VERİYE DAYALI YÖNTEMLER YARDIMI İLE KİMYASAL TANKERDE YAKIT TÜKETİMİ TAHMİNİ

Year 2022, Volume: 14 Issue: 2, 190 - 205, 30.12.2022

Tayfun Uyanık

https://doi.org/10.18613/deudfd.1015260

Cited By: 1

Abstract

Ticari gemilerde yakıt tüketimi denizcilik işletmelerinde en önemli gider kalemini oluşturmaktadır. Aynı zamanda enerji verimliliği ile de yakından alakalı olan bu konu denizcilik sektörü açısından son derece önem arz etmektedir. Uluslararası Denizcilik Örgütü kuralları gereği denizcilik sektöründe emisyon azaltma konusunun gündemdeki yerini koruduğu da göz önünde bulundurulduğunda gemilerde yakıt tüketimi ve ortaya çıkan emisyonlar denizcilik otoriteleri tarafından ciddi olarak takip edilmektedir.
Bu çalışmada bir kimyasal tanker gemisinin yakıt tüketimi gerçek sefer verilerinden hareketle veriye dayalı yöntemler yardımıyla modellenip tahmin edilmiştir. Öncelikle gemiden alınan sefer verileri işlenip algoritmaların üzerinde çalışabileceği hale getirilmiştir. Algoritmalar veri seti üzerinde çalıştırılmış ve yakıt tüketimi tahmin başarımları incelenmiştir. İlk aşamada bazı algoritmaların başarısı yetersiz bulunmuştur. Tahmin başarımları yetersiz bulunan algoritmaların parametreleri ayarlanarak tahmin işlemi tekrar edilmiştir. Son olarak hata metrikleri kullanılarak algoritmaların yaptığı tahminler karşılaştırılmıştır. Sonuçlar incelendiğinde Çok Katmanlı Derin Sinir Ağı yönteminin kimyasal tanker yakıt tüketimi tahmini problemi kapsamında ele alınan diğer yöntemlerden daha başarılı olduğu tespit edilmiştir.

Keywords

Kimyasal tanker, enerji verimliliği, yakıt tüketimi tahmini, veriye dayalı yöntemler.

FUEL CONSUMPTION PREDICTION IN CHEMICAL TANKER WITH DATA-DRIVEN METHODS

Abstract

Fuel consumption in commercial ships constitutes the most important expense item in maritime enterprises. This issue, which is also closely related to energy efficiency, is extremely important for the maritime industry. Considering that the issue of emission reduction remains on the agenda in the maritime sector as per the International Maritime Organization rules, fuel consumption and the emissions on ships are followed seriously by the maritime authorities.
In this study, the fuel consumption of a chemical tanker ship was modeled and estimated with the help of data-driven methods based on real voyage data. First of all, the voyage data taken from the ship was processed and made into a way that algorithms can work on. Algorithms were run on the data and fuel consumption prediction performances were examined. The success of some models established in the first stage was found to be inadequate. The estimation process was repeated by tuning the algorithm parameters with unsatisfactory estimation performance. Finally, the predictions were compared using error metrics. When the results are examined, it has been determined that the Multi-Layer Deep Neural Network method is more successful than the other methods discussed for the chemical tanker fuel consumption estimation problem.

Keywords

Chemical tanker, energy efficiency, fuel consumption estimation, data-driven methods.

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