Su Dalga Enerjisi Üretimi ve Yapay Zekâ: Asya, Avrupa ve Türkiye’nin Potansiyeli

Selma Kaymaz; Tuğrul Bayraktar; Çağrı Sel

doi:10.53433/yyufbed.1445985

Derleme

Wave Energy Production and Artificial Intelligence: The Potential of Asia, Europe and Türkiye

Yıl 2024, Cilt: 29 Sayı: 2, 798 - 822, 31.08.2024

Selma Kaymaz , Tuğrul Bayraktar , Çağrı Sel

https://doi.org/10.53433/yyufbed.1445985

Öz

In recent years, there has been a growing need to reduce our reliance on non-renewable energy sources for a more sustainable world. The use of renewable energy sources is increasing as we move towards cleaner energy options and away from fossil fuels. Wave energy technology is gaining particular attention for generating energy sustainably. Under optimal conditions, wave energy projects have the potential to contribute significantly to a country's well-being. In addition to traditional methods, artificial intelligence techniques are widely used in wave energy technology due to the high costs and labor-intensive nature of experimental field measurements and the preparation of parameters and inputs for numerical methods. One such technique involves artificial neural networks to solve problems in this field. This study examines existing research on water-based energy production in Asia and Europe, evaluates Türkiye’s wave energy potential based on the available literature, and discusses the application of artificial neural networks in wave energy technology and the methods employed in the literature.

Anahtar Kelimeler

Artificial neural network, Asia, Europe, Tidal stream, Türkiye, Wave energy converter

Kaynakça

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Su Dalga Enerjisi Üretimi ve Yapay Zekâ: Asya, Avrupa ve Türkiye’nin Potansiyeli

Yıl 2024, Cilt: 29 Sayı: 2, 798 - 822, 31.08.2024

Selma Kaymaz , Tuğrul Bayraktar , Çağrı Sel

https://doi.org/10.53433/yyufbed.1445985

Öz

Son yıllarda, sürdürülebilir bir dünya için yenilenemeyen enerji kaynaklarının kullanımının azaltılması gerekliliği giderek daha belirgin hale gelmektedir. Fosil yakıt tüketiminden, daha temiz bir enerjiye geçiş döneminde, yenilenebilir enerji kaynakları hızla gelişme göstermektedir. Bu gelişmeler ışığında su enerjisi teknolojilerine odak artmaktadır. Enerji potansiyeli için gerekli şartlar karşılandığı sürece; su kaynaklı enerji üretim projelerinin uygulanması ülkelerin refahına katkı sağlama potansiyeli taşımaktadır. Yenilenebilir enerji üretiminde rekabete konu olan su kaynaklı enerji üretimi için; literatürde kıtalar arası enerjinin incelendiği, su potansiyelinin ölçüldüğü, santraller için uygun yer seçiminin yapıldığı, dalga – iklim ilişkisinin incelendiği, okyanus enerjisi teknolojileri konularını içeren çalışmalarda geleneksel teknikler yanı sıra yapay zekâ tekniklerine de yer verilmektedir. Deneysel modelleme saha ölçüm tekniklerinin yüksek maliyetli olduğu, sayısal yöntemlerin parametre ve girdi hazırlık sürecinin zahmetli olması sebebiyle çeşitli yapay zekâ yöntemleri, su kaynaklı enerji üretimi teknolojisinde yoğun şekilde kullanılmaktadır. Yapay sinir ağları da bu alanda karşılaşılan problemlerin çözümünde kullanılan tekniklerden birisi olarak yer almaktadır. Bu derlemede, Asya ve Avrupa kıtasında su kaynaklı enerji üretimi hakkında yapılmış mevcut çalışmalardan bahsedilmekte, Türkiye’nin su enerjisi potansiyelini, mevcut literatür incelenerek ortaya konulmaktadır. Ayrıca yapay zekâ tekniklerinden yapay sinir ağı metodunun su enerjisi teknolojilerinde ne şekilde ve hangi ölçüde kullanıldığı ve kullanılan yöntemlerle ilgili literatüre yer verilmiştir.

Anahtar Kelimeler

Asya, Avrupa, Gelgit enerjisi, Su dalga enerjisi, Türkiye, Yapay sinir ağları

Kaynakça

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Webb, A., Waseda, T., & Kiyomatsu, K. (2020). A high-resolution, long-term wave resource assessment of Japan with wave-current effects. Renewable Energy, 161, 1341-1358. https://doi.org/10.1016/j.renene.2020.05.030
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Yücel, U., Özdemi̇r, E., & Ayaz, M. (2021). Yenilenebilir enerji kaynaklarından üretilen elektrik enerjisi teşvik yöntemlerinin incelenmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(2), 774-790. https://doi.org/10.29130/dubited.774963
Yüksel, F. Ş. (2023). Türkiye’nin havayolu taşıyıcı modellerine göre yolcu talebinin çoklu doğrusal regresyon, anfıs ve yapay sinir ağı teknikleri ile tahminlenmesi. (Doktora Tezi), Çukurova Üniversitesi, Fen Bilimleri Enstitüsü, Adana.
Zhang, J., Zhao, X., Greaves, D., & Jin, S. (2023). Modeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments. Applied Energy, 341, 121072. https://doi.org/10.1016/j.apenergy.2023.121072
Zheng, C.-w., Pan, J., & Li, J.-x. (2013). Assessing the China Sea wind energy and wave energy resources from 1988 to 2009. Ocean Engineering, 65, 39-48. https://doi.org/10.1016/j.oceaneng.2013.03.006
Zheng, J., Dai, P., & Zhang, J. (2015). Tidal stream energy in China. Procedia Engineering, 116, 880-887. https://doi.org/10.1016/j.proeng.2015.08.377
Zheng, Z., Ali, M., Jamei, M., Xiang, Y., Abdulla, S., Yaseen, Z. M., & Farooque, A. A. (2023). Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation. Renewable and Sustainable Energy Reviews, 185, 113645. https://doi.org/10.1016/j.rser.2023.113645

Toplam 101 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	Türkçe
Konular	Okyanus Mühendisliği, Yenilenebilir Enerji Sistemleri
Bölüm	Derleme Makaleler / Review Articles
Yazarlar	Selma Kaymaz 0009-0002-6342-871X Tuğrul Bayraktar 0000-0001-5620-5804 Çağrı Sel 0000-0002-8657-2303
Yayımlanma Tarihi	31 Ağustos 2024
Gönderilme Tarihi	2 Mart 2024
Kabul Tarihi	4 Haziran 2024
Yayımlandığı Sayı	Yıl 2024 Cilt: 29 Sayı: 2

Kaynak Göster

APA	Kaymaz, S., Bayraktar, T., & Sel, Ç. (2024). Su Dalga Enerjisi Üretimi ve Yapay Zekâ: Asya, Avrupa ve Türkiye’nin Potansiyeli. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 29(2), 798-822. https://doi.org/10.53433/yyufbed.1445985

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