Estimation of Turkey Electric Energy Demand until Year 2035 Using TLBO Algorithm
AbstractIn this study, the estimation of Turkey primary electric energy demand until 2035 is tried to estimate by using Teaching-Learning Based Optimization (TLBO) Algorithm. Two models are proposed which are based on economic indicators TLBO algorithm linear energy demand (TLBOEDL) and TLBO algorithm quadratic energy demand (TLBOEDQ). In both of these two models the indicators used are Gross Domestic Product (GDP), population, importation and exportation. After a comparison of these two models with real values between 1979 and 2005 years, it is applied to the estimation of Turkey electric energy demand until 2035 by three different scenario. The estimation results are suitable with the estimation of Turkey total primary energy supply of 2013 Energy Report of World Energy Council Turkish National Committee (WEC-TNC ).
T. Lorde, K. Waithe, B. Francis, “The importance of electrical energy for economic growth in Barbados”, Energy Economics, vol. 32(6), pp. 1411-1420, Nov. 2010.
Turkey Energy Report 2013, World Energy Council Turkish National Committee (WEC-TNC), ISSN: 1301-6318, Ankara, Jan. 2014.
V. Yiğit, “Estimation Of Turkey Net Electric Energy Consumption Until to Year 2020 Using Genetic Algorithm”, International J. of Engineering Res. and Development, Vol. 3, pp. 37-41, Jun. 2011.
Haldenbilen, S., Ceylan, H., “Genetic algorithm approach to estimate transport energy demand in Turkey”, Energy Policy, Vol. 33(1), pp. 89-98, Jan. 2005.
O. E. Canyurt., H. Ceylan, H.K. Öztürk., A. Hepbaşlı, “Energy demand estimation based on two-different genetic algorithm approaches”, Energy Sources, vol. 26, pp. 1313–20, Feb. 2004.
H. Ceylan, H. K. Öztürk, “Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach”, Energy Convers Manag, vol. 45 (15–16), pp. 2525–37, Sep. 2004.
H.K. Öztürk,O.E. Canyurt, A. Hepbaşlı, Z. Utlu, “Residential-commercial energy input estimation based on genetic algorithm approaches: an application of Turkey”, Energy Build, vol. 36, pp. 175–83, Feb. 2004.
A. Sözen, E. Arcaklıoğlu, M. Özkaymak, “Turkey’s net energy consumption”, Apply Energy, vol. 8, pp. 209–21, Sep. 2004.
Y.S. Murat, H. Ceylan, “Use of artificial neural networks for transport energy demand modeling”, Energy Policy, vol. 34, pp. 3165–72, Nov. 2006.
C. Hamzaçebi, “Forecasting of Turkey’s net electricity energy consumption on sectorial bases”, Energy Policy, vol. 35, pp. 2009–16, Mar. 2007.
M.D. Toksarı, “Ant colony optimization approach to estimate energy demand of Turkey”, Energy Policy, vol. 35, pp. 3984–90, Mar. 2007.
M. S. Kıran, E. Özceylan, M. Gündüz, T. Paksoy, “A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey”, Energy Conversion and Manag. ,vol. 53, pp.75–83, Sep. 2011.
R.V. Rao, V.J. Savsani, D. P. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”, Computer-Aided Desig., vol. 43, pp. 303–15, Dec. 2010.
A. Unler, “Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025”, Energy Policy , vol. 36, pp. 1937–44, Apr. 2008.
M.D. Toksarı, “Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey”, Energy Policy, vol. 37, pp. 1181–87, Jan. 2009.
E. Assareh, M.A. Behrang, M.R. Assari, A. Ghanbarzadeh, “Application of PSO and GA techniques on demand estimation of oil in Iran”, Energy, vol. 35, pp. 5223–29, Dec. 2010
TSI., Turkish Statistical Institute, Statistics, (http://www.tuik.gov.tr/) Ankara, 2015.
MENR., The Ministry of Energy and Natural Resources, (http://www.enerji.gov.tr/tr-TR/EIGM-Raporlari), Ankara, 2015.
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