As fuel is one of the largest operating expenses, reducing the fuel consumption of vessels can increase efficiency and profitability in ship management. However, estimating the fuel consumption of a ship is a challenging task because the rate of fuel consumption of the vessel directly depends on several external factors, like the main engine, the weight of the containers, the ship's draught, the state of the sea, the weather, etc. The potential for saving fuel is feasible for both newly built ships as well as for ships already in service if greater energy efficiency measures are implemented. The parameters that can influence fuel reduction are ship speed, trim, weather conditions (wave and wind), mean draft, etc. Due to engines' increased power, vessels are the biggest consumers of gasoline. A ship's energy needs are mostly met by propulsion (82%), electric power generation (17%), and limited steam generation (1%). In this paper, various machine learning algorithms are reviewed to estimate the fuel consumption of ships. The findings presented in this paper can be used as a reference for creating a better ship energy efficiency management plan.

vessel, energy efficiency, machine learning, predicting fuel consumption