**Abstract**

Particle swarm optimization (PSO) and genetic algorithm (GA) are the most important optimization techniques among various modern heuristic optimization techniques. The study aims to forecast the energy consumption in Turkey until the year 2050 using PSO and GA models. The annual data provided by the Ministry of Energy and Natural Resources, International Energy Agency (IEA), OECD, Turkish Statistical Institute were used in the study. PSO and GA energy demand forecasting models are developed using population, import, export and gross domestic product (GDP). All models are proposed in linear and quadratic forms. Turkey's energy consumption is projected according to four different scenarios. According the analysis results, the study found for the PSO analysis the R2 values in the linear model was 91.72%, in the quadratic model was 94.06% at the same time for the GA analysis R2 values in the linear model was 91.71%, in the quadratic model was 93.97%. Additionally, the mean absolute percent error rates were 11.58% for PSO and 11.69% for GA in the quadratic model. According to Lewis, these values showed that models could be used for energy consumption estimation purposes. The study determined that the statistical performance criteria of PSO models were more successful than the statistical performance criteria of GA models.

**Keywords:** Energy Consumption, Forecasting, Genetic Algorithm, Particle Swarm Optimization, Turkey

**Suggested citation**

Modeling of Energy Consumption Forecast with Economic Indicators Using Particle Swarm Optimization and Genetic Algorithm: An Application in Turkey between 1979 and 2050. Alphanumeric Journal, 8(1), 59-78. https://doi.org/10.17093/alphanumeric.747427

().**References**

- Alireza, A. L. F. I. (2011). PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Automatica Sinica, 37(5), 541-549.
- Assareh, E., Behrang, M. A., & Ghanbarzdeh, A. (2012). Forecasting energy demand in Iran using genetic algorithm (GA) and particle swarm optimization (PSO) methods. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 411-422.
- Avami, A., & Boroushaki, M. (2011). Energy consumption forecasting of Iran using recurrent neural networks. Energy Sources, Part B: Economics, Planning, and Policy, 6(4), 339-347.
- Baluja, S. (1994), Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning, Tech. Rep. No. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, PA.
- Barak, S., & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems, 82, 92-104.
- Behrang, M. A., Assareh, E., Assari, M. R., & Ghanbarzadeh, A. (2011). Total energy demand estimation in Iran using bees algorithm. Energy Sources, Part B: Economics, Planning, and Policy, 6(3), 294-303.
- Bodenhofer, U. (2003). Genetic algorithms: theory and applications.In: Lecture notes, Fuzzy Logic Laboratorium Linz-Hagenberg, Winter.
- Boğar, E. & Boğar, Z. Ö. (2017). Türkiye Net Elektrik Enerjisi Tüketiminin Parçacık Sürü Optimizasyonu Tabanlı Modellenmesi. Akademia Mühendislik ve Fen Bilimleri Dergisi, 1(3), 40-47.
- Cao, Z., Yuan, P., & Ma, Y. B. (2014). Energy Demand Forecasting Based on Economy-related Factors in China. Energy Sources, Part B: Economics, Planning, and Policy, 9(2), 214-219.
- Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 45(15), 2525-2537.
- Ceylan, H., Ozturk, H. K., Hepbasli, A., & Utlu, Z. (2005). Estimating energy and exergy production and consumption values using three different genetic algorithm approaches. Energy Sources, Part 2: Application and scenarios. 27(7), 629-639.
- Clerc, M. (2010). Particle swarm optimization (Vol. 93). New Jersey: John Wiley & Sons.
- Çolak, S. (2010). Genetik algoritmalar yardımı ile gezgin satıcı probleminin çözümü üzerine bir uygulama. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 19(3), 423-438.
- Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers. Singapore: World Scientific Publishing Company.
- Couceiro, M., & Ghamisi, P. (2016). Particle swarm optimization. In Fractional order darwinian particle swarm optimization (pp.11-20). Springer International Publishing.
- Deb, K. (1999). Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary computation, 7(3), 205-230.
- Değertekin, S. Ö., Ülker, M., & Hayalioğlu, M. S. (2006). Uzay çelik çerçevelerin tabu arama ve genetik algoritma yöntemleriyle optimum tasarımı. İMO Teknik Dergi, 259, 3917-3934.
- Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence. USA: Morgan Kaufmann Publishers.
- Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708.
- Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2):512-517.
- Emel, G. G., & Taşkın, Ç. (2002). Genetik Algoritmalar ve Uygulama Alanlari. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 129-152.
- Feng, S. J., Ma, Y. D., Song, Z. L., & Ying, J. (2012). Forecasting the energy consumption of China by the grey prediction model. Energy Sources, Part B: Economics, Planning, and Policy, 7(4), 376-389.
- Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, 37(10), 4049-4054.
- Gen, M., ve Cheng, R. ( 2000). Genetic algorithm and engineering optimization. New York: John Wily and Sons.
- Gerşil, M., & Palamutçuoğlu, T. (2013). Ders çizelgeleme probleminin melez genetik algoritmalar ile performans analizi. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(1), 242-262.
- Goldberg, D. E. (1989). Genetic algorithms in search, optimisation and machine learning, New York: Addison, Wesley.
- Haldenbilen, S., & Ceylan, H. (2005). Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy, 33(1), 89-98.
- Huang, Y., Bor, Y. J., & Peng, C. Y. (2011). The long-term forecast of Taiwan’s energy supply and demand: LEAP model application. Energy policy, 39(11), 6790-6803.
- Kankal, M., Akpınar, A., Kömürcü, M. İ., & Özşahin, T. Ş. (2011). Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Applied Energy, 88(5), 1927-1939.
- Kaynar, O., Özekicioğlu, H., & Demirkoparan, F. (2017). Forecasting of Turkey's Electricity Consumption with Support Vector Regression and Chaotic Particle Swarm Algorithm. Journal of Administrative Sciences/Yonetim Bilimleri Dergisi, 15(29).211-224.
- Kaynar, O., Yüksek, A. G., & Demirkoparan, F. (2016). Genetık Algorıtma Ile Egıtılmıs Destek Vektör Regresyon Kullanılarak Türkıye'nın Elektrık Tüketım Tahmını/Forecastıng Of Turkey's Electrıcıty Consumptıon Usıng Support Vector Regressıon Traıned Wıth Genetıc Algorıthm. Istanbul Üniversitesi Iktisat Fakültesi Mecmuasi, 66(2), 45-60.
- Kaytez, F., Taplamacioglu, M. C., Cam, E., & Hardalac, F. (2015). Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems, 67, 431-438.
- Kıran, M. S., & Gündüz, M. (2012). A novel artificial bee colony-based algorithm for solving the numerical optimization problems. International Journal of Innovative Computing, Information and Control, 8(9), 6107-6121.
- Kiran, M. S., Hakli, H., Gunduz, M., & Uguz, H. (2015). Artificial bee colony algorithm with variable search strategy for continuous optimization. Information Sciences, 300, 140-157.
- Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy conversion and management, 53(1), 75-83.
- Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103.
- Kiranyaz, S., Ince, T., & Gabbouj, M. (2014). Multidimensional particle swarm optimization for machine learning and pattern recognition. NewYork: Springer.
- Kisi O. 2014. Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy. 64, 429–436.
- Kumar, U., & Jain, V. K. (2010). Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy, 35(4), 1709-1716.
- Lazinica, A. (2009). Particle Swarm Optimization. Rijeka, Croatia: InTech.
- Lee, Y. S., & Tong, L. I. (2011). Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52(1), 147-152.
- Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann.
- Mitchell, M. (1998). An introduction to genetic algorithms. Cambridge: MIT press.
- Mucuk, M., & Uysal, D. (2009). Turkey’s energy demand. Current Research Journal of Social Sciences, 1(3), 123-128.
- Olsson, A. E. (2010). Particle swarm optimization: theory, techniques and applications. New York: Nova Science Publishers, Inc..
- Omran, M. G. H. (2004). Particle swarm optimization methods for patternrecognition and image processing. PhD Thesis, University of Pretoria, Pretoria.
- Özçakar, N., Görener, A., & Arikan, V. (2012). Depolama sistemlerinde siparis toplama islemlerinin genetik algoritmalarla optimizasyonu. Isletme Iktisadi Enstitüsü Yönetim Dergisi, (71), 118-144.
- Özdemir, M. T., & Öztürk, D. (2016). İki Bölgeli Güç Sistemininin Optikten Esinlenen Optimizasyon Algoritması ile Optimal Yük Frekans Kontrolü. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 57-66.
- Özsağlam, M. Y., & Çunkaş, M. (2008). Optimizasyon problemlerinin çözümü için parçaçık sürü optimizasyonu algoritması. Politeknik Dergisi, 11(4), 299-305.
- Özyön, S., Yaşar, C., Temurtaş, H., & Aydın, D. (2012). Yasak İşletim Bölgeli Ekonomik Güç Dağıtım Problemlerine Geliştirilmiş Parçacık Sürü Optimizasyonu Yaklaşımı. Çankaya Üniversitesi Bilim ve Mühendislik Dergisi, 9(2). 89-106.
- Parsopoulos K. E. & Vrahatis, M. N. (2010). Particle Swarm Optimization and Intelligence: Advances and Applications. Hershey, PA, USA: Information Science Reference.
- Rehman, A., & Deyuan, Z. (2018). Pakistan’s energy scenario: a forecast of commercial energy consumption and supply from different sources through 2030. Energy, sustainability and society, 8(1), 26.
- Shamshirband, S., Mohammadi, K., Yee, L., Petković, D., & Mostafaeipour, A. (2015). A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renewable and sustainable energy reviews, 52, 1031-1042.
- Shi, Y., & Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In International conference on evolutionary programming (pp. 591-600). Springer, Berlin, Heidelberg.
- Şişman, B., Arıol, H., & Eleren, A. (2011). Tedarik Zinciri Ağı Tasarımında Parçacık Sürüsü Optimizasyon Yöntemi İle Çapraz Yükleme Yerlerinin Belirlenmesi.
- Song, Q., Li, J., Duan, H., Yu, D., & Wang, Z. (2017). Towards to sustainable energy-efficient city: a case study of Macau. Renewable and Sustainable Energy Reviews, 75, 504-514.
- Sun, J., Lai, C. H., & Wu, X. J. (2011). Particle swarm optimisation: classical and quantum perspectives. Florida: Crc Press.
- Toksarı, M. D. (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984-3990.
- Toksarı, M. D. (2009). Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey. Energy Policy, 37(3), 1181-1187.
- Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 36(6), 1937-1944.
- Wang, L., & Singh, C. (2007). Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm. Electric Power Systems Research, 77(12), 1654-1664.
- Xie, N. M., Yuan, C. Q., & Yang, Y. J. (2015). Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model. International Journal of Electrical Power & Energy Systems, 66, 1-8.
- Yakut, E., & Süzülmüş, S. (2020). Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey. Network: Computation in Neural Systems, 1-36.
- Yiğit, V. (2011). Genetik algoritma ile Türkiye net elektrik enerjisi tüketiminin 2020 yılınakadar tahmini. International Journal of Engineering Research and Development, 3(2), 37-41.
- Yu, S. W., & Zhu, K. J. (2012). A hybrid procedure for energy demand forecasting in China. Energy, 37(1), 396-404.
- Yuan, X. C., Wei, Y. M., Mi, Z., Sun, X., Zhao, W., & Wang, B. (2017). Forecasting China’s regional energy demand by 2030: A Bayesian approach. Resources, Conservation and Recycling, 127, 85-95.

2020.08.01.OR.03

alphanumeric journal

Pages 59-78

Received: April 3, 2020

Accepted: June 29, 2020

Published: June 30, 2020

2020 Yakut, E., Özkan, E.

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Faculty of Transportation and Logistics, Istanbul University

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Bahadır Fatih Yıldırım, Ph.D.

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