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A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

  • Tang, Ling
  • Yu, Lean
  • Wang, Shuai
  • Li, Jianping
  • Wang, Shouyang
Registered author(s):

In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of “decomposition and ensemble”. This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMFs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity.

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Article provided by Elsevier in its journal Applied Energy.

Volume (Year): 93 (2012)
Issue (Month): C ()
Pages: 432-443

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Handle: RePEc:eee:appene:v:93:y:2012:i:c:p:432-443
DOI: 10.1016/j.apenergy.2011.12.030
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  1. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
  2. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
  3. Buran, B. & Butler, L. & Currano, A. & Smith, E. & Tung, W. & Cleveland, K. & Buxton, C. & Lam, D. & Obler, T. & Rais-Bahrami, S. & Stryker, M. & Herold, K., 2003. "Environmental benefits of implementing alternative energy technologies in developing countries," Applied Energy, Elsevier, vol. 76(1-3), pages 89-100, September.
  4. Erdogdu, Erkan, 2007. "Nuclear power in open energy markets: A case study of Turkey," Energy Policy, Elsevier, vol. 35(5), pages 3061-3073, May.
  5. Inglesi, Roula, 2010. "Aggregate electricity demand in South Africa: Conditional forecasts to 2030," Applied Energy, Elsevier, vol. 87(1), pages 197-204, January.
  6. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
  7. Jewell, Jessica, 2011. "Ready for nuclear energy?: An assessment of capacities and motivations for launching new national nuclear power programs," Energy Policy, Elsevier, vol. 39(3), pages 1041-1055, March.
  8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  9. Besmann, Theodore M., 2010. "Projections of US GHG reductions from nuclear power new capacity based on historic levels of investment," Energy Policy, Elsevier, vol. 38(5), pages 2431-2437, May.
  10. Chang, Ching-Chih, 2010. "A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China," Applied Energy, Elsevier, vol. 87(11), pages 3533-3537, November.
  11. Zhu, Suling & Wang, Jianzhou & Zhao, Weigang & Wang, Jujie, 2011. "A seasonal hybrid procedure for electricity demand forecasting in China," Applied Energy, Elsevier, vol. 88(11), pages 3807-3815.
  12. Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
  13. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
  14. Abdel-Aal, R.E. & Al-Garni, A.Z. & Al-Nassar, Y.N., 1997. "Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks," Energy, Elsevier, vol. 22(9), pages 911-921.
  15. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
  16. Xu, Yi-chong, 2008. "Nuclear energy in China: Contested regimes," Energy, Elsevier, vol. 33(8), pages 1197-1205.
  17. Wang, Qiang, 2009. "China needing a cautious approach to nuclear power strategy," Energy Policy, Elsevier, vol. 37(7), pages 2487-2491, July.
  18. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
  19. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
  20. Gutiérrez, R. & Gutiérrez-Sánchez, R. & Nafidi, A., 2006. "Electricity consumption in Morocco: Stochastic Gompertz diffusion analysis with exogenous factors," Applied Energy, Elsevier, vol. 83(10), pages 1139-1151, October.
  21. Beck, Roderick & Solow, John L, 1994. "Forecasting nuclear power supply with Bayesian autoregression," Energy Economics, Elsevier, vol. 16(3), pages 185-192, July.
  22. Kucukali, Serhat & Baris, Kemal, 2010. "Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach," Energy Policy, Elsevier, vol. 38(5), pages 2438-2445, May.
  23. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
  24. Zhou, Yun, 2010. "Why is China going nuclear?," Energy Policy, Elsevier, vol. 38(7), pages 3755-3762, July.
  25. Abdel-Aal, R.E. & Al-Garni, A.Z., 1997. "Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis," Energy, Elsevier, vol. 22(11), pages 1059-1069.
  26. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
  27. Azadeh, A. & Ghaderi, S.F. & Sohrabkhani, S., 2008. "A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran," Energy Policy, Elsevier, vol. 36(7), pages 2637-2644, July.
  28. Ghorashi, Amir Hossien, 2007. "Prospects of nuclear power plants for sustainable energy development in Islamic Republic of Iran," Energy Policy, Elsevier, vol. 35(3), pages 1643-1647, March.
  29. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
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