DENİZ YIRTICILARI ALGORİTMASI İLE TÜRKİYE’NİN ENERJİ TALEBİNİN TAHMİN EDİLMESİNE YÖNELİK KAPSAMLI BİR ÇALIŞMA

Yıl 2024, Cilt: 27 Sayı: 2, 615 – 630, 03.06.2024

Ahmet Özkış

https://doi.org/10.17780/ksujes.1413432

Öz

Enerjiye olan talep her geçen gün artmakta ve bu talebin önceden tahmin edilebilmesi büyük önem arz etmektedir. Bu çalışma, yakın zamanda önerilen deniz avcıları algoritması (MPA) ile Türkiye’nin 1979 – 2015 yılları arasındaki enerji talebini tahmin etmek amacıyla yapılmıştır. Çalışmada kullanılan doğrusal ve ikinci dereceden regresyon modellerinin ağırlıklarının belirlenmesinde MPA’dan yararlanılmıştır. Yapılan incelemelere göre MPA literatürde ilk kez bu amaçla kullanılmaktadır. MPA’nın toplam karesel hata ve toplam bağıl yüzde hatası metrikleri için elde ettiği sonuçlar, literatürde iyi bilinen diferansiyel evrim, Arşimet optimizasyon, güve alev optimizasyonu ve gri kurt algoritmaları ile kıyaslanmıştır. Literatürdeki diğer çalışmalardan farklı olarak performans karşılaştırmaları sadece en iyi değer üzerinden değil; en iyi, en kötü, ortalama ve standart sapma değerlerine göre yapılmıştır. Elde edilen sonuçlar MPA’nın enerji talep tahmin probleminde karşılaştırılan algoritmalardan daha başarılı ve kararlı bir yapıya sahip olduğunu göstermiştir.

Anahtar Kelimeler

deniz avcıları algoritması, enerji talep tahmini, regresyon analizi, metasezgisel algoritmalar.

Etik Beyan

Herhangi bir kurum ya da kişiyle çıkar çatışması bulunmamaktadır.

Destekleyen Kurum

Destek alınan bir kurum bulunmamaktadır.

Teşekkür

Emekleriniz için şimdiden teşekkürler.

Kaynakça

  • Abd Elminaam, D. S., Nabil, A., Ibraheem, S. A., & Houssein, E. H. (2021). An efficient marine predators algorithm for feature selection. IEEE Access, 9, 60136-60153. https://doi.org/10.1109/ACCESS.2021.3073261
  • Abdel-Basset, M., El-Shahat, D., Chakrabortty, R. K., & Ryan, M. (2021). Parameter estimation of photovoltaic models using an improved marine predators algorithm. Energy Conversion Management, 227, 113491. https://doi.org/10.1016/j.enconman.2020.113491
  • Abdel-Basset, M., Mohamed, R., & Abouhawwash, M. (2022). Hybrid marine predators algorithm for image segmentation: Analysis and validations. Artificial Intelligence Review, 1-53. https://doi.org/10.1007/s10462-021-10086-0
  • Anwar, J. (2016). Analysis of energy security, environmental emission and fuel import costs under energy import reduction targets: A case of Pakistan. Renewable Sustainable Energy Reviews, 65, 1065-1078. https://doi.org/10.1016/j.rser.2016.07.037
  • Arslan, S. (2023). Güncel Metasezgisel Algoritmalarının Performansları Üzerine Karşılaştırılmalı Bir Çalışma. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(4), 1861-1884. https://doi.org/10.29130/dubited.1150453
  • Aslan, M. (2023). Archimedes optimization algorithm based approaches for solving energy demand estimation problem: a case study of Turkey. Neural Computing and Applications, 35(26), 19627-19649. 10.1007/s00521-023-08769-6
  • Aslan, M., & Beşkirli, M. (2022). Realization of Turkey’s energy demand forecast with the improved arithmetic optimization algorithm. Energy Reports, 8, 18-32. https://doi.org/10.1016/j.egyr.2022.06.101
  • Baştemur Kaya, C. (2023). On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems. Symmetry, 15(8), 1610. https://doi.org/10.3390/sym15081610
  • Baum, V. (1984). Energy planning in developing countries. USA: Oxford University Press.
  • Bilici, Z., & Özdemir, D. (2023). Comparative analysis of metaheuristic optimization algorithms for natural gas demand forecast with meteorological parameters. Journal of the Faculty of Engineering Architecture of Gazi University, 38(2), 1153-1167. https://doi.org/10.1016/j.jer.2023.100127
  • Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion Management, 45(15-16), 2525-2537. https://doi.org/10.1016/j.enconman.2003.11.010
  • Cihan, P. (2022). Impact of the COVID-19 lockdowns on electricity and natural gas consumption in the different industrial zones and forecasting consumption amounts: Turkey case study. International Journal of Electrical Power & Energy Systems, 134, 107369. https://doi.org/10.1016/j.ijepes.2021.107369
  • de Oliveira, E. M., & Oliveira, F. L. C. (2018). Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144, 776-788. https://doi.org/10.1016/j.energy.2017.12.049
  • Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
  • Ediger, V. Ş., & Tatlıdil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473-487. https://doi.org/10.1016/S0196-8904(01)00033-4
  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517. https://doi.org/10.1016/j.energy.2009.10.018
  • Es, H., Kalender Öksüz, F., & Hamzacebi, C. (2014). Forecasting the net energy demand of Turkey by artificial neural networks. Journal of the Faculty of Engineering Architecture of Gazi University, 29(3).
  • Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377. https://doi.org/10.1016/j.eswa.2020.113377
  • Ghalehkhondabi, I., Ardjmand, E., Weckman, G. R., & Young, W. A. (2017). An overview of energy demand forecasting methods published in 2005–2015. Energy Systems, 8, 411-447. http://dx.doi.org/10.1007%2Fs12667-016-0203-y
  • Gorucu, F. B. (2004). Artificial Neural Network Modeling for Forecasting Gas Consumption. Energy Sources, 26(3), 299-307. 10.1080/00908310490256626
  • Gulcu, S., & Kodaz, H. (2017). The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey. Procedia computer science, 111, 64-70. https://doi.org/10.1016/j.procs.2017.06.011
  • Haldenbilen, S., & Ceylan, H. (2005). Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy, 33(1), 89-98. https://doi.org/10.1016/S0301-4215(03)00202-7
  • Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51, 1531-1551. https://doi.org/10.1007/s10489-020-01893-z
  • Ikram, R. M. A., Ewees, A. A., Parmar, K. S., Yaseen, Z. M., Shahid, S., & Kisi, O. (2022). The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction. Applied Soft Computing, 131, 109739. https://doi.org/10.1016/j.asoc.2022.109739
  • International Energy Outlook – U.S. Energy Information Administration (EIA). (2023). https://www.eia.gov/outlooks/ieo/data.php Erişim: 13.11.2023
  • İsmail, K., Nureddin, R., & Kahramanlı, H. (2018). Türkiye'de enerji talebini tahmin etmek için doğrusal form kullanarak gsa (yerçekimi arama algoritması) ve iwo (yabani ot optimizasyon algoritması) tekniklerinin uygulanması. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 6(4), 529-543. https://doi.org/10.15317/Scitech.2018.150
  • Jangir, P., Buch, H., Mirjalili, S., & Manoharan, P. (2023). MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems. Evolutionary Intelligence, 16(1), 169-195. https://doi.org/10.1007/s12065-021-00649-z
  • Kankal, M., & Uzlu, E. (2017). Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Computing Applications, 28, 737-747. https://doi.org/10.1007/s00521-016-2409-2
  • Karakoyun, M., & Özkış, A. (2021). Transfer Fonksiyonları Kullanarak İkili Güve-Alev Optimizasyonu Algoritmalarının Geliştirilmesi ve Performanslarının Karşılaştırılması. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 3(2), 1-10. https://doi.org/10.47112/neufmbd.2021.7
  • Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012a). 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. https://doi.org/10.1016/j.enconman.2011.08.004
  • Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012b). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103. https://doi.org/10.1016/j.knosys.2012.06.009
  • Koc, I., Kivrak, H., & Babaoglu, I. (2019). The estimation of the energy demand in turkey using grey wolf optimizer algorithm. Annals of the Faculty of Engineering Hunedoara, 17(1), 113-117.
  • Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Özdemir, D., Dörterler, S., & Aydın, D. (2022). A new modified artificial bee colony algorithm for energy demand forecasting problem. Neural Computing and Applications, 34(20), 17455-17471. https://doi.org/10.1007/s00521-022-07675-7
  • Özkan, E. (2018). Parçacık Sürü Optimizasyonu ve Genetik Algoritma Kullanarak Türkiye’nin 2050 Yılına Kadar Enerji Tüketim Tahmininin Yapılması. Yüksek Lisans Tezi. Korkut Ata Üniversitesi Sosyal Bilimler Enstitüsü Yönetim Bilişim Sistemleri Ana Bilim Dalı, Osmaniye 108s.
  • Özkış, A. (2020). A new model based on vortex search algorithm for estimating energy demand of Turkey. Pamukkale University Journal of Engineering Sciences, 26(5), 959-965. https://dx.doi.org/10.5505/pajes.2020.74943
  • Ozturk, H. K., Ceylan, H., Canyurt, O. E., & Hepbasli, A. (2005). Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy, 30(7), 1003-1012. https://doi.org/10.1016/j.energy.2004.08.008
  • Ozturk, S., & Ozturk, F. (2018). Forecasting energy consumption of Turkey by Arima model. Journal of Asian Scientific Research, 8(2), 52.
  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11, 341-359. https://doi.org/10.1023/A:1008202821328
  • Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable Sustainable Energy Reviews, 16(2), 1223-1240. https://doi.org/10.1016/j.rser.2011.08.014
  • TC Enerji ve Tabii Kaynaklar Bakanlığı. Enerji üretim kaynakları ve gelecekteki talep tahminleri.(2023). https://enerji.gov.tr/bilgi-merkezi-enerji-elektrik#:~:text=2022%20y%C4%B1l%C4%B1nda%20elektrik%20%C3%BCretimimizin%2C%20%34,%C3%BC%20di%C4%9Fer%20kaynaklardan%20elde%20edilmi%C5%9Ftir. Erişim: 21.12.2023
  • Toksarı, M. D. (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984-3990. https://doi.org/10.1016/j.enpol.2007.01.028
  • 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. https://doi.org/10.1016/j.enpol.2008.11.017
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  • Zhong, K., Zhou, G., Deng, W., Zhou, Y., & Luo, Q. (2021). MOMPA: Multi-objective marine predator algorithm. Computer Methods in Applied Mechanics Engineering, 385, 114029. https://doi.org/10.1007/s12065-021-00649-z

A COMPREHENSIVE STUDY ON FORECASTING TURKEY'S ENERGY DEMAND WITH THE MARINE PREDATORS ALGORITHM

Yıl 2024, Cilt: 27 Sayı: 2, 615 – 630, 03.06.2024

Ahmet Özkış

https://doi.org/10.17780/ksujes.1413432

Öz

The energy demand is increasing day by day, and it is of great importance to predict this demand. This study was conducted to estimate Turkey’s energy demand between 1979 – 2015 with the recently proposed marine predators algorithm (MPA). To determine the weights of the linear and quadratic regression models used in the study is utilized from the MPA. According to the studies, MPA is used for this purpose for the first time in the literature. The results obtained by the MPA for sum-squared-error and total-relative-percentage-error metrics were compared with algorithms well-known in the literature differential evolution, Archimedes optimization, moth flame optimization, and grey wolf optimizer. Unlike other studies in the literature, performance comparisons are not only based on the best value; it was made according to the best, worst, average, and standard deviation values. The results showed that MPA has a more successful and stable structure than the compared algorithms in the energy demand forecasting problem.

Anahtar Kelimeler

Marine predator algorithm, energy demand estimation, regression analysis, metaheuristic algorithms

Etik Beyan

There is no conflict of interest with any institution or person.

Destekleyen Kurum

There is no institution that receives support.

Teşekkür

Thanks in advance for your efforts.

Kaynakça

  • Abd Elminaam, D. S., Nabil, A., Ibraheem, S. A., & Houssein, E. H. (2021). An efficient marine predators algorithm for feature selection. IEEE Access, 9, 60136-60153. https://doi.org/10.1109/ACCESS.2021.3073261
  • Abdel-Basset, M., El-Shahat, D., Chakrabortty, R. K., & Ryan, M. (2021). Parameter estimation of photovoltaic models using an improved marine predators algorithm. Energy Conversion Management, 227, 113491. https://doi.org/10.1016/j.enconman.2020.113491
  • Abdel-Basset, M., Mohamed, R., & Abouhawwash, M. (2022). Hybrid marine predators algorithm for image segmentation: Analysis and validations. Artificial Intelligence Review, 1-53. https://doi.org/10.1007/s10462-021-10086-0
  • Anwar, J. (2016). Analysis of energy security, environmental emission and fuel import costs under energy import reduction targets: A case of Pakistan. Renewable Sustainable Energy Reviews, 65, 1065-1078. https://doi.org/10.1016/j.rser.2016.07.037
  • Arslan, S. (2023). Güncel Metasezgisel Algoritmalarının Performansları Üzerine Karşılaştırılmalı Bir Çalışma. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(4), 1861-1884. https://doi.org/10.29130/dubited.1150453
  • Aslan, M. (2023). Archimedes optimization algorithm based approaches for solving energy demand estimation problem: a case study of Turkey. Neural Computing and Applications, 35(26), 19627-19649. 10.1007/s00521-023-08769-6
  • Aslan, M., & Beşkirli, M. (2022). Realization of Turkey’s energy demand forecast with the improved arithmetic optimization algorithm. Energy Reports, 8, 18-32. https://doi.org/10.1016/j.egyr.2022.06.101
  • Baştemur Kaya, C. (2023). On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems. Symmetry, 15(8), 1610. https://doi.org/10.3390/sym15081610
  • Baum, V. (1984). Energy planning in developing countries. USA: Oxford University Press.
  • Bilici, Z., & Özdemir, D. (2023). Comparative analysis of metaheuristic optimization algorithms for natural gas demand forecast with meteorological parameters. Journal of the Faculty of Engineering Architecture of Gazi University, 38(2), 1153-1167. https://doi.org/10.1016/j.jer.2023.100127
  • Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion Management, 45(15-16), 2525-2537. https://doi.org/10.1016/j.enconman.2003.11.010
  • Cihan, P. (2022). Impact of the COVID-19 lockdowns on electricity and natural gas consumption in the different industrial zones and forecasting consumption amounts: Turkey case study. International Journal of Electrical Power & Energy Systems, 134, 107369. https://doi.org/10.1016/j.ijepes.2021.107369
  • de Oliveira, E. M., & Oliveira, F. L. C. (2018). Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144, 776-788. https://doi.org/10.1016/j.energy.2017.12.049
  • Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
  • Ediger, V. Ş., & Tatlıdil, H. (2002). Forecasting the primary energy demand in Turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473-487. https://doi.org/10.1016/S0196-8904(01)00033-4
  • Ekonomou, L. (2010). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35(2), 512-517. https://doi.org/10.1016/j.energy.2009.10.018
  • Es, H., Kalender Öksüz, F., & Hamzacebi, C. (2014). Forecasting the net energy demand of Turkey by artificial neural networks. Journal of the Faculty of Engineering Architecture of Gazi University, 29(3).
  • Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377. https://doi.org/10.1016/j.eswa.2020.113377
  • Ghalehkhondabi, I., Ardjmand, E., Weckman, G. R., & Young, W. A. (2017). An overview of energy demand forecasting methods published in 2005–2015. Energy Systems, 8, 411-447. http://dx.doi.org/10.1007%2Fs12667-016-0203-y
  • Gorucu, F. B. (2004). Artificial Neural Network Modeling for Forecasting Gas Consumption. Energy Sources, 26(3), 299-307. 10.1080/00908310490256626
  • Gulcu, S., & Kodaz, H. (2017). The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey. Procedia computer science, 111, 64-70. https://doi.org/10.1016/j.procs.2017.06.011
  • Haldenbilen, S., & Ceylan, H. (2005). Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy, 33(1), 89-98. https://doi.org/10.1016/S0301-4215(03)00202-7
  • Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51, 1531-1551. https://doi.org/10.1007/s10489-020-01893-z
  • Ikram, R. M. A., Ewees, A. A., Parmar, K. S., Yaseen, Z. M., Shahid, S., & Kisi, O. (2022). The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction. Applied Soft Computing, 131, 109739. https://doi.org/10.1016/j.asoc.2022.109739
  • International Energy Outlook – U.S. Energy Information Administration (EIA). (2023). https://www.eia.gov/outlooks/ieo/data.php Erişim: 13.11.2023
  • İsmail, K., Nureddin, R., & Kahramanlı, H. (2018). Türkiye'de enerji talebini tahmin etmek için doğrusal form kullanarak gsa (yerçekimi arama algoritması) ve iwo (yabani ot optimizasyon algoritması) tekniklerinin uygulanması. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 6(4), 529-543. https://doi.org/10.15317/Scitech.2018.150
  • Jangir, P., Buch, H., Mirjalili, S., & Manoharan, P. (2023). MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems. Evolutionary Intelligence, 16(1), 169-195. https://doi.org/10.1007/s12065-021-00649-z
  • Kankal, M., & Uzlu, E. (2017). Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Computing Applications, 28, 737-747. https://doi.org/10.1007/s00521-016-2409-2
  • Karakoyun, M., & Özkış, A. (2021). Transfer Fonksiyonları Kullanarak İkili Güve-Alev Optimizasyonu Algoritmalarının Geliştirilmesi ve Performanslarının Karşılaştırılması. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 3(2), 1-10. https://doi.org/10.47112/neufmbd.2021.7
  • Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012a). 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. https://doi.org/10.1016/j.enconman.2011.08.004
  • Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012b). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103. https://doi.org/10.1016/j.knosys.2012.06.009
  • Koc, I., Kivrak, H., & Babaoglu, I. (2019). The estimation of the energy demand in turkey using grey wolf optimizer algorithm. Annals of the Faculty of Engineering Hunedoara, 17(1), 113-117.
  • Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
  • Özdemir, D., Dörterler, S., & Aydın, D. (2022). A new modified artificial bee colony algorithm for energy demand forecasting problem. Neural Computing and Applications, 34(20), 17455-17471. https://doi.org/10.1007/s00521-022-07675-7
  • Özkan, E. (2018). Parçacık Sürü Optimizasyonu ve Genetik Algoritma Kullanarak Türkiye’nin 2050 Yılına Kadar Enerji Tüketim Tahmininin Yapılması. Yüksek Lisans Tezi. Korkut Ata Üniversitesi Sosyal Bilimler Enstitüsü Yönetim Bilişim Sistemleri Ana Bilim Dalı, Osmaniye 108s.
  • Özkış, A. (2020). A new model based on vortex search algorithm for estimating energy demand of Turkey. Pamukkale University Journal of Engineering Sciences, 26(5), 959-965. https://dx.doi.org/10.5505/pajes.2020.74943
  • Ozturk, H. K., Ceylan, H., Canyurt, O. E., & Hepbasli, A. (2005). Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy, 30(7), 1003-1012. https://doi.org/10.1016/j.energy.2004.08.008
  • Ozturk, S., & Ozturk, F. (2018). Forecasting energy consumption of Turkey by Arima model. Journal of Asian Scientific Research, 8(2), 52.
  • Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11, 341-359. https://doi.org/10.1023/A:1008202821328
  • Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable Sustainable Energy Reviews, 16(2), 1223-1240. https://doi.org/10.1016/j.rser.2011.08.014
  • TC Enerji ve Tabii Kaynaklar Bakanlığı. Enerji üretim kaynakları ve gelecekteki talep tahminleri.(2023). https://enerji.gov.tr/bilgi-merkezi-enerji-elektrik#:~:text=2022%20y%C4%B1l%C4%B1nda%20elektrik%20%C3%BCretimimizin%2C%20%34,%C3%BC%20di%C4%9Fer%20kaynaklardan%20elde%20edilmi%C5%9Ftir. Erişim: 21.12.2023
  • Toksarı, M. D. (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984-3990. https://doi.org/10.1016/j.enpol.2007.01.028
  • 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. https://doi.org/10.1016/j.enpol.2008.11.017
  • Top, S., & Vapur, H. (2018). Evolution of energy strategies in Turkey: Forecasts by time series. Journal of Energy Research Reviews, 1(4), 1-16.
  • 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. https://doi.org/10.1016/j.enpol.2008.02.018
  • Zergane, S., Smaili, A., & Masson, C. (2018). Optimization of wind turbine placement in a wind farm using a new pseudo-random number generation method. Renewable Energy, 125, 166-171. https://doi.org/10.1016/j.renene.2018.02.082
  • Zhong, K., Zhou, G., Deng, W., Zhou, Y., & Luo, Q. (2021). MOMPA: Multi-objective marine predator algorithm. Computer Methods in Applied Mechanics Engineering, 385, 114029. https://doi.org/10.1007/s12065-021-00649-z

Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Takviyeli Öğrenme, Evrimsel Hesaplama, Modelleme ve Simülasyon
BölümBilgisayar Mühendisliği
Yazarlar

Ahmet Özkış NECMETTİN ERBAKAN ÜNİVERSİTESİ 0000-0002-1899-5494 Türkiye

Yayımlanma Tarihi3 Haziran 2024
Gönderilme Tarihi3 Ocak 2024
Kabul Tarihi14 Şubat 2024
Yayımlandığı Sayı Yıl 2024Cilt: 27 Sayı: 2

Kaynak Göster

APAÖzkış, A. (2024). DENİZ YIRTICILARI ALGORİTMASI İLE TÜRKİYE’NİN ENERJİ TALEBİNİN TAHMİN EDİLMESİNE YÖNELİK KAPSAMLI BİR ÇALIŞMA. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 615-630. https://doi.org/10.17780/ksujes.1413432

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