Jinlu Sheng, Qiang Guo, Li Wan, and Fang Mei
Big data; main engine fuel consumption; fuel consumption prediction; BP neural network
The major expense of sailing for ocean ships is the fuel consumption of the main engine. This study aims to directly reflect the multiple influencing factors of the fuel consumption of the ship’s main engine (SME) during navigation and accurately predict the change in fuel consumption of the SME in the future, allowing the shipping company to take reasonable energy-saving and emissions-reduction measures. The ship cruise was the subject of this paper’s data mining and analysis. Furthermore, the BP neural network model was used to provide a visual representation of both the SME’s fuel use and its related influencing elements, as well as to forecast the boats’ fuel consumption throughout a sailing time. Furthermore, the predicted outcomes of the BP neural network model and the standard model in terms of fuel consumption prediction were compared. The impact of the main engine’s consumption characteristics, the difference value of draft, and the shipping speed on the ship’s fuel consumption during navigation was clearly portrayed on the SME’s visual display. The fuel consumption of the ship’s main engine, as predicted by the BP neural network model based on trip data, was determined to be more accurate than the figure anticipated by the conventional model. Furthermore, the BP neural network model based on voyage data can not only estimate the ship’s various navigation conditions but also accurately predict the changing trend of fuel consumption, and it can be used as one of the factors to consider when a shipping company manages ocean ship navigation.
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