Skip to main content

Advertisement

SpringerLink
Log in

Artificial Intelligence Approaches to Estimate the Transport Energy Demand in Turkey

  • Research Article-Mechanical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. World Bank national accounts data, and OECD National Accounts data files (2019). http://data.worldbank.org/country/turkey. Accessed 3 Dec 2019

  2. Number of Registered Automobiles Statistics for Turkey. Turkish Statistical Institute (2019). http://www.tuik.gov.tr/. Accessed 3 Dec 2019

  3. Energy Balance Sheets of Turkey for 2018. General Directorate of Energy Affairs (2019). https://www.eigm.gov.tr/tr-TR/Denge-Tablolari/Denge-Tablolari. Accessed 3 Dec 2019

  4. Kraft, J.; Kraft, A.: On the relationship between energy and GDP. J. Energy Dev. 3, 401–403 (1978)

    Google Scholar 

  5. Sims, C.A.: Money, income, and causality. Am. Econ. Rev. 62, 540–552 (1972)

    Google Scholar 

  6. Hwang, D.; Gum, B.: The causal relationship between energy and GDP: the case of Taiwan. J. Energy Dev. 16, 219–226 (1992)

  7. Yu, S.H.; Choi, J.Y.: The causal relationship between energy and GDP: an international comparison. J. Energy Dev. 10, 249–272 (1985)

    Article  Google Scholar 

  8. Sözen, A.; Arcaklioglu, E.: Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy 35, 4981–4992 (2007)

    Article  Google Scholar 

  9. Soytas, U.; Sarı, R.; Özdemir, O.: Energy consumption and GDP relation in Turkey: a cointegration and vector error correction analysis. In: Economies and Business in Transition: Facilitating Competitiveness and Change in the Global Environment Proceedings, pp. 838–844 (2001)

  10. Altınay, G.; Karagöl, E.: Structural break, unit root, and the causality between energy consumption and GDP in Turkey. Energy Econ. 26, 985–994 (2004)

    Article  Google Scholar 

  11. Say, N.P.; Yucel, M.: Energy consumption and CO2 emissions in Turkey: empirical analysis and future projection based on economic growth. Energy Policy 34, 3870–3876 (2006)

    Article  Google Scholar 

  12. Lise, W.; Montfort, K.V.: Energy consumption and GDP in Turkey: is there a co-integration relationship? Energy Econ. 29, 1166–1178 (2007)

    Article  Google Scholar 

  13. Kankal, M.; Akpınar, A.; Kömürcü, Mİ.; Özşahin, T.Ş.: Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Appl. Energy 88, 1927–1939 (2011)

    Article  Google Scholar 

  14. Fumo, N.; Biswas, M.A.R.: Regression analysis for prediction of residential energy consumption. Renew. Sust. Energy 47, 332–343 (2015)

    Article  Google Scholar 

  15. Iniyan, S.; Sumathy, K.: The application of a Delphi technique in the linear programming optimization of future renewable energy options for India. Biomass Bioenergy 24, 39–50 (2003)

    Article  Google Scholar 

  16. Ozturk, H.K.; Ceylan, H.; Canyurt, O.E.; Hepbasli, A.: Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy 30, 1003–1012 (2005)

    Article  Google Scholar 

  17. Toksari, M.D.: Ant colony optimization approach to estimate energy demand in Turkey. Energy Policy 35, 3984–3990 (2007)

    Article  Google Scholar 

  18. Ünler, A.: Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025. Energy Policy 36, 1937–1944 (2008)

    Article  Google Scholar 

  19. WECTNC. World Energy Council, Ankara. Energy Report (2014) (in Turkish)

  20. Hepbasli, A.; Oturanc, G.; Kurnaz, A.; Ergin, E.; Genc, A.; Iyit, N.: Simple correlations for estimating the energy production of Turkey. Energy Sources 24, 855–867 (2002)

    Article  Google Scholar 

  21. Yan, X.; Chang, Y.; Qian, X.: The properties of an aluminum/UV-curable, infrared, low-emissivity coating modified by nano-silica slurry. Coatings 10, 382 (2020)

    Article  Google Scholar 

  22. Yan, X.; Chang, Y.; Qian, X.: Effect of concentration of thermochromic ink on performance of waterborne finish films for the surface of Cunninghamia lanceolata. Polymers 12, 552 (2020)

    Article  Google Scholar 

  23. Haldenbilen, S.; Ceylan, H.: Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy 33, 89–98 (2005)

    Article  Google Scholar 

  24. Canyurt, O.E.; Ozturk, H.K.; Hepbasli, A.; Utlu, Z.: Genetic algorithm (GA) approaches for the transport energy demand estimation: model development and application. Energy Sources Part A 28, 1405–1413 (2006)

    Article  Google Scholar 

  25. Murat, Y.S.; Ceylan, H.: Use of artificial neural networks for transport energy demand modeling. Energy Policy 34, 3165–3172 (2006)

    Article  Google Scholar 

  26. Ceylan, H.; Ceylan, H.; Haldenbilen, S.; Baskan, O.: Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey. Energy Policy 36, 2527–2535 (2008)

    Article  Google Scholar 

  27. Average Annual Crude Oil Prices for US Crude oil (2019). http://inflationdata.com/Inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp, Accessed 3 Dec 2019

  28. Circulation and Transportation on Motorways, State and Provincial Roads, Turkish Ministry of Transport Maritime Affairs and Communications, General Directorate of Highways, Department of Traffic Safety (2019). http://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Istatistikler/DevletveIlYolEnvanteri.aspx. Accessed 3 Dec 2019

  29. Henry, P.V.; Aznar, J.C.: Tool-use in Charadrii: active bait-fishing by a Herring Gull. Waterbirds 29, 233–234 (2006)

    Article  Google Scholar 

  30. Dhiman, G.; Kumar, V.: Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl. Based Syst. 165, 53–76 (2019)

    Article  Google Scholar 

  31. Vommi, V.B.; Vermula, R.: A very optimistic method of minimization (VOMMI) for unconstrained problems. Inf. Sci. 454–455, 255–274 (2018)

    Article  MathSciNet  Google Scholar 

  32. May, R.: Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976)

    Article  Google Scholar 

  33. Adarsh, B.R.; Raghunathan, T.; Jayabarathi, T.; Yang, X.S.: Economic dispatch using chaotic bat algorithm. Energy 96, 666–675 (2016)

    Article  Google Scholar 

  34. Kaur, G.; Arora, S.: Chaotic whale optimization algorithm. J. Comput. Des. Eng. 5, 275–284 (2018)

    Google Scholar 

  35. Mousavi, Y.; Alfi, A.: Fractional calculus-based firefly algorithm applied to parameter estimation of chaotic systems. Chaos. Solut. Fract. 114, 202–215 (2018)

    Article  Google Scholar 

  36. Alatas, B.: Chaotic harmony search algorithms. Appl. Math. Comput. 216, 2687–2699 (2010)

    Article  Google Scholar 

  37. Chou, J.S.; Ngo, N.T.: Modified firefly algorithm for multidimensional optimization in structural design problems. Struct. Multidiscip. Optim. 55, 2013–2028 (2017)

    Article  Google Scholar 

  38. Yao, X.; Liu, G.: Evolutionary programming made faster. IEEE Trans. Evolut. Comput. 3, 82–102 (1999)

    Article  Google Scholar 

  39. Arora, S.; Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23, 715–734 (2019)

    Article  Google Scholar 

  40. Heidari, A.A.; Mirjalili, S.; Faris, H.; Aljarah, I.; Mafarja, M.; Chen, H.: Harris Hawks optimization: algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  41. Kaveh, A.; Kaveh, V.; Ghazaan, M.I.: A new meta-heuristic algorithm: vibrating particles system. Sci. Iran 24, 551–566 (2017)

    Google Scholar 

  42. Moosavi, S.H.S.; Bardsiri, V.K.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 60, 1–15 (2017)

    Article  Google Scholar 

  43. Dhiman, G.; Kumar, V.: Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl. Based Syst. 159, 20–50 (2018)

    Article  Google Scholar 

  44. Dhiman, G.; Kumar, V.: Spotted hyena optimizer: a novel bioinspired based metaheuristic technique for engineering applications. Adv. Eng. Softw. 114, 48–70 (2017)

    Article  Google Scholar 

  45. Kıran, M.S.: TSA: tree-seed algorithm for continuous optimization. Expert Syst. Appl. 42, 6686–6698 (2015)

    Article  Google Scholar 

  46. Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl. Based Syst. 26, 69–74 (2012)

    Article  Google Scholar 

  47. Gandomi, A.H.; Yang, X.S.; Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2013)

    Article  Google Scholar 

  48. Hyndman, R.J.; Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 26, 1–22 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Ege University, 35040, Bornova, Izmir, Turkey

    Mert Sinan Turgut

  2. Department of Business, Faculty of Economics and Administrative Sciences, Izmir Bakırçay University, 35665, Menemen, Izmir, Turkey

    Uğur Eliiyi

  3. Department of Industrial Engineering, Faculty of Engineering and Architecture, Izmir Bakırçay University, 35665, Menemen, Izmir, Turkey

    Oguz Emrah Turgut & Deniz Türsel Eliiyi

  4. Department of Industrial Engineering, Faculty of Engineering, Yaşar University, 35100, Bornova, Izmir, Turkey

    Erdinç Öner

Authors
  1. Mert Sinan Turgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uğur Eliiyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oguz Emrah Turgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erdinç Öner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Deniz Türsel Eliiyi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mert Sinan Turgut.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Turgut, M.S., Eliiyi, U., Turgut, O.E. et al. Artificial Intelligence Approaches to Estimate the Transport Energy Demand in Turkey. Arab J Sci Eng 46, 2443–2476 (2021). https://doi.org/10.1007/s13369-020-05108-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05108-y

Keywords

Search

Navigation