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Machine learning approach to ship fuel consumption: A case of container vessel

机译：机器学习方法运输燃料消耗：容器船舶的情况

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摘要

An improvement of the marine vessel's fuel consumption will provide efficiency and profitability in ship management since fuel cost is one of the biggest operating cost. However, estimation of the fuel consumption of marine vessels is a difficult issue, because the fuel consumption rate of the vessel is directly dependent on multiple external factors such as the condition of the main engine, cargo weight, ship draft, sea condition, weather condition, etc. Nowadays, statistical models have been established based on actual ship data, and the fuel consumption of the vessel has been tried to be estimated as best as possible. In this study, various prediction models such as Multiple Linear Regression, Ridge and LASSO Regression, Support Vector Regression, Tree-Based Algorithms, Boosting Algorithms have been established for a container ship. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, coefficient of determination are employed in order to evaluate the correctness of estimation models and correlation analysis between variables is accomplished. Parameters such as main engine rpm, main engine cylinder values, scavenge air, shaft indicators are found highly correlated with fuel consumption. Under the influence of various external factors on fuel consumption, the nearest estimation of the actual fuel consumption data is made by multiple linear regression and ridge regression with 0.0001 root mean square error, 0.002 mean absolute error and %99.9 coefficient of determination score.
机译：由于燃料成本是最大的运营成本之一，船舶管理的船舶燃料消耗的提高将为船舶管理提供效率和盈利能力。然而，估计海洋船舶的燃料消耗是一个困难的问题，因为船只的燃料消耗率直接依赖于多个外部因素，例如主发动机，货物重量，船舶牵引，海况，天气状况如今，已经基于实际船舶数据建立了统计模型，并且已经尝试尽可能地估计船只的燃料消耗。在本研究中，各种预测模型，例如多元线性回归，脊和套索回归，支持向量回归，基于树的算法，升压算法，用于集装箱船。模型的准确性由k折叠交叉验证确定。诸如根均方误差等误差度量，使用误差，确定确定系数，以便评估估计模型的正确性和变量之间的相关分析。主发动机RPM，主发动机气缸值，清除空气，轴指示器等参数发现与燃料消耗高度相关。在各种外部因素对燃料消耗的影响下，最接近的实际燃料消耗数据估计是由0.0001根均方误差的多个线性回归和脊回归进行，0.002平均绝对误差和％99.9确定得分系数。

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