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A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting

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Abstract

Electricity price is an important aspect in a restructured power market. Because of nonlinearity, nonstationary, and volatility of electricity price, it is highly essential to forecast the price on a short-term scale. This paper presents an efficient method based on a hybrid functional link dynamic neural network (DNN) trained by an adaptive robust unscented Kalman filter (UKF). The proposed method forecasts hourly prices for the day-ahead electricity market. The functional block helps to introduce nonlinearity by expanding the input space to higher-dimensional space through a basis function without using any hidden layer like multilayer perceptron structure. The DNN includes one or more infinite impulse response filters in the forward path providing feedback connections between outputs and inputs. This allows signal flow in both forward and backward directions, giving the network a dynamic memory useful to mimic dynamic systems. Also to improve the accuracy of the forecast, the noise covariance matrices of the UKF are adapted recursively. The proposed method is tested on PJM electricity market, and the residuals mean absolute error is compared with other forecasting methods, indicating the improved accuracy of the approach and its suitability to produce a real-time forecast. Further, to compare the accuracy of the forecast, an alternative UKF noise covariance optimization is attempted using differential evolution.

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References

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

    Article  Google Scholar 

  2. Wu L, Shahidehpour M (2010) A hybrid model for day-ahead price forecasting. IEEE Trans Power Syst 25(3):1519–2010

    Article  Google Scholar 

  3. Peter Zhang G (2003) Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, vol 50. Elsevier, pp 159–175

  4. Amjady N, Daraeepour A, Keynia F (2010) Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network. IET Gen Transm Distrib 4(3):432–444

    Article  Google Scholar 

  5. Tan Z, Zhang J, Wang J, Xu J (2010) Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl Energy 87(11):3606–3610

    Article  Google Scholar 

  6. Zareipour H, Canizares CA, Bhattacharya K, Thomson J (2006) Application of public-domain market information to forecast Ontario’s wholesale electricity prices. IEEE Trans Power Syst 21(4):1707–1717

    Article  Google Scholar 

  7. Zhou M, Yan Z, Ni YX, Li G, Nie Y (2006) Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach. Proc Inst Elect Eng Gen Transm Distrib 153(2):187–195

    Article  Google Scholar 

  8. Shahidehpour M, Yamin H, Li ZY (2002) Market operations in electric power systems. Wiley, New York

    Book  Google Scholar 

  9. Aggarwal SK, Saini LM, Kumar A (2009) Electricity price forecasting in deregulated markets: a review and evaluation. Elect Power Energy Syst 31(1):13–22

    Article  Google Scholar 

  10. Mandal P, Senjyu T, Urasaki N, Funabshi T, Srivastava AK (2007) A novel approach to forecast electricity price for PJM using neural network and similar days method. IEEE Trans Power Syst 22(4):2058–2065

    Article  Google Scholar 

  11. Lin WM, Gow HJ, Tsai MT (2010) An enhanced radial basis function network for short-term electricity price forecasting. Appl Energy 87:3226–3234

    Article  Google Scholar 

  12. PJM Electricity Market Data [Online]. http://www.pjm.com

  13. Li Y, Flynn PC (2004) Deregulated power prices: comparison of volatility. Energy Policy 32(14):1591–1601

    Article  Google Scholar 

  14. Campolucci P, Uncini A, Piazza F, Rao BD (1999) On-line learning algorithms for locally recurrent neural networks. IEEE Trans Neural Netw 10(2):253–270

    Article  Google Scholar 

  15. Xiong K, Zhang H, Liu L (2008) Adaptive robust extended Kalman filter for nonlinear stochastic systems. IET Control Theory Appl 2(3):239–250

    Article  MathSciNet  Google Scholar 

  16. Kaneda Y, Irizuki Y, Yamakita M (2013) Robust unscented Kalman filter via I1 regression and design method of its parameters. In: Proc. IEEE 2013 9th Asian control conference (ASCC), Istanbul, pp 1–6. doi:10.1109/ASCC.2013.660622

  17. Mirzaee A, Salahshoor K (2012) Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller. J Process Control 22(3):626–634

    Article  Google Scholar 

  18. Valverde G, Terzija V (2011) Unscented Kalman filter for power system dynamic state estimation. IET Gen Trans Distrib 5(1):29–37

    Article  Google Scholar 

  19. Song Q, Han JD (2008) An adaptive UKF algorithm for the state and parameter estimations of a mobile Robot. Acta Autom Sin 34(1):72–79

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhao L, Wangm X (2009) An adaptive UKF with noise statistic estimator. In: Proc. ICIEA 2009. 4th IEEE conference on industrial electronics and applications, Xi’an, pp 614–618. doi:10.1109/ICIEA.2009.5138274

  21. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Article  Google Scholar 

  22. Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  23. Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66

    Article  MathSciNet  Google Scholar 

  24. Dash PK, Satpathy HP, Liew AC, Rahman S (1997) A real-time short-term load forecasting system using functional link networks. IEEE Trans Power Syst 12(2):675–680

    Article  Google Scholar 

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Authors and Affiliations

  1. Multidisciplinary Research Cell, Siksha O Anusandhan University, Bhubaneswar, Odisha, India

    Sujit Kumar Dash, Ranjeeta Bisoi & P. K. Dash

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  1. Sujit Kumar Dash
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  2. Ranjeeta Bisoi
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  3. P. K. Dash
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Correspondence to P. K. Dash.

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Dash, S.K., Bisoi, R. & Dash, P.K. A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting. Neural Comput & Applic 27, 2123–2140 (2016). https://doi.org/10.1007/s00521-015-2011-z

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  • DOI: https://doi.org/10.1007/s00521-015-2011-z

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