Using Artificial Neural Networks for predicting ship fuel consumption

• Autorzy: Nguyen Van Giao , Sakthivel Rajamohan , Rudzik Krzysztof , Kozak Janusz , Sharma Prabhakar , Pham Nguyen Dang Khoa , Nguyen Phuoc Quy Phong , Nguyen Xuan Phuong

Identyfikatory

DOI

10.2478/pomr-2023-0020

• Języki publikacji: EN

Abstrakty

EN

In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

Słowa kluczowe

EN

artificial neural network; fuel management; marine engine; ship fuel consumption; energy efficiencys

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2023
• Tom: nr 2
• Strony: 39--60
• Opis fizyczny: Bibliogr. 203 poz., rys., tab.

Twórcy

autor

Nguyen Van Giao

• Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam

autor

Sakthivel Rajamohan

• Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

• https://orcid.org/0000-0002-0766-0285

autor

Rudzik Krzysztof

• Gdynia Maritime University, Faculty of Marine Engineering, Poland

• https://orcid.org/0000-0001-9788-273X

autor

Kozak Janusz

• Gdansk University of Technology, Poland

• https://orcid.org/0000-0002-0169-5356

autor

Sharma Prabhakar

• Mechanical Engineering Department, Delhi Skill and Entrepreneurship University, India

• https://orcid.org/0000-0002-7585-6693

autor

Pham Nguyen Dang Khoa

• PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

autor

Nguyen Phuoc Quy Phong

• PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

autor

Nguyen Xuan Phuong

• PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam

• https://orcid.org/0000-0003-0354-8648

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).