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A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting

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  • Tang, Ling
  • Yu, Lean
  • He, Kaijian

Abstract

Due to the unique features of nuclear energy market, this paper tries to propose a novel data-characteristic-driven modeling methodology based on the principle of “data-characteristic-driven modeling”, aiming at formulating appropriate forecasting model closely in terms of sample data’s own data characteristics. In the novel data-characteristic-driven modeling methodology, two steps are mainly involved, i.e., data analysis and forecasting modeling. First, the sample data of nuclear energy consumption are thoroughly investigated in order to capture the main inner rules and hidden patterns driving the data dynamics, in terms of data characteristics. Second, the corresponding forecasting model is accordingly formulated and designed based on these data characteristics. For illustration and verification purposes, the proposed methodology is implemented to predict the nuclear energy consumption of USA and China. The empirical results demonstrate that the novel methodology with the principle of “data-characteristic-driven modeling” strikingly improves prediction performance, since the models elaborately built based on data characteristics statistically outperform all other benchmark models without consideration of data characteristics. This further confirms that the proposed methodology is a very promising tool in both analyzing and forecasting nuclear energy consumption.

Suggested Citation

  • Tang, Ling & Yu, Lean & He, Kaijian, 2014. "A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 128(C), pages 1-14.
  • Handle: RePEc:eee:appene:v:128:y:2014:i:c:p:1-14
    DOI: 10.1016/j.apenergy.2014.04.021
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    References listed on IDEAS

    as
    1. Beck, Roderick & Solow, John L, 1994. "Forecasting nuclear power supply with Bayesian autoregression," Energy Economics, Elsevier, vol. 16(3), pages 185-192, July.
    2. Uebele, Martin & Ritschl, Albrecht, 2009. "Stock markets and business cycle comovement in Germany before World War I: Evidence from spectral analysis," Journal of Macroeconomics, Elsevier, vol. 31(1), pages 35-57, March.
    3. Hasanov, Mübariz & Telatar, Erdinc, 2011. "A re-examination of stationarity of energy consumption: Evidence from new unit root tests," Energy Policy, Elsevier, vol. 39(12), pages 7726-7738.
    4. Maslyuk, Svetlana & Smyth, Russell, 2009. "Non-linear unit root properties of crude oil production," Energy Economics, Elsevier, vol. 31(1), pages 109-118, January.
    5. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
    6. Bao, Qin & Tang, Ling & Zhang, ZhongXiang & Wang, Shouyang, 2013. "Impacts of border carbon adjustments on China's sectoral emissions: Simulations with a dynamic computable general equilibrium model," China Economic Review, Elsevier, vol. 24(C), pages 77-94.
    7. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    8. Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2007. "Efficient tests of the seasonal unit root hypothesis," Journal of Econometrics, Elsevier, vol. 141(2), pages 548-573, December.
    9. 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.
    10. Yang, Lijian & Tschernig, Rolf, 2002. "Non- And Semiparametric Identification Of Seasonal Nonlinear Autoregression Models," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1408-1448, December.
    11. 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.
    12. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Chen, Pei-Fen & Lee, Chien-Chiang, 2007. "Is energy consumption per capita broken stationary? New evidence from regional-based panels," Energy Policy, Elsevier, vol. 35(6), pages 3526-3540, June.
    18. Zhou, Yun, 2010. "Why is China going nuclear?," Energy Policy, Elsevier, vol. 38(7), pages 3755-3762, July.
    19. Chen, Wei-Shing, 2011. "Use of recurrence plot and recurrence quantification analysis in Taiwan unemployment rate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1332-1342.
    20. 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.
    21. 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.
    22. Hayashi, Masatsugu & Hughes, Larry, 2013. "The Fukushima nuclear accident and its effect on global energy security," Energy Policy, Elsevier, vol. 59(C), pages 102-111.
    23. 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.
    24. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.
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    7. Wang, Shanyong & Wang, Jing & Lin, Shoufu & Li, Jun, 2019. "Public perceptions and acceptance of nuclear energy in China: The role of public knowledge, perceived benefit, perceived risk and public engagement," Energy Policy, Elsevier, vol. 126(C), pages 352-360.
    8. Li, Qian & Wu, Zhou & Xia, Xiaohua, 2018. "Estimate and characterize PV power at demand-side hybrid system," Applied Energy, Elsevier, vol. 218(C), pages 66-77.
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