Big data και πρόβλεψη της κατανάλωσης καυσίμου στη ναυτιλία. Ανασκόπηση βιβλιογραφίας
Big data and fuel oil consumption in shipping. A literature review

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Abstract
The prediction of fuel consumption on ships, under different sailing conditions, is a current but at the same time a rather difficult approach issue, as there are numerous factors that affect the fuel consumption of a ship, such as the condition of the main engine, the weight of cargo, ship's draft, sea state, and weather conditions. At the same time, the optimal use of modern technologies as well as scientific fields such as data analysis and modeling, can bring about solutions such as the optimization of a ship's performance per trip, in real-time, the reduction of pollutants, compliance with international regulations, but also the saving of energy efficiency and operating costs of a fleet.
Therefore, the purpose of this thesis is the interdisciplinary examination and comparison of the already existing research that has been carried out on the prediction of fuel consumption in ocean-going ships, with the ultimate aim of drawing conclusions regarding the opportunities and difficulties faced by the shipping industry in this effort. In order to achieve the above goal, the structure of this work was divided into five main sections, which are examined a) the modern sources of data extraction from a seagoing ship today, b) the current use of big data in the shipping industry and the prospects for further utilization, c) what depends on the fuel consumption of ocean-going ships today, d) what are the already existing research and scientific approaches in terms of predicting fuel consumption based on the studies today and finally e) what are the similarities and differences of the studies under study with the aim of drawing similar conclusions.


