon SJP (2014) An architectural frame work of ANN based short term electricity price forecasting engine for Indian energy exchange using similar day approach. Int J Res Eng Technol 2(4):11–122

    Google Scholar 

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Authors and Affiliations

  1. DRIEMS, Cuttack, Odisha, India

    Prasanta Kumar Pany

  2. NIT, Durgapur, West Bengal, India

    S. P. Ghoshal

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  1. Prasanta Kumar Pany
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  2. S. P. Ghoshal
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Correspondence to Prasanta Kumar Pany.

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Pany, P.K., Ghoshal, S.P. Dynamic electricity price forecasting using local linear wavelet neural network. Neural Comput & Applic 26, 2039–2047 (2015). https://doi.org/10.1007/s00521-015-1867-2

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  • DOI: https://doi.org/10.1007/s00521-015-1867-2

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