IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v36y2008i7p2527-2535.html
   My bibliography  Save this article

Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey

Author

Listed:
  • Ceylan, Huseyin
  • Ceylan, Halim
  • Haldenbilen, Soner
  • Baskan, Ozgur

Abstract

This study proposes a new method for estimating transport energy demand using a harmony search (HS) approach. HArmony Search Transport Energy Demand Estimation (HASTEDE) models are developedtaking population, gross domestic product and vehicle kilometers as an input. The HASTEDE models are in forms of linear, exponential and quadratic mathematical expressions and they are applied to Turkish Transportation sector energy consumption. Optimum or near-optimum values of the HS parameters are obtained with sensitivity analysis (SA). Performance of all models is compared with the Ministry of Energy and Natural Resources (MENR) projections. Results showed that HS algorithm may be used for energy modeling, but SA is required to obtain best values of the HS parameters. The quadratic form of HASTEDE will overestimate transport sector energy consumption by about 26% and linear and exponential forms underestimate by about 21% when they are compared with the MENR projections. This may happen due to the modeling procedure and selected parameters for models, but determining the upper and lower values of transportation sector energy consumption will provide a framework and flexibility for setting up energy policies.

Suggested Citation

  • Ceylan, Huseyin & Ceylan, Halim & Haldenbilen, Soner & Baskan, Ozgur, 2008. "Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2527-2535, July.
    • Handle: RePEc:eee:enepol:v:36:y:2008:i:7:p:2527-2535
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301-4215(08)00157-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Say, Nuriye Peker & Yucel, Muzaffer, 2006. "Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth," Energy Policy, Elsevier, vol. 34(18), pages 3870-3876, December.
    2. Kavrakoglu, Ibrahim, 1983. "Modelling energy-economy interactions," European Journal of Operational Research, Elsevier, vol. 13(1), pages 29-40, May.
    3. Ediger, Volkan S. & Camdali, Unal, 2007. "Energy and exergy efficiencies in Turkish transportation sector, 1988-2004," Energy Policy, Elsevier, vol. 35(2), pages 1238-1244, February.
    4. Saidur, R. & Sattar, M.A. & Masjuki, H.H. & Ahmed, S. & Hashim, U., 2007. "An estimation of the energy and exergy efficiencies for the energy resources consumption in the transportation sector in Malaysia," Energy Policy, Elsevier, vol. 35(8), pages 4018-4026, August.
    5. Ebohon, Obas John, 1996. "Energy, economic growth and causality in developing countries : A case study of Tanzania and Nigeria," Energy Policy, Elsevier, vol. 24(5), pages 447-453, May.
    6. Cheng, Benjamin S. & Lai, Tin Wei, 1997. "An investigation of co-integration and causality between energy consumption and economic activity in Taiwan," Energy Economics, Elsevier, vol. 19(4), pages 435-444, October.
    7. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    8. Wohlgemuth, Norbert, 1997. "World transport energy demand modelling : Methodology and elasticities," Energy Policy, Elsevier, vol. 25(14-15), pages 1109-1119, December.
    9. Duran Toksari, M., 2007. "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, Elsevier, vol. 35(8), pages 3984-3990, August.
    10. Haldenbilen, Soner & Ceylan, Halim, 2005. "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, Elsevier, vol. 33(1), pages 89-98, January.
    11. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    12. Utlu, Zafer & Hepbasli, Arif, 2006. "Assessment of the energy utilization efficiency in the Turkish transportation sector between 2000 and 2020 using energy and exergy analysis method," Energy Policy, Elsevier, vol. 34(13), pages 1611-1618, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Altay, Elif Varol & Alatas, Bilal, 2020. "Randomness as source for inspiring solution search methods: Music based approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. A. Talha Yalta, 2013. "The Dynamics of Road Energy Demand and Illegal Fuel Activity in Turkey: A Rolling Window Analysis," Working Papers 1304, TOBB University of Economics and Technology, Department of Economics, revised Jul 2013.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. Llorca, Manuel & Baños, José & Somoza, José & Arbués, Pelayo, 2014. "A latent class approach for estimating energy demands and efficiency in transport: An application to Latin America and the Caribbean," Efficiency Series Papers 2014/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    5. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    6. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    7. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    8. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    9. Tamás Bányai & Péter Veres, 2013. "Optimisation Of Knapsack Problem With Matlab, Based On Harmony Search Algorithm," Advanced Logistic systems, University of Miskolc, Department of Material Handling and Logistics, vol. 7(1), pages 13-20, December.
    10. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    11. Hoxha, Julian & Çodur, Muhammed Yasin & Mustafaraj, Enea & Kanj, Hassan & El Masri, Ali, 2023. "Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis," Applied Energy, Elsevier, vol. 350(C).
    12. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    13. M Laguna & J Molina & F Pérez & R Caballero & A G Hernández-Díaz, 2010. "The challenge of optimizing expensive black boxes: a scatter search/rough set theory approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 53-67, January.
    14. Valian, Ehsan & Tavakoli, Saeed & Mohanna, Shahram, 2014. "An intelligent global harmony search approach to continuous optimization problems," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 670-684.
    15. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    16. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    17. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    18. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    19. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    20. Ratanavaraha, Vatanavongs & Jomnonkwao, Sajjakaj, 2015. "Trends in Thailand CO2 emissions in the transportation sector and Policy Mitigation," Transport Policy, Elsevier, vol. 41(C), pages 136-146.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Duran Toksari, M., 2007. "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, Elsevier, vol. 35(8), pages 3984-3990, August.
    2. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    3. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    4. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.
    5. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    6. Ünler, Alper, 2008. "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, Elsevier, vol. 36(6), pages 1937-1944, June.
    7. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    8. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    9. Zhang, M. & Li, G. & Mu, H.L. & Ning, Y.D., 2011. "Energy and exergy efficiencies in the Chinese transportation sector, 1980–2009," Energy, Elsevier, vol. 36(2), pages 770-776.
    10. Llorca, Manuel & Baños, José & Somoza, José & Arbués, Pelayo, 2014. "A latent class approach for estimating energy demands and efficiency in transport: An application to Latin America and the Caribbean," Efficiency Series Papers 2014/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    11. Feng, Chao & Wang, Miao, 2018. "Analysis of energy efficiency in China's transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 565-575.
    12. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    13. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    14. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    15. Motasemi, F. & Afzal, Muhammad T. & Salema, Arshad Adam & Moghavvemi, M. & Shekarchian, M. & Zarifi, F. & Mohsin, R., 2014. "Energy and exergy utilization efficiencies and emission performance of Canadian transportation sector, 1990–2035," Energy, Elsevier, vol. 64(C), pages 355-366.
    16. Gasparatos, Alexandros & El-Haram, Mohamed & Horner, Malcolm, 2009. "A longitudinal analysis of the UK transport sector, 1970-2010," Energy Policy, Elsevier, vol. 37(2), pages 623-632, February.
    17. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    18. Abdulkerim Karaaslan & Mesliha Gezen, 2017. "Forecasting of Turkey s Sectoral Energy Demand by Using Fuzzy Grey Regression Model," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 67-77.
    19. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    20. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:36:y:2008:i:7:p:2527-2535. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.