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A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting

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  • Ling Tang
  • Shuai Wang
  • Kaijian He
  • Shouyang Wang

Abstract

We propose a novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Our method is based on the principles of “data-characteristic-based modeling” and “decomposition and ensemble”. The model improves on existing decomposition ensemble learning techniques (with “decomposition and ensemble”) by using “data-characteristic-based modeling” to forecast the decomposed modes. Ensemble empirical mode decomposition is first used to decompose the original nuclear energy consumption data into a series of comparatively simple modes, reducing the complexity of the data. Then, the extracted modes are thoroughly analyzed to capture hidden data characteristics. These characteristics are used to determine appropriate forecasting models for each mode. Final forecasts are obtained by combining these predicted components using an effective ensemble tool, such as least squares support vector regression. For illustration and verification purposes, we have implemented the proposed model to forecast nuclear energy consumption in China. Our numerical results demonstrate that the novel method significantly outperforms all considered benchmarks. This indicates that it is a very promising tool for forecasting complex and irregular data such as nuclear energy consumption. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Ling Tang & Shuai Wang & Kaijian He & Shouyang Wang, 2015. "A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting," Annals of Operations Research, Springer, vol. 234(1), pages 111-132, November.
    • Handle: RePEc:spr:annopr:v:234:y:2015:i:1:p:111-132:10.1007/s10479-014-1595-5
    DOI: 10.1007/s10479-014-1595-5
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    1. 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.
    2. 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.
    3. Zhou, Yun, 2010. "Why is China going nuclear?," Energy Policy, Elsevier, vol. 38(7), pages 3755-3762, July.
    4. 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.
    5. David Edelman, 2007. "Adapting support vector machine methods for horserace odds prediction," Annals of Operations Research, Springer, vol. 151(1), pages 325-336, April.
    6. Dorit Hochbaum & Barak Fishbain, 2011. "Nuclear threat detection with mobile distributed sensor networks," Annals of Operations Research, Springer, vol. 187(1), pages 45-63, July.
    7. 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.
    8. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    9. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    10. Tianshi Jiao & Jiming Peng & Tamás Terlaky, 2009. "A confidence voting process for ranking problems based on support vector machines," Annals of Operations Research, Springer, vol. 166(1), pages 23-38, February.
    11. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Yi Fang & Jong Park & Young-Seon Jeong & Myong Jeong & Seung Baek & Hyun Cho, 2011. "Enhanced predictions of wood properties using hybrid models of PCR and PLS with high-dimensional NIR spectral data," Annals of Operations Research, Springer, vol. 190(1), pages 3-15, October.
    14. 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.
    15. 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.
    16. Beck, Roderick & Solow, John L, 1994. "Forecasting nuclear power supply with Bayesian autoregression," Energy Economics, Elsevier, vol. 16(3), pages 185-192, July.
    17. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    18. Ling Tang & Lean Yu & Fangtao Liu & Weixuan Xu, 2013. "An Integrated Data Characteristic Testing Scheme For Complex Time Series Data Exploration," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 491-521.
    19. 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.
    Full references (including those not matched with items on IDEAS)

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