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The energy demand estimation for Turkey using differential evolution algorithm

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Abstract

The energy demand estimation commands great importance for both developing and developed countries in terms of the economy and country resources. In this study, the differential evolution algorithm (DE) was used to forecast the long-term energy demand in Turkey. In addition to being employed for solving regular optimization problems, DE is also a global, meta-heuristic algorithm that enables fast, reliable and operative stochastic searches based on population. Considering the correlation between the increase in certain economic indicators in Turkey and the increase of energy consumption, two equations were used—one applying the linear form and the other the quadratic form. Turkey’s long-term energy demand from 2012 to 2031 was estimated through the DE method in three different scenarios and in terms of the gross domestic product, import, export and population. To prove the success of the DE method in addressing the energy demand problem, the DE method was compared to other methods found in the literature. Results showed that the proposed DE method was more successful than the other methods. Furthermore, the future projections of energy demand obtained using the proposed method were compared to the indicators of energy demand estimated and observed by the Ministry of Energy and Natural Resources.

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References

  1. Güneş G and Aslan E 2015 Use of renewable energy sources and its effects to sustainable tourism – Turkey example. Doğu Karadeniz Bölgesi Sürdürülebilir Turizm Kongresi. Gümüşhane Üniversitesi Yayınları-31, Gümüshane, Turkey, pp. 221–234

  2. Oğurlu H 2011 Matematiksel Modelleme Kullanarak Türkiye’nin Uzun Dönem Elektrik Yük Tahmini. Selçuk Üniversitesi Fen Bilimleri Enstitüsü

  3. Mahmutoğlu M and Öztürk F 2015 Türkiye Elektrik Tüketimi Öngörüsü ve Bu Kapsamda Geliştirilebilecek Politika Önerileri. In: Proceedings of EY International Congress on Economics II (EYC2015), November 5–6, Ankara, Turkey. Ekonomik Yaklasim Association

  4. Erdogdu E 2007 Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey. Energy Policy 35: 1129–1146

    Article  Google Scholar 

  5. Tiris M 2005 Global trends for energy. In: Proceedings of the Turkish Workshop on Sustainable Development: Meeting the Challenges, JuØlich

  6. WECTNC 2013 Energy report-2013. Ankara: World Energy Council (in Turkish), ISSN: 1301-63182014

    Google Scholar 

  7. WECTNC 2014 Energy report-2014. Ankara: World Energy Council (in Turkish), ISSN: 1301-63182015

    Google Scholar 

  8. Kıran M S, Özceylan E, Gündüz M and Paksoy T 2012 A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Convers. Manage. 53: 75–83

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. TMMOB 2014 Türkiye’nin enerji görünümü. MMO2014

  11. Ediger V Ş and Akar S 2007 ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35: 1701–1708

    Article  Google Scholar 

  12. Ediger V Ş and Tatlıdil H 2002 Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Convers. Manage. 43: 473–487

    Article  Google Scholar 

  13. Yumurtaci Z and Asmaz E 2004 Electric energy demand of Turkey for the year 2050. Energy Sources 26: 1157–1164

    Article  Google Scholar 

  14. Akkurt M, Demirel O F and Zaim S 2016 Forecasting Turkey’s natural gas consumption by using time series methods. Eur. J. Econ. Polit. Stud. 3: 1–21

    Google Scholar 

  15. Mucuk M and Uysal D 2009 Turkey’s energy demand. Curr. Res. J. Soc. Sci. 1: 123–128

    Google Scholar 

  16. Dilaver Z and Hunt L C 2011 Industrial electricity demand for Turkey: a structural time series analysis. Energy Econ. 33: 426–436

    Article  Google Scholar 

  17. Sözen A and Arcaklioğlu E 2007 Prospects for future projections of the basic energy sources in Turkey. Energy Sources Part B 2: 183–201

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Sozen A, Arcaklioglu E and Ozkaymak M 2005 Modelling of Turkey’s net energy consumption using artificial neural network. Int. J. Comput. Appl. Technol. 22: 130–136

    Article  Google Scholar 

  20. Ceylan H and Ozturk H K 2004 Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Convers. Manage. 45: 2525–2537

    Article  Google Scholar 

  21. Haldenbilen S and Ceylan H 2005 Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy 33: 89–98

    Article  Google Scholar 

  22. Toksarı M D 2007 Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy 35: 3984–3990

    Article  Google Scholar 

  23. Kıran M S and Gündüz M 2013 A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl. Soft Comput. 13: 2188–2203

    Article  Google Scholar 

  24. Uguz H and Hakli H 2015 A new algorithm based on artificial bee colony algorithm for energy demand forecasting in Turkey. In: Proceedings of the 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), IEEE2015, pp. 56–61

  25. Kıran M S, Özceylan E, Gündüz M and Paksoy T 2012 Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowl.-Based Syst. 36: 93–103

    Article  Google Scholar 

  26. Ghanbari A, Kazemi S M R, Mehmanpazir F and Nakhostin M M 2013 A Cooperative Ant Colony Optimization-Genetic Algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowl.-Based Syst. 39: 194–206

    Article  Google Scholar 

  27. Salcedo-Sanz S, Muñoz-Bulnes J, Portilla-Figueras J A and Del Ser J 2015 One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Convers. Manage. 99: 62–71

    Article  Google Scholar 

  28. Sánchez-Oro J, Duarte A and Salcedo-Sanz S 2016 Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm. Energy Convers. Manage. 123: 445–452

    Article  Google Scholar 

  29. Özdemir M 2013 Zaman Kısıtı Altında Takım Oryantiring Problemlerinin Yapay Arı Kolonisi Yaklaşımı ile Çözümü. İstanbul Üniversitesi Sosyal Bilimler Enstitüsü

  30. Qin A K, Huang V L and Suganthan P N 2009 Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13: 398–417

    Article  Google Scholar 

  31. Jędrzejowicz P and Skakovski A 2014 Island-based differential evolution algorithm for the discrete–continuous scheduling with continuous resource discretisation. Proc. Comput. Sci. 35: 111–117

    Article  Google Scholar 

  32. Liu Y, Rowe M, Holderbaum W and Potter B 2016 A novel battery network modelling using constraint differential evolution algorithm optimisation. Knowl.-Based Syst. 99: 10–18

    Article  Google Scholar 

  33. Mlakar U, Potočnik B and Brest J 2016 A hybrid differential evolution for optimal multilevel image thresholding. Expert Syst. Appl. 65: 221–232

    Article  Google Scholar 

  34. Rogalsky T, Kocabiyik S and Derksen R 2000 Differential evolution in aerodynamic optimization. Can. Aeronaut. Space J. 46: 183–190

    Google Scholar 

  35. Storn R 1996 On the usage of differential evolution for function optimization. In: Proceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS, IEEE1996, pp. 519–523

  36. Parouha R P and Das K N 2016 A robust memory based hybrid differential evolution for continuous optimization problem. Knowl.-Based Syst. 103: 118–131

    Article  Google Scholar 

  37. Zorarpacı E and Özel S A 2016 A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Syst. Appl. 62: 91–103

    Article  Google Scholar 

  38. Maciel L, Gomide F and Ballini R 2016 A differential evolution algorithm for yield curve estimation. Math. Comput. Simul. 129: 10–30

    Article  MathSciNet  Google Scholar 

  39. Storn R and Price K 1997 Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optimizat. 11: 341–359

    Article  MATH  MathSciNet  Google Scholar 

  40. Price K V 1999 An introduction to differential evolution. In: New ideas in optimization. Maidenhead, UK: McGraw-Hill

  41. Price K, Storn R M and Lampinen J A 2006 Differential evolution: a practical approach to global optimization. Springer Science & Business Media

  42. Storn R and Price K 1995 Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley: ICSI

    MATH  Google Scholar 

  43. Gämperle R, Müller S D and Koumoutsakos P 2002 A parameter study for differential evolution. Adv. Intell. Syst. Fuzzy Syst. Evol. Comput. 10: 293–298

    Google Scholar 

  44. Karaboğa D 2014 Yapay Zeka Optimizasyon Algoritmalari

  45. Hrstka O and Kučerová A 2004 Improvements of real coded genetic algorithms based on differential operators preventing premature convergence. Adv. Eng. Softw. 35: 237–246

    Article  Google Scholar 

  46. Mayer D, Kinghorn B and Archer A 2005 Differential evolution—an easy and efficient evolutionary algorithm for model optimisation. Agric. Syst. 83: 315–328

    Article  Google Scholar 

  47. Özsağlam M Y and Cunkaş M 2008 Optimizasyon Problemlerinin Çözümü için Parçaçık Sürü Optimizasyonu Algoritması. Politek. Dergisi 11: 299–305

    Google Scholar 

  48. Ronkkonen J, Kukkonen S and Price K V 2005 Real-parameter optimization with differential evolution. In: Proceedings of IEEE CEC2005, pp. 506–513

  49. Bulut Y M and Yıldız Z 2016 Comparing energy demand estimation using various statistical methods: the case of Turkey. Gazi Univ. J. Sci. 29: 237–244

    Google Scholar 

  50. National Statistics (NS) 2016 http://www.tuik.gov.tr (in Turkish)

  51. Central Bank of Turkey (CBT) 2016 http://www.tcmb.gov.tr (in Turkish)

  52. TMMOB 2014 Elektrik enerjisi talep tahmin yöntemleri paneli (e-book, in Turkish)

  53. Geem Z W and Roper W E 2009 Energy demand estimation of South Korea using artificial neural network. Energy Policy 37: 4049–4054

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Scientific Research Project of Selcuk University.

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Authors and Affiliations

  1. Department of Computer Engineering, Selcuk University, 42075, Konya, Turkey

    MEHMET BESKIRLI, HUSEYIN HAKLI & HALIFE KODAZ

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  1. MEHMET BESKIRLI
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  2. HUSEYIN HAKLI
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Correspondence to MEHMET BESKIRLI.

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BESKIRLI, M., HAKLI, H. & KODAZ, H. The energy demand estimation for Turkey using differential evolution algorithm. Sādhanā 42, 1705–1715 (2017). https://doi.org/10.1007/s12046-017-0724-7

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  • DOI: https://doi.org/10.1007/s12046-017-0724-7

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