Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 980410, 11 pages
http://dx.doi.org/10.1155/2014/980410
Research Article

A Driving Force Analysis and Forecast for Gas Consumption Demand in China

1School of Finance and Economics, Xi’an JiaoTong University, Xi’an 710061, China
2International Business School, Shaanxi Normal University, Xi’an 710062, China
3Department of Management Sciences, City University of Hong Kong, Hong Kong

Received 28 December 2013; Revised 15 March 2014; Accepted 10 April 2014; Published 7 May 2014

Academic Editor: Jianping Li

Copyright © 2014 Qing Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Das, A. A. McFarlane, and M. Chowdhury, “The dynamics of natural gas consumption and GDP in Bangladesh,” Renewable and Sustainable Energy Reviews, vol. 22, pp. 269–274, 2013. View at Google Scholar
  2. N. Apergis and J. E. Payne, “Natural gas consumption and economic growth: a panel investigation of 67 countries,” Applied Energy, vol. 87, no. 8, pp. 2759–2763, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Kankal, A. Akpinar, M. I. Kömürcü, and T. Ş. Özşahin, “Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables,” Applied Energy, vol. 88, no. 5, pp. 1927–1939, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. V. Bianco, F. Scarpa, and L. A. Tagliafico, “Scenario analysis of nonresidential natural gas consumption in Italy,” Applied Energy, vol. 113, pp. 392–403, 2014. View at Google Scholar
  5. O. E. Canyurt and H. K. Ozturk, “Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey,” Energy Policy, vol. 36, no. 7, pp. 2562–2569, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. E. F. Sánchez-Úbeda and A. Berzosa, “Modeling and forecasting industrial end-use natural gas consumption,” Energy Economics, vol. 29, no. 4, pp. 710–742, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Gutiérrez, A. Nafidi, and R. G. Sánchez, “Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model,” Applied Energy, vol. 80, no. 2, pp. 115–124, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Khotanzad, H. Elragal, and T.-L. Lu, “Combination of artificial neural-network forecasters for prediction of natural gas consumption,” IEEE Transactions on Neural Networks, vol. 11, no. 2, pp. 464–473, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Xu and W. Wang, “Forecasting China's natural gas consumption based on a combination model,” Journal of Natural Gas Chemistry, vol. 19, no. 5, pp. 493–496, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Soldo, “Forecasting natural gas consumption,” Applied Energy, vol. 92, pp. 26–37, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. F. B. Gorucu, “Artificial neural network modeling for forecasting Gas consumption,” Energy Sources, vol. 26, no. 3, pp. 299–307, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. Y.-S. Lee and L.-I. Tong, “Forecasting energy consumption using a grey model improved by incorporating genetic programming,” Energy Conversion and Management, vol. 52, no. 1, pp. 147–152, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Azadeh, S. M. Asadzadeh, M. Saberi, V. Nadimi, A. Tajvidi, and M. Sheikalishahi, “A neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE,” Applied Energy, vol. 88, no. 11, pp. 3850–3859, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. Y.-Y. He, Q.-F. Xu, S.-l. Yang, and B.-G. Xu, “A power load probability density forecasting method based on RBF neural network quantile Regression,” Proceedings of the CSEE, vol. 33, no. 1, pp. 93–98, 2013 (Chinese). View at Google Scholar
  15. P. Crompton and Y. Wu, “Energy consumption in China: past trends and future directions,” Energy Economics, vol. 27, no. 1, pp. 195–208, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Chai, J.-E. Guo, L. Meng, and S.-Y. Wang, “Exploring the core factors and its dynamic effects on oil price: an application on path analysis and BVAR-TVP model,” Energy Policy, vol. 39, no. 12, pp. 8022–8036, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Moody and C. J. Darken, “Fast learning in networks of locally-tuned processing units,” Neural Computation, vol. 1, no. 2, pp. 281–294, 1989. View at Google Scholar
  18. R. Koenker and G. Bassett Jr., “Regression quantiles,” Econometrica, vol. 46, no. 1, pp. 33–50, 1978. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  19. J. W. Taylor, “A quantile regression neural network approach to estimating the conditional density of multiperiod returns,” Journal of Forecasting, vol. 19, no. 4, pp. 299–311, 2000. View at Google Scholar · View at Scopus
  20. B. K. Ray and R. S. Tsay, “Long-range dependence in daily stock volatilities,” Journal of Business and Economic Statistics, vol. 18, no. 2, pp. 254–262, 2000. View at Google Scholar · View at Scopus
  21. J.-R. Kurz-Kim, “Combining forecasts using optimal combination weight and generalized autoregression,” Journal of Forecasting, vol. 27, no. 5, pp. 419–432, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus