Comparing Energy Demand Estimation Using Various Statistical Methods: The Case of Turkey

Year 2016, Volume: 29 Issue: 2, 237 - 244, 20.06.2016

Yakup Murat Bulut , Zeki Yıldız

Abstract

Many engineers and scientists concern with future energy demand. They use many different statistical methods to estimate future energy demand such as multiple linear regression, neural networks, genetic algorithms and so on. In this paper, we propose ridge regression (RR) and partial least squares regression (PLSR) methods to estimate future energy demand. Because of the fact that variables, which are used in energy demand, are very collinear, ridge regression and partial least squares regression methods give more realistic results than least squares regression method. So, energy demand equations are developed based on RR and PLSR methods. Since, RR give better estimation, we estimate Turkey’s future energy demand based on RR method.

Keywords

: Energy Demand, Energy Modeling, Biased Regression

References

• Ceylan, H and Öztürk, H. K. 2004. Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management 45: 2525- 2537.
• Sözen, A., Arcaklıoğlu, E., Özalp, M. and Çağlar, N. 2005. Forecasting based on neural network approach of solar potential in Turkey. Renewable Energy 30: 1075-1090.
• Kıran, M. S., Özceylan, E., Gündüz M. and Paksoy, T. 2012. A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management 53: 75-83.
• Ünler, A. 2008. Improvement of energy demand forecast using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy 36: 1937-1944.
• Toksarı, M. D. 2007. Ant colony optimization approach to estimate energy demand in Turkey. Energy Policy 35: 3984-3990.
• Öztürk, H. K. and Ceylan H. 2005. Forecasting total and industrial sector electricity demand based on genetic algorithm approach: Turkey case study. International Journal of Energy Research 29: 829- 840.
• Ebohan, O. J. 1996. Energy, economic growth and casuality in developing countries: a case study of Tanzania and Nigeria. Energy Policy 24: 447-453.
• Dinçer, I. and Dost, S. 1997. Energy and GDP. International Journal of Energy Research 21(2): 153-167.
• Ediger, V. Ş. and Tatlıdil, H. 2002. Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management 43: 473-487.
• Aras, H. and Aras, N. 2004. Forecasting residential natural gas demand. Energy Sources 26: 463-472.
• Ediger, V. Ş., Akar, S. and Uğurlu, B. 2006. Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model. Energy Policy 34: 3836-3846.
• Sözen, A., Arcaklıoğlu, E. and Özkaymak, M. 2005. Modelling of the Turkey’s net energy consumption using artificial neural network. International Journal of Computer Applications in Technology 22(2/3): 130-136.
• Murat, Y. S. and Ceylan, H. 2006. Use of artificial neural Networks for transport energy demand modelling. Energy Policy 34: 3165-3172.
• Canyurt, O. E., Ceylan, H., Öztürk, H. K. and Hepbaşlı, A. 2004. Energy demand estimation based on two-different genetic algorithm approaches. Energy Sources 26: 1313-1320.
• Haldenbilen, S. and Ceylan, H. 2005. Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy 33: 89-98.
• Ceylan, H., Öztürk, H. K., Hepbaşlı, A. and Utlu, Z. 2005. Estimating energy and exergy production and consumption values using three different genetic algorithm approaches. Part 2: Application and scenarios. Energy Sources 27: 629-639.
• Öztürk, H. K., Canyurt, O. E., Hepbaşlı, A. and Utlu, Z. 2004. Residential-commercial energy input estimation based on genetic algorithm approaches: an application of Turkey. Energy and Building 36(2): 175-183.
• Yumurtacı, Z. and Asmaz, E. 2004. Electric energy demand of Turkey for the year 2050. Energy Sources 26: 1157-1164.
• Abdel-Aal, R. E., Al-Garni, A. Z. and Al-Nassar, Y. N. 1997. Modeling and forecasting monthly electric energy consumption in Eastern Saudi Arabia using abductive Networks. Energy 22: 911- 921.
• Al-Garni, A. Z., Zubair, S. M. and Nizami, J. S. 1994. A regression model for electric-energy consumption forecasting in Eastern Saudi Arabia. Energy 19: 1043-1049.
• Jyoti, P., Pallav, P. and Pallavi, M. 2007. Demand projections of petroleum products and natural gas in India. Energy 32: 1825-1837.
• Garthwaite, P. H. 1994. An interpretation of Partial Least Squares. Journal of the American Statistical Association 89: 122-127.
• Bulut, Y. M. 2011. Comparison of Partial Least Squares Regression and its Alternative Methods When Multicollinearity Exists. M. Sc. Thesis, Eskisehir Osmangazi University, Institute of Natural Science, 79 p. (Unpublished).
• Gunst, R. F. and Mason, R. L. 1980. Regression Analysis and its Application: A Data-oriented Approach. New York: Marcel Dekker.
• Hoerl, A. E. and Kennard, R. W. 1970. Ridge regression: Biased Estimation for Non-orthogonal Problems. Technometrics 55-67.
• Abdi, H. 2010. Partial least square regression, projection on latent structure regression, PLS- Regression. Wiley Interdisciplinary Reviews: Computational Statistics 2: 97-106.
• Wold, H. 1975. Soft Modelling by Latent variables: the nonlinear iterative partial least squares approach. In Gani, J., editor, Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett, London, Academic Press.
• Glen, W. G., Sarker, M., Dunn III, W. J. and Scott, D, R. 1989. UN1PALS: Software for principal components analysis and partial least squares regression. Tetrahedron Comput. Methodol., 2(6): 377–396.
• Lindgren, F., Geladi, P. and Wold, S. 1993. The kernel algorithm for PLS., J. Chemometrics, 7: 45– 59.
• De Jong, S. 1993. SIMPLS: An Alternative Approach to Partial Least Squares Regression. Chemometrics and Intelligent Laboratory Systems 18: 251-263.
• Phatak, A. and De Jong, S. 1997. The Geometry of Partial Least Squares. Journal of Chemometrics 11: 311-338.
• CBT, Central Bank of Turkey, http//.www.tcmb.gov.tr (in Turkish); 2013.
• WECTNC, World Energy Council Turkish National Committee, Energy Report, Ankara (in Turkish) 2011.
• WECTNC, World Energy Council Turkish National Committee, Energy Report, Ankara (in Turkish) 2012.
• NS, National Statistics http//.www.tuik.gov.tr (in Turkish); 2013.