Türkiye için gri kurt optimizasyon algoritması ile yapay sinir ağlarını kullanarak enerji tüketiminin tahmini

Year 2019, Volume: 7 Issue: 2, 245 - 262, 11.06.2019

Ergun Uzlu

https://doi.org/10.29109/gujsc.519553

Cited By: 6

Abstract

Bu
çalışmanın amacı gri kurt optimizasyon (GKO) algoritması ile eğitilmiş bir
yapay sinir ağı (YSA) modelini kullanarak Türkiye’nin enerji tüketimini tahmin
etmektir. Modelde gayri safi yurt içi hasıla, nüfus, ithalat ve ihracat
verileri bağımsız değişken olarak seçilmiştir. Sunulan modelin
uygulanabilirliğini ve doğruluğunu değerlendirmek için,  YSA-GKO modeli yapay arı kolonisi (YAK)
algoritması ve geri yayılımlı (GY) algoritma ile eğitilmiş YSA modelleri ile
karşılaştırılmıştır. Yapılan karşılaştırmalar YSA-GKO modelinin YSA-YAK ve
YSA-GY modellerinden daha üstün olduğunu göstermiştir. YSA-GKO modeli
kullanılarak Türkiye’nin enerji tüketimi iki farklı senaryoya göre 2023’e kadar
tahmin edilmiştir. Elde edilen sonuçlar Enerji ve Tabi Kaynaklar Bakanlığı ve
literatürdeki çalışmalardan elde edilen sonuçlarla karşılaştırılmıştır.
Sonuçlar YSA-GKO modelinin enerji tüketimi tahmininde kullanılabileceğini
göstermiştir.

Keywords

Gri Kurt Optimizasyon Algoritması; Yapay Arı Kolonisi Algoritması; Geri Yayılımlı Algoritma; Enerji Tüketimi/Talebi; Yapay Sinir Ağları; Türkiye

References

• Kaynaklar (References)
• [1] Boru Hatları İle Petrol Taşıma Anonim Şirketi (BOTAŞ). 2015 Sektör raporu<https://www.botas.gov.tr/docs/raporlar/tur/sektorap_2015.pdf>
• [2] Enerji ve Tabi Kaynaklar Bakanlığı (ETKB). İstatistikler, denge tabloları<http://www.eigm.gov.tr/tr-TR/Denge-Tablolari/Denge-Tablolari>
• [3] Enerji ve Tabi Kaynaklar Bakanlığı (ETKB). Enerji ve tabi kaynaklar bakanlığı ile bağlı ve ilgili kuruluşlarının amaç ve faliyetleri. <http://www.enerji.gov.tr/File/?path=ROOT%2F1%2FDocuments%2FMavi%20Kitap%2FMavi_Kitap_2012.pdf>
• [4] E. UZLU, M. Kankal, A. Akpınar, T. Dede, Estimates of energy consumption in Turkey using neural networks with the teaching-learning-based optimization algorithm, Energy 75 (2014) 295–303.
• [5] B. Guo, Y. Wang, A. Zhang, China's energy future: leap tool application in China, East Asia Energy Futures (EAEF). In: Asia energy security project energy paths analysis, methods training workshop, vancouver, British Columbia, Canada;2003.
• [6] S.W. Yu, Y.M. Wei, K. Wang, A PSO-GA optimal model to estimate primary energy, Energy Policy 42 (2012) 329–340.
• [7] Y. Fan, Y. Xia, Exploring energy consumption and demand in China, Energy 40 (2012) 23–30.
• [8] Y.R. Zeng, Y. Zeng, B. Choi, L. Wang, Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network, Energy 127 (2017) 381–396.
• [9] X. Wang, D.L. Xu, Z.Z. Sun. Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation, Energy 152 (2018) 539–548.
• [10] Z.W. Geem, W.E. Roper, Energy demand estimation of South Korea using artificial neural network, Energy Policy 37 (2009) 4049–4054.
• [11] L. Ekonomou, Greek long–term energy consumption prediction using artificial neural networks, Energy 35 (2010) 512–517.
• [12] O.A. Oludolapo, A.A. Jimoh, P.A. Kholopane, Comparing performance of MLP and RBF neural network models for predicting South Africa’s energy consumption, Journal of Energy in South Africa 23 (2012) 40–6.
• [13] K. Muralitharan, R. Sakthivel, R. Vishnuvarthan, Neural network based optimization approach for energy demand prediction in smart grid, Neurocomputing 273 (2018) 199–208.
• [14] P. Sutthichaimethee, D. Ariyasajjakorn, Fore¬casting energy consumption in short-term and long-term period by using arimax model in the construction and materials sector in Thailand, Journal of Ecological Engineering 18 (2017) 52–59.
• [15] M.A. Behrang, E. Assareh, M.R. Assari, A. Ghanbarzadeh, Total energy demand estimation in Iran using bees algorithm, Energy Sources, B: Economics, Planning and Policy 6 (2011) 294–303.
• [16] A. Sozen, E. Arcaklioglu, Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey, Energy Policy 35 (2007) 4981–4992.[17] A. Sozen, E. Arcaklioglu, M. Ozkaymak, Turkey’s net energy consumption, Applied Energy 81 (2005) 209–221.
• [18] A. Sozen, M.A. Akcayol, E. Arcaklioglu, Forecasting net energy consumption using artificial neural network, Energy Sources, B: Economics, Planning and Policy 1 (2006) 147–155.
• [19] M. Kankal, A. Akpinar, M.I. Komurcu, T.S. Ozsahin, Modeling and forecasting of Turkey’s energy consumption using socio–economic and demographic variables, Applied Energy 88 (2011) 1927–1939.
• [20] H.A. Es, F.Y. Kalender, C. Hamzaçebi, Forecasting the net energy demand of Turkey by artificial neural networks, Journal of the Faculty of Engineering and Architecture of Gazi University 29 (2014) 495–504.
• [21] H.K. Ozturk, O.E. Canyurt, A. Hepbasli, Z. Utlu, Residential–commercial energy input estimation based on genetic algorithm approaches: an application of Turkey, Energy and Buildings 36 (2004) 175–183.
• [22] O.E. Canyurt, H. Ceylan, H.K. Ozturk, A. Hepbasli, Energy demand estimation based on two–different genetic algorithm approaches, Energy Sources 26 (2004) 1313–1320.
• [23] H. Ceylan, H.K. Ozturk, A. Hepbasli, Z. Utlu Estimating energy and exergy production and consumption values using three different genetic algorithm approaches, part 2: application and scenarios, Energy Sources 27 (2005) 629–639.
• [24] H. Ceylan, H.K. Ozturk, Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach, Energy Conversion and Management 45 (2004) 2525–2537.
• [25] M.D. Toksari, Ant colony optimization approach to estimate energy demand of Turkey, Energy Policy 35 (2007) 3984–3990.
• [26] A. Unler, Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025, Energy Policy 36 (2008) 1937–1944.
• [27] M.S. Kıran, M. Gunduz, A recombination–based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems, Applied Soft Computing 13 (2013) 2188–2203.
• [28] M.S. Kıran, E. Ozceylan, M. Gunduz, T. Paksoy, A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey, Energy Conversion and Management 53 (2012) 75–83.[29] V.S. Ediger, S. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Policy 35 (2007) 1701–1708.
• [30] M. Mucuk, D. Uysal Turkey’s energy demand, Current Research Journal of Social Sciences 1 (2009) 123–128.
• [31] N.P. Say, M. Yucel, Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth, Energy Policy 34 (2006) 3870–3876.
• [32] G. Aydin, Modeling of energy consumption based on economic and demographic factors: the case of Turkey with projections, Renewable and Sustainable Energy Reviews 35 (2014) 382–389.
• [33] V.S. Ediger, H. Tatlidil, Forecasting the primary energy demand in Turkey and analysis of cyclic patterns, Energy Conversion and Management 43 (2002) 473–487.
• [34] M.F. Tefek, H. Uğuz, M. Güçyetmez, A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey, Neural Computing and Applications 2017. http://dx.doi.org/10.1007/s00521-017-3244-9.
• [35] E. Uzlu, A. Akpınar, H.T. Öztürk, S. Nacar, M. Kankal, Estimates of hydroelectric generation using neural networks with artificial bee colony algorithm for Turkey, Energy 69 (2014) 638–647.
• [36] M. Kankal, E. Uzlu, Neural network approach with teaching-learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey, Neural Computing and Applications 28 (2017) 737–747.
• [37] D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning representations by back–propagating errors, Nature 323 (1986) 533–536.
• [38] D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University Engineering Faculty Computer Engineering Department; 2005.
• [39] S. Mirjalili, M.S. Mirjalili, A. Lewis, Grey wolf optimizer. Advances in Engineering Software 69 (2014) 46–61. [40] S. Mirjalili, How effective is the Grey Wolf optimizer in training multi-layer perceptrons, Applied Intelligence 43 (2015) 150–161.
• [41] Türkiye İstatistik Kurumu (TÜİK). Nüfus ve demografi, nüfus istatistikleri.<http://www.tuik.gov.tr/UstMenu.do?metod=temelist>
• [42] Türkiye İstatistik Kurumu (TÜİK). Dış ticaret, yıllara göre dış ticaret.<http://www.tuik.gov.tr/UstMenu.do?metod=temelist>
• [43] Strateji ve Bütçe Başkanlığı (SBB), Ekoomik ve sosyal göstegeler, milli gelir ve ürerim <http://www.sbb.gov.tr/Pages/EkonomikSosyalGostergeler.aspx>
• [44] Türkiye İhracatçılar Meclisi (TİM). 2023 Türkiye ihracat stratejisinin uygulamaya aktarılması ve sektörel kırılımı. http://www.tim.org.tr/files/downloads/2023/tim%202023%20ihracat%20stratejisi%20raporu.pdf