An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks
Transport Energy Demand
Keywords:
artificial neural networks, gross domestic product, transport energy demand, passenger, oil price
Abstract
The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.
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[4] Solak AO. Reducing Energy Consumption of Transportation Sector in Turkey: A Scenario Approach. Journal of Economic and Social Research. 2013;9(1): 125-140.
[5] Turkish Statistical Institute (TSI); 2015. Available from: http://www.turkstat.gov.tr/PreTabloArama.do [Accessed 10 February 2018].
[6] Deutschland Turkey Industry; 2015. Available from: http://www.dtr-ihk.de/tr/ekonomik-veriler/ekonomi-raporlari/gtai-wirtschaftsbericht-tuerkei-2015 [Accessed 14 February 2018].
[7] The Ministry of Energy and Natural Resources (MENR); 2013. Available from: http://www.enerji.gov.tr/en-US/Mainpage [Accessed 14 March 2018].
[8] Turkish Statistical Institute (TSI); 2016. Available from:http://www.turkstat.gov.tr [Accessed 7 February 2018].
[9] Murat ŞY, Ceylan H. Use of Artificial Neural Networks for Transport Energy Demand Modeling. Energy Policy. 2016;34: 3165-3172.
[10] Haldenbilen S, Ceylan H. Genetic Algorithm Approach to Estimate Transport Energy Demand in Turkey. Energy Policy. 2005;33: 89-98.
[11] Canyurt EO, Ceylan H, Öztürk HK, Hepbaşlı A. Energy Demand Estimation Based on Two-different Genetic Algorithm Approaches. Energy Sources. 2004;26: 1313-1320.
[12] Ediger VŞ, Çamdalı, Ü. Energy and Exergy Efficiencies in Turkish Transportation Sector. Energy Policy. 2007;35: 1238-1244.
[13] Utlu Z, Hepbaşlı, A. Assessment of the Energy Utilization Efficiency in the Turkish Transportation Sector Between 2000 and 2020 Using Energy and Exergy Analysis Method. Energy Policy. 2006;34(13): 1611-1618.
[14] Saidur R, Sattara MA, Masjukia HH, Ahmed S, Hashim U. An Estimation of the Energy and Exergy Efficiencies for the Energy Resources Consumption in the Transportation Sector in Malaysia. Energy Policy. 2007;35: 4018-4026.
[15] Wohlgemuth N. World Transport Energy Demand Modeling: Methodology and Elasticities. Energy Policy. 1997; 25(14–15): 1109–1119.
[16] Geem WZ. Transport Energy Demand Modeling of South Korea Using Artificial Neural Network. Energy Policy. 2011;39: 4644-2050.
[17] Ceylan H, Ceylan H, Haldenbilen S, Başkan O. Transport Energy Modeling with Meta-heuristic Harmony Search Algorithm, an Application to Turkey. Energy Policy. 2008;36: 2527-2535.
[18] Limanond T, Jommonkwao S, Srikaew A. Projection of Future Transport Energy Demand of Thailand. Energy Policy. 2011;39: 2754-2763.
[19] Bose RK, Srinivivasachary V. Policies to Reduce Energy Use and Environmental Emissions in the Transport Sector: A Case of Delhi City. Energy Policy. 1997;25(14-15): 1137-1150.
[20] Shabbir R, Ahmad SS. Monitoring Urban Transport Air Pollution and Energy Demand in Rawalpindi and Islamabad Using Leap Model. Energy. 2010;35: 2323-2332.
[21] Başkan O, Haldenbilen S, Ceylan H, Ceylan H. Estimating Transportation Energy Demand Using Ant Colony Optimization. Energy Sources. 2015;7(2): 188-199.
[22] Zhang M, Mu H, Li G, Ning Y. Forecasting the Transport Energy Demand Based on PLSR Method in China. Energy. 2009;34(9): 1396-1400.
[23] Sönmez M, Akgüngör AP, Bektaş S. Estimating Transportation Energy Demand in Turkey Using the Artificial Bee Colony Algorithm. Energy. 2017;122: 301-310.
[24] Gonzalez PA, Zamarreno, JM. Prediction of Hourly Energy Consumption in Buildings Based on a Feedback Artificial Neural Network. Energy and Buildings. 2005;37: 595-601.
[25] Kalogirou S, Bojic M. Artificial Neural Networks for the Prediction of the Energy Consumption of a Passive Solar Building. Energy. 2000;25(5): 479-491.
[26] Çodur MY, Tortum A. An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. Promet – Traffic & Transportation. 2015;27(3): 217-225.
[27] Wei X, Xu G, Kusiak A. Modeling and Optimization of a Chiller Plant. Energy. 2014;73: 898-907.
Published
2019-03-26
How to Cite
1.
Çodur MY, Ünal A. An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks. Promet [Internet]. 2019Mar.26 [cited 2024Nov.27];31(2):151-6. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3041
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Copyright (c) 2019 Muhammed Yasin Çodur; Ahmet Ünal
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