Jaya algoritması ile optimize edilmiş yapay sinir ağlarını kullanarak Türkiye’de elektrik enerjisi tüketiminin tahmini

Year 2020, Volume: 8 Issue: 3, 511 - 528, 27.09.2020

Ergun Uzlu Tayfun Dede

https://doi.org/10.29109/gujsc.684334

Cited By: 1

Abstract

Bu çalışmanın temel amacı, Türkiye'nin gelecekteki elektrik enerjisi tüketimini (EET) tahmin etmek için Jaya algoritması kullanılarak eğitilmiş bir yapay sinir ağ (YSA) modeli oluşturmaktır. Gayri safi yurtiçi hasıla (GSYİH), nüfus, ithalat ve ihracat verileri modelde bağımsız değişkenler olarak kullanılarak önerilen yöntem irdelenmiştir. Önerilen yöntemin doğruluğunu göstermek için YSA-Jaya diğer iki yüksek performanslı optimizasyon yöntemi olan yapay arı kolonisi (YAK) ve öğretme öğrenme tabanlı optimizasyon (ÖÖTO) algoritmaları eğitilmiş YSA modelleri ile karşılaştırılmıştır. YSA-Jaya modeli, test veri setinde YSA-YAK ve YSA-ÖÖTO modellerinden daha küçük hata değerlerine yakınsamıştır. Bu nedenle, YSA-Jaya algoritması kullanılarak Türkiye’nin EET projeksiyonu iki farklı senaryoya göre 2023 yılına kadar yapılmıştır. Sonuçlar TEİAŞ (Türkiye Elektrik İletim Kurumu) tarafından yapılan projeksiyonlar ve literatürdeki diğer ilgili çalışmalarla karşılaştırılmıştır. Sonuçlar, EET'nin YSA-Jaya kullanılarak doğru bir şekilde modellenebileceğini ve bu optimizasyon yönteminin gelecekteki elektrik tüketimini tahmin etmek için avantajlı olduğunu göstermektedir.

Keywords

Jaya Algoritması, Yapay Arı Kolonisi Algoritması, Öğretme Öğrenme Tabanlı Optimizasyon Algoritması, Elektrik Enerjisi Tüketimi, Yapay Sinir Ağları, Türkiye

References

• [1] Türkiye Elektrik İletim A.Ş. (TEİAŞ). Türkiye brüt elektrik enerjisi üretim-ithalat-ihracat ve talebinin yıllar itibariyle gelişimi. https://www.teias.gov.tr/tr/iii-elektrik-enerjisi-uretimi-tuketimi-kayiplar Erişim Tarihi Ağustos, 20, 2019.
• [2] S. Ding, K.W. Hipel, Y. Dang, Forecasting China's electricity consumption using a new grey prediction model, Energy 149 (2018) 314–28.
• [3] S.H.A. Kaboli, A. Fallahpour, J. Selvaraj, N.A. Rahim, Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming, Energy 126 (2017) 144-64.
• [4] N. Xu, Y. Dang, Y. Gong, Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China, Energy 118 (2017) 473–80.
• [5] A. Kasule, K. Ayan, Forecasting Uganda’s net electricity consumption using a hybrid pso-abc algorithm. Arabian Journal for Science and Engineering 44 (2019) 3021-31.
• [6] S.H.A. Kaboli, J. Selvaraj, N.A. Rahim, Long-term electric energy consumption forecasting via artificial cooperative search algorithm, Energy 115 (2016) 857–71.
• [7] A. Askarzadeh, Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: a case study of Iran, Energy 72 (2014) 484–91.
• [8] N. An, W. Zhao, J. Wang, D. Shang, E. Zhao, Using multi-output feedforward neural network with empirical model decomposition based signal filtering for electricity demand forecasting, Energy 49 (2013) 279–88.
• [9] H.T. Pao, Comparing linear and nonlinear forecasts for Taiwan’s electricity consumption, Energy 31 (2006) 2129–41.
• [10] L. Wang, H. Hu, X.Y. Ai, H. Liu, Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm, Energy 153 (2018) 801–15.
• [11] R.E. Gonzalez, M.M. Jaramillo, F.D. Carmona, Monthly electric energy demand forecasting based on trend extraction, IEEE Transactıons on Power Systems 21 (2006) 1946–53.
• [12] F.J. Ardakani, M.M. Ardehali, Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types, Energy 65(2014) 452–61.
• [13] C. Hamzacebi, F. Kutay, Electric consumption forecasting of Turkey usıng artıfıcıal neural networks up to year 2010, Journal of the Faculty of Engineering and Architecture of Gazi University 19 (2004) 227–33.
• [14] C. Hamzacebi, Forecasting of Turkey’s net electricity energy consumption on sectoral bases, Energy Policy 35 (2007) 2009–16.
• [15] K. Kavaklioglu, H. Ceylan, H.K. Ozturk, O.E. Canyurt, Modeling and prediction of Turkey’s electricity consumption using artificial neural networks, Energy Conversion Management 50 (2009) 2719–27.
• [16] M. Bilgili, Estimation of net electricity consumption of Turkey, Journal of Thermal Science and Technology 29 (2009) 89–98.
• [17] G. Oğcu, O.F. Demirel, S. Zaim, Forecasting electricity consumption with neural networks and support vector regression, Procedia-Social and Behavioral Sciences 58 (2012) 1576–85.
• [18] F. Gürbüz, C. Öztürk, P. Pardalos, Prediction of electricity energy consumption of Turkey via artificial bee colony: a case study, Energy Systems 4 (2013) 289–300.
• [19] F. Kaytez, M.C. Taplamacioglu, E. Cam, F. Hardalac, Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines, Electrical Power and Energy Systems 67 (2015) 431–8.
• [20] Ş. Birim, A. Tümtürk, Modeling and forecasting Turkey’s electricity consumption by using artificial neural network, American Scientific Research Journal for Engineering, Technology and Sciences 25 (2016) 192–208.
• [21] M. E. Günay, Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: case of Turkey, Energy Policy 90 (2016) 92–101.
• [22] 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) 737–47.
• [23] H.K. Ozturk, H. Ceylan, O.E. Canyurt, A. Hepbasli, Electricity estimation using genetic algorithm approach: a case study of Turkey, Energy 30 (2005) 1003–12.
• [24] Z. Yumurtaci, E. Asmaz, Electric energy demand of Turkey for the year 2050, Energy Sources 36 (2004) 1157–64.
• [25] M. Tunc, U. Camdali, C. Parmaksizoglu, Comparison of Turkey’s electrical energy consumption and production with some european countries and optimization of future electrical power supply investments in Turkey, Energy Policy 34 (2006) 50–9.
• [26] D. Akay, M. Atak. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey, Energy 32 (2007) 1670–5.
• [27] C. Hamzacebi, H.A. Es, Forecasting the annual electricity consumption of Turkey using an optimized grey model, Energy 70 (2014) 165–71.
• [28] E. Erdogdu, Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey, Energy Policy 35 (2007) 1129–46.
• [29] K.K. Sumer, O. Goktas, A. Hepsag, The application of seasonal latent variable in forecasting electricity demand as an alternative method, Energy Policy 37 (2009) 1317–22.
• [30] M.D. Toksarı, Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey, Energy Policy 37 (2009) 1181–7.
• [31] S. Kucukali, K. Baris, Turkeys short-term gross annual electricity demand forecast by fuzzy logic approach, Energy Policy 38 (2010) 2438–45.
• [32] O. Demirel, A. Kakilli, M. Tektas, Electrıc energy load forecastıng usıng anfis and arma methods, Journal of the Faculty of Engineering and Architecture of Gazi University 25 (2010) 601–10.
• [33] K. Kavaklioglu, Modeling and prediction of Turkey’s electricity consumption using support vector regression, Applied Energy 88 (2011) 368–75.
• [34] Z. Dilaver, L.C. Hunt, Turkish aggregate electricity demand: an outlook to 2020, Energy 36 (2011) 6686–96.
• [35] M.S. Kıran, E. Özceylan, M. Gündüz, T. Paksoy, Swarm intelligence approaches to estimate electricity energy demand in Turkey, Knowledge-Based System 36 (2012) 93–103.
• [36] R.V. Rao, D.P. Rai, J. Ramkumar, J. Balic, A new multi-objective Jaya algorithm for optimization of modern machining processes, Advances in Production Engineering & Management 11 (2016) 271–86.
• [37] M. Bhoye, M.H. Pandya, S. Valvi, I.N. Trivedi, P. Jangir, S.A. Parmar, An emission constraint economic load dispatch problem solution with microgrid using JAYA algorithm, In: 2016 International conference on energy efficient technologies for sustainability (ICEETS) Nagercoil, (2016) 497–502.
• [38] R.V. Rao, K.C. More, Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm, Energy Conversion and Management 140 (2017) 24–35.
• [39] T. Dede, Jaya algorithm to solve single objective size optimization problem for steel grillage structures, Steel And Composıte Structures 26 (2018) 163–70.
• [40] R.V. Rao, Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems, International Journal of Industrial Engineering Computations 7 (2016) 19–34.
• [41] R.V. Rao, K.C. More, J. Taler, P. Oclon, Dimensional optimization of a micro-channel heat sink using Jaya algorithm, Applied Thermal Engineering 103 (2016) 572–82.
• [42] D.C. Du, H.H. Vinh, V.D. Trung, N.T.H. Quyen, N.T. Trung, Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function, Engineering Optimization 50 (2018) 1233–51.
• [43] 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) 638–47.
• [44] 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.
• [45] M. Çunkaş, A. A. Altun, Long term electricity demand forecasting in Turkey using artificial neural networks, Energy Sources, Part B: Economics, Planning and Policy 5 (2010) 279–89.
• [46] M. Kankal, A. Akpinar, M.İ. Kömürcü, T.Ş. Özşahin, Modeling and forecasting of Turkey’s energy consumption using socio–economic and demographic variables, Applied Energy 88 (2011) 1927–39.
• [47] V. Gümüş, A. Başak, K. Yengün, Yapay sinir ağları ile Şanlıurfa istasyonunun kuraklığının tahmini, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji 6 (2018) 621–633.
• [48] D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University Engineering Faculty Computer Engineering Department 2005.
• [49] E. Uzlu, M.İ. Kömürcü, M. Kankal, T. Dede, H.T. Öztürk, Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms, Applied Ocean Research 48 (2014) 103–13.
• [50] C. Ozkan, O. Kisi, B. Akay, Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration, Irrigation Science 29 (2011) 431–41.
• [51] R.V. Rao, V.J. Savsani, D.P. Vakharia, Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems, Computer-Aided Design 43 (2011) 303–15.
• [52] 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 31 (2019) 2939-54.
• [53] T. Dede, Y. Ayvaz, Combined size and shape optimization of structures with a new meta-heuristic algorithm, Applied Soft Computing 28 (2015) 250–8.
• [54] T. Dede, Optimum design of grillage structures to LRFD–AISC with teaching–learning based optimization, Structural and Multidisciplinary Optimization 48 (2013) 955–64.
• [55] R.V. Rao, A. Saroj, Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm, Energy 128 (2017) 785–800.
• [56] R.V. Rao, Jaya: A Simple and New Optimization Algorithm For Solving Constrained and Unconstrained Optimization Problems, International Journal of Industrial Engineering Computations, 7 (2016) 19–34.
• [57] E. Uzlu, Kıyıya dik katı madde hareketi sonucu oluşan yığılma profilinin fiziksel modelle incelenmesi, Doktora Tezi, Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü Trabzon 2016.
• [58] R.V. Rao, G. Waghmare, A new optimization algorithm for solving complex constrained design optimization problems, Engineering Optimization 49 (2017) 60–83.
• [59] E. Uzlu, Türkiye için gri kurt optimizasyon algoritması ile yapay sinir ağlarını kullanarak enerji tüketiminin tahmini, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji 7 (2018) 245–262.
• [60] 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.
• [61] Türkiye İstatistik Kurumu (TÜİK). Nüfus ve demografi, nüfus istatistikleri. http://www.tuik.gov.tr/UstMenu.do?metod=temelist. Erişim Tarihi Ağustos, 17, 2018.
• [62] Türkiye İstatistik Kurumu (TÜİK). Dış ticaret, yıllara göre dış ticaret. http://www.tuik.gov.tr/UstMenu.do?metod=temelist. Erişim Tarihi Temmuz, 17, 2018.
• [63] Türkiye Cumhuriyeti Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı, Gösterge ve istatistikler, göstergeler, ekonomik ve sosyal göstergeler, 1. Bölüm Milli gelir ve üretim. http://www.sbb.gov.tr/ekonomik-ve-sosyal-gostergeler/ Erişim Tarihi Ağustos, 20, 2019.
• [64] Türkiye İstatistik Kurumu (TÜİK). Temel istatistikler, nüfus ve demografi, nüfus projeksiyonları. http://www.tuik.gov.tr/UstMenu.do?metod=temelist Erişim Tarihi Ağustos, 20, 2019.
• [65] Türkiye Elektrik İletim A.Ş. (TEİAŞ). 10 Yıllık talep tahminleri raporu. <https://www.teias.gov.tr/sites/default/files/2018-02/Taleprapor_2017.pdf Erişim Tarihi Ağustos, 20, 2019.