Araştırma Makalesi

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

Cilt: 27 Sayı: 2 3 Haziran 2024
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A COMPREHENSIVE STUDY ON FORECASTING TURKEY'S ENERGY DEMAND WITH THE MARINE PREDATORS ALGORITHM

Abstract

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.

Keywords

Destekleyen Kurum

Destek alınan bir kurum bulunmamaktadır.

Etik Beyan

Herhangi bir kurum ya da kişiyle çıkar çatışması bulunmamaktadır.

Teşekkür

Emekleriniz için şimdiden teşekkürler.

Kaynakça

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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

Ayrıntılar

Birincil Dil

Türkçe

Konular

Takviyeli Öğrenme , Evrimsel Hesaplama , Modelleme ve Simülasyon

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

3 Haziran 2024

Gönderilme Tarihi

3 Ocak 2024

Kabul Tarihi

14 Şubat 2024

Yayımlandığı Sayı

Yıl 1970 Cilt: 27 Sayı: 2

Kaynak Göster

APA
Özkış, 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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