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A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting

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Abstract

This paper proposes an effective fusion of neural networks and grey modeling for adaptive electricity load forecasting. The fusion employs the complementary strength of these two appealing techniques. In terms of forecasting accuracy, the proposed fusion scheme outperforms the individual ones and the statistical autoregressive methods according to the results of a substantial number of experiments. In addition to the fusion scheme, this paper also proposes a grey relational analysis to automatically assess the importance of each input variable for the forecasting task. This analysis helps the forecaster choose dominant ones among the many input variables, thus removing much burden of acquiring professional domain knowledge for problems and reducing the interference of irrelevant inputs on the forecasting. Experimental results are shown in this paper to verify the effectiveness of the grey relational analysis.

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References

  1. Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55

    Article  Google Scholar 

  2. Liang RH, Cheng CC (2000) Combined regression-fuzzy approach for short-term load forecasting. IEE P-Gener Transm D 147(4):261–266

    Article  Google Scholar 

  3. Srinivasan D, Tan SS, Chang CS, Chan EK (1999) Parallel neural network-fuzzy expert system strategy for short-term load forecasting: System implementation and performance evaluation. IEEE Trans Power Syst 14(3):1100–1105

    Article  Google Scholar 

  4. Chiu CC, Kao LJ, Cook DF (1997) Combining a neural network with a rule-based expert system approach for short-term power load forecasting in taiwan. Expert Syst Appl 13(4):299–305

    Article  Google Scholar 

  5. Hsu CC, Chen CY (2003) Regional load forecasting in Taiwan—applications of artificial neural networks. Energ Convers Manage 44(12):1941–1949

    Article  Google Scholar 

  6. Ganjavi MR, Lucas C, Javidi MH (1999) Short term load forecasting using fuzzy neural network modified by the similarity and subsethood measures. J Intell Fuzzy Syst 7(4):347–357

    Google Scholar 

  7. Papadakis SE, Theocharis JB, Kiartzis SJ, Bakirtzis AG (1998) A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Trans Power Syst 13(2):480–489

    Article  Google Scholar 

  8. He SH, Li XY, Zhong J (2002) Identification of armax based on genetic algorithm. Trans Nonferrous Metals Soc China 12(2):349–355

    Google Scholar 

  9. Yang HT, Huang CM, Huang CL (1996) Identification of armax model for short term load forecasting: an evolutionary programming approach. IEEE Trans Power Syst 11(1):403–408

    Article  MathSciNet  Google Scholar 

  10. Amjady N (2001) Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans Power Syst 16(4):798–805

    Article  Google Scholar 

  11. Lin CB, Su SF, Hsu YT (2001) High-precision forecast using grey models. Int J Syst Sci 32(5):609–619

    Article  MATH  Google Scholar 

  12. Luo YX, Zhang LT, Cai AH, He ZM (2004) Grey gm(1,1) model with function-transfer method and application to energy consuming prediction. Kybernetes 33(2):322–330

    Article  MATH  Google Scholar 

  13. Tan JQ, Xie ZH, Ji LR (2003) A new way to predict forecast skill. Adv Atmos Sci 20(5):837–841

    Article  Google Scholar 

  14. Yao AWL, Chi SC, Chen JH (2003) An improved grey-based approach for electricity demand forecasting. Electric Power Syst Res 67(3):217–224

    Article  Google Scholar 

  15. Yu PS, Chen CJ, Chen SJ, Lin SC (2001) Application of grey model toward runoff forecasting. J Am Water Resource Assoc 37(1):151–166

    Article  Google Scholar 

  16. Ranaweera DK, Hubele NF, Karady GG (1996) Fuzzy logic for short term load forecasting. Int J Elec Power Energ Syst 18(4):215–222

    Article  Google Scholar 

  17. Chen JF, Wang WM, Huang CM (1995) Analysis of an adaptive time-series autoregressive moving-average (arma) model for short-term load forecasting. Elec Pow Syst Res 34(3):187–196

    Article  Google Scholar 

  18. Nowicka-Zagrajek J, Weron R (2002) Modeling electricity loads in california: Arma models with hyperbolic noise. Signal Process 82(12):1903–1915

    Article  MATH  Google Scholar 

  19. Ranaweera DK, Karady GG, Farmer RG (1997) Economic impact analysts of load forecasting. IEEE Trans Power Syst 12(3):1388–1392

    Article  Google Scholar 

  20. Douglas AP, Breipohl AM, Lee FN, Adapa R (1998) Risk due to load forecast uncertainty in short term bower system planning. IEEE Trans Power Syst 13(4):1493–1499

    Article  Google Scholar 

  21. Hobbs BF, Jitprapaikulsarn S, Konda S, Chankong V, Loparo KA, Maratukulam DJ (1999) Analysis of the value for unit commitment of improved load forecasts. IEEE Trans Power Syst 14(4):1342–1348

    Article  Google Scholar 

  22. Bunn DW (2000) Forecasting loads and prices in competitive power markets. Proc IEEE 88(2):163–169

    Article  Google Scholar 

  23. Nahmias S (1997) Production and operations analysis, 3rd edn. Chap 2, Forecasting. McGraw-Hill, New York

  24. Pollock D, Green R, Nugyen T (1999) Handbook of time series analysis, signal processing, and dynamics. Academic, San Diego, Bk&CD-Rom edn

  25. Viswanathan V, Krishnan V, Tsoukalas LH (1999) Novel ai approaches in power systems. In: 1999 International conference on information intelligence and systems. Rockville, Maryland, pp 275–280

  26. Perrone MP, Cooper LN (1993) When networks disagree: ensemble methods for hybrid neural networks. In: Mammone RJ (ed) Neural networks for speech and image processing. Chapman-Hall, London, pp 126–142

    Google Scholar 

  27. Hashem S, Schmeiser B (1995) Natural gas load forecasting with combination of adaptive. IEEE Trans Neural Networ 6(3):792–794

    Article  Google Scholar 

  28. Elragal H, Khotanzad A (1999) Natural gas load forecasting with combination of adaptive. In: Proceedings of the international joint conference on neural networks, pp 4069–4072

  29. Deng JL (1989) Introduction to grey system theory. Syst Control Lett 5(1):1–24

    Google Scholar 

  30. Huang SJ, Huang CL (2000) Control of an inverted pendulum using grey prediction model. IEEE Trans Ind Appl 36(2):452–458

    Article  Google Scholar 

  31. Su SF, Lin CB, Hsu YT (2002) A high precision global prediction approach based on local prediction approaches. IEEE Trans Syst Man Cybern C Appl Rev 32(4):416–425

    Article  Google Scholar 

  32. Bellman R (1961) Adaptive control processes: a guided tour. Princeton University Press, Princeton

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Dong Hwa University, Shoufeng, Hualien, 974, Taiwan

    Cheng-Chin Chiang, Ming-Che Ho & Jen-An Chen

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  1. Cheng-Chin Chiang
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  2. Ming-Che Ho
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  3. Jen-An Chen
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Correspondence to Cheng-Chin Chiang.

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Chiang, CC., Ho, MC. & Chen, JA. A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting. Neural Comput & Applic 15, 328–338 (2006). https://doi.org/10.1007/s00521-006-0031-4

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