IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v35y2014icp382-389.html
   My bibliography  Save this article

Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections

Author

Listed:
  • Aydin, Gokhan

Abstract

Modeling and forecasting of the primary energy consumption (PEC) play a vital role for policy makers and related organizations in developing countries such as Turkey. In this study, Turkey׳s PEC is modeled by regression analysis (RA) based on population (CP) and gross domestic product (GDP). The derived model is validated by various statistical approaches such as the determination coefficient, t-test, F-test, and residual analysis. Additionally, the performance of the derived model is assessed using mean absolute percentage error (MAPE), root mean square error (RMSE) and means absolute error (MAE). Three scenarios are used for forecasting Turkey׳s PEC in the years 2010–2025. For each scenario, various assumptions are made considering different growth rate for CP and GDP. Using the proposed model, Turkey׳ PEC is forecasted under different scenarios. The results show that the proposed model can be affectively used for forecasting of Turkey׳s PEC. The scenarios also show that the future energy consumption of Turkey would vary between 174.65 and 203.13Mtoe in 2025.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:35:y:2014:i:c:p:382-389
    DOI: 10.1016/j.rser.2014.04.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032114002317
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2014.04.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Aydin, Gokhan & Karakurt, Izzet & Aydiner, Kerim, 2010. "Evaluation of geologic storage options of CO2: Applicability, cost, storage capacity and safety," Energy Policy, Elsevier, vol. 38(9), pages 5072-5080, September.
    3. Shi, Jing & Guo, Jinmei & Zheng, Songtao, 2012. "Evaluation of hybrid forecasting approaches for wind speed and power generation time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3471-3480.
    4. 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.
    5. 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.
    6. Canyurt, Olcay Ersel & Ozturk, Harun Kemal & Hepbasli, Arif & Utlu, Zafer, 2005. "Estimating the Turkish residential-commercial energy output based on genetic algorithm (GA) approaches," Energy Policy, Elsevier, vol. 33(8), pages 1011-1019, May.
    7. 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.
    8. Canyurt, Olcay Ersel & Ozturk, Harun Kemal, 2008. "Application of genetic algorithm (GA) technique on demand estimation of fossil fuels in Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2562-2569, July.
    9. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
    10. Erdal, Gülistan & Erdal, Hilmi & Esengün, Kemal, 2008. "The causality between energy consumption and economic growth in Turkey," Energy Policy, Elsevier, vol. 36(10), pages 3838-3842, October.
    11. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    12. Ozturk, Harun Kemal & Ceylan, Halim & Hepbasli, Arif & Utlu, Zafer, 2004. "Estimating petroleum exergy production and consumption using vehicle ownership and GDP based on genetic algorithm approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(3), pages 289-302, June.
    13. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    14. 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.
    15. Friedl, Birgit & Getzner, Michael, 2003. "Determinants of CO2 emissions in a small open economy," Ecological Economics, Elsevier, vol. 45(1), pages 133-148, April.
    16. Sözen, Adnan, 2009. "Future projection of the energy dependency of Turkey using artificial neural network," Energy Policy, Elsevier, vol. 37(11), pages 4827-4833, November.
    17. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    18. Soyhan, Hakan S., 2009. "Sustainable energy production and consumption in Turkey: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1350-1360, August.
    19. Sozen, Adnan & Gulseven, Zafer & Arcaklioglu, Erol, 2007. "Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies," Energy Policy, Elsevier, vol. 35(12), pages 6491-6505, December.
    20. Hakan HOTUNLUOGLU & Etem KARAKAYA, 2011. "Forecasting Turkey’s Energy Demand Using Artificial Neural Networks: Three Scenario Applications," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 11(Special I), pages 87-94.
    21. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    22. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    23. Ozturk, Harun Kemal & Ceylan, Halim & Canyurt, Olcay Ersel & Hepbasli, Arif, 2005. "Electricity estimation using genetic algorithm approach: a case study of Turkey," Energy, Elsevier, vol. 30(7), pages 1003-1012.
    24. Hamzacebi, Coskun, 2007. "Forecasting of Turkey's net electricity energy consumption on sectoral bases," Energy Policy, Elsevier, vol. 35(3), pages 2009-2016, March.
    25. ToksarI, M. Duran, 2009. "Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey," Energy Policy, Elsevier, vol. 37(3), pages 1181-1187, March.
    26. Azadeh, A. & Tarverdian, S., 2007. "Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption," Energy Policy, Elsevier, vol. 35(10), pages 5229-5241, October.
    27. Tunc, Murat & Camdali, Unal & Liman, Tunc & Deger, Anil, 2006. "Electrical energy consumption and production of Turkey versus world," Energy Policy, Elsevier, vol. 34(17), pages 3284-3292, November.
    28. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    29. Köne, Aylin Çigdem & Büke, Tayfun, 2010. "Forecasting of CO2 emissions from fuel combustion using trend analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2906-2915, December.
    30. Karakurt, Izzet & Aydin, Gokhan & Aydiner, Kerim, 2012. "Sources and mitigation of methane emissions by sectors: A critical review," Renewable Energy, Elsevier, vol. 39(1), pages 40-48.
    31. O'Neill, Brian C. & Desai, Mausami, 2005. "Accuracy of past projections of US energy consumption," Energy Policy, Elsevier, vol. 33(8), pages 979-993, May.
    32. Utgikar, V.P. & Scott, J.P., 2006. "Energy forecasting: Predictions, reality and analysis of causes of error," Energy Policy, Elsevier, vol. 34(17), pages 3087-3092, November.
    33. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    34. 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.
    35. Chang, Yusang & Lee, Jinsoo & Yoon, Hyerim, 2012. "Alternative projection of the world energy consumption-in comparison with the 2010 international energy outlook," Energy Policy, Elsevier, vol. 50(C), pages 154-160.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. 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).
    4. 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.
    5. 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.
    6. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    7. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    8. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    9. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    10. Erdogdu, Erkan, 2010. "Natural gas demand in Turkey," Applied Energy, Elsevier, vol. 87(1), pages 211-219, January.
    11. Elsland, Rainer & Divrak, Can & Fleiter, Tobias & Wietschel, Martin, 2014. "Turkey’s Strategic Energy Efficiency Plan – An ex ante impact assessment of the residential sector," Energy Policy, Elsevier, vol. 70(C), pages 14-29.
    12. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
    13. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    14. Sumer, Kutluk Kagan & Goktas, Ozlem & Hepsag, Aycan, 2009. "The application of seasonal latent variable in forecasting electricity demand as an alternative method," Energy Policy, Elsevier, vol. 37(4), pages 1317-1322, April.
    15. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
    16. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    17. Azimi, Seyyed Shahabeddin & Namazi, Mohammad Hosain, 2015. "Modeling of combustion of gas oil and natural gas in a furnace: Comparison of combustion characteristics," Energy, Elsevier, vol. 93(P1), pages 458-465.
    18. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    19. 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.
    20. Colmenar, J.M. & Hidalgo, J.I. & Salcedo-Sanz, S., 2018. "Automatic generation of models for energy demand estimation using Grammatical Evolution," Energy, Elsevier, vol. 164(C), pages 183-193.

    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:rensus:v:35:y:2014:i:c:p:382-389. 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/wps/find/journaldescription.cws_home/600126/description#description .

    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.