A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 93 (2012)
Issue (Month): C ()
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- Amarawickrama, Himanshu A. & Hunt, Lester C., 2008.
"Electricity demand for Sri Lanka: A time series analysis,"
Elsevier, vol. 33(5), pages 724-739.
- Himanshu A. Amarawickrama & Lester C Hunt, 2007. "Electricity Demand for Sri Lanka: A Time Series Analysis," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 118, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
- 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.
- Xu, Yi-chong, 2008. "Nuclear energy in China: Contested regimes," Energy, Elsevier, vol. 33(8), pages 1197-1205.
- Pesaran, M.H. & Timmermann, A., 1990.
"A Simple Non-Parametric Test Of Predictive Performance,"
29, California Los Angeles - Applied Econometrics.
- Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
- Pesaran, M.H. & Timmermann, A., 1990. "A Simple, Non-Parametric Test Of Predictive Performance," Cambridge Working Papers in Economics 9021, Faculty of Economics, University of Cambridge.
- 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.
- 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.
- 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.
- 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.
- Zhou, Yun, 2010. "Why is China going nuclear?," Energy Policy, Elsevier, vol. 38(7), pages 3755-3762, July.
- 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.
- Erdogdu, Erkan, 2007.
"Nuclear Power in Open Energy Markets: A case study of Turkey,"
19098, University Library of Munich, Germany.
- Erdogdu, Erkan, 2007. "Nuclear power in open energy markets: A case study of Turkey," Energy Policy, Elsevier, vol. 35(5), pages 3061-3073, May.
- 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.
- Pao, Hsiao-Tien, 2006. "Comparing linear and nonlinear forecasts for Taiwan's electricity consumption," Energy, Elsevier, vol. 31(12), pages 2129-2141.
- Beck, Roderick & Solow, John L, 1994. "Forecasting nuclear power supply with Bayesian autoregression," Energy Economics, Elsevier, vol. 16(3), pages 185-192, July.
- Inglesi, Roula, 2010. "Aggregate electricity demand in South Africa: Conditional forecasts to 2030," Applied Energy, Elsevier, vol. 87(1), pages 197-204, January.
- 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.
- 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.
- 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.
- Wang, Qiang, 2009. "China needing a cautious approach to nuclear power strategy," Energy Policy, Elsevier, vol. 37(7), pages 2487-2491, July.
- 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.
- 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.
- Erdogdu, Erkan, 2007.
"Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey,"
Elsevier, vol. 35(2), pages 1129-1146, February.
- Erdogdu, Erkan, 2007. "Electricity Demand Analysis Using Cointegration and ARIMA Modelling: A case study of Turkey," MPRA Paper 19099, University Library of Munich, Germany.
- 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.
- 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.
- Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:93:y:2012:i:c:p:432-443. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.