References
- Aras, N. 2008. Forecasting residential consumption of natural gas using genetic algorithms. Energy Explor. Exploit. 26:241–266.
- Avami, A., and Boroushaki, M. 2011. Energy consumption forecasting of Iran using recurrent neural networks. Energy Sour. 6:339–347.
- Bro, R., Sidiropoulos, N. D., and Smilde, A. k. 2002. Maximum likelihood fitting using ordinary least squares algorithms. J. Chemom. 16:387–400.
- Dai, L. X. 2011. Modeling and application research on gas load forecasting. Comput. Simul. 28:180–183.
- Gorucu, F. B., and Gumrah, F. 2004. Evaluation and forecasting of gas consumption by statistical analysis. Energy Sour. 26:267–276.
- Kiers, H. A. L. 1997. Weighted least squares fitting using ordinary least squares algorithms. Psychometrika 62:251–266.
- Kizilaslan, R., and Karlik, B. 2009. Combination of neural networks forecasters for monthly natural gas consumption prediction. Neural Network World 19:191–199.
- Kumar, U., and Jain, V. K. 2010. Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy 35:1709–1716.
- Lim, H. J., and Yoo, S. H. 2012. Natural gas consumption and economic growth in Korea: A causality analysis. Energy Sour. 7:169–176.
- Li, J. C., Dong, X. C., Shanggua, J. X., and Hook, M. 2011. Forecasting the growth of China’s natural gas consumption. Energy 36:1380–1385.
- National Bureau of Statistics of China. 2012. China Statistics Press
- Soldo, B. 2012. Forecasting natural gas consumption. Appl. Energy 92:26–37.
- Sozen, A., Akcayol, M. A., and Arcaklioglu, E. 2006. Forecasting net energy consumption using artificial neural network. Energy Sour. 1:147–155.
- Tonkovic, Z., Zekic-Susac, M., and Somolanji, M. 2009. Predicting natural gas consumption by neural networks .Tehnicki Vjesnik 16:51–61.
- Wang, J. L., Feng, L. Y., Zhao, L., and Snowden, S. 2013. China’s natural gas: Resources, production and its impacts. Energy Policy 55:690–698.
- Xu, X. Z., Ding, S. F., Jia, W. K., Ma, G., and Jin, F. X. 2013. Research of assembling optimized classification algorithm by neural network based on ordinary Least Squares (OLS). Neural Comput. Appl. 22:187–193.
- Zahedi, G., Azizi, S., Bahadori, A., Bahadori, A., Eikamel, A., and Alwi, S. R. W. 2013. Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada. Energy 19:323–328.