Abstract
Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness. In this study, we propose a novel hybrid fuel consumption prediction model based on sensor data collected from an ocean-going container ship. First, a data processing method is proposed to clean the collected data. Secondly, the Bayesian optimization method of hyperparameters is used to reasonably set the hyperparameter values of the model. Finally, a hybrid fuel consumption prediction model is established by integrating extremely randomized tree (ET), random forest (RF), Xgboost (XGB) and multiple linear regression (MLR) methods. The experimental results show that data cleaning, the size of the dataset, marine environmental factors, and hyperparameter optimization can all affect the accuracy of the model, and the proposed hybrid model provides better predictive performance (higher accuracy) and greater robustness (smaller standard deviation) as compared with a single model. The proposed hybrid model should play a significant role in ship fuel consumption real-time monitoring, fault diagnosis, energy saving and emission reduction, etc.
Highlights
The maritime transportation industry has played a significant role in the cargo industry as a whole since the development of international trade [1,2], and it has an important impact on the development of the national economy [3]
To verify the superiority of the proposed hybrid model, it was validated against reference models developed using multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), extremely randomized tree (ET), random forest (RF), and XGB
Since the training set and the test set were divided according to random− state, the model result in a certain random− state does not indicate whether the model is suitable; it is necessary to test in different random− state and take the average value as the value of model performance
Summary
The maritime transportation industry has played a significant role in the cargo industry as a whole since the development of international trade [1,2], and it has an important impact on the development of the national economy [3]. The total volume of international seaborne trade has been growing significantly over the last years [4]. Container shipping is important for global seaborne trade and the quantity of cargo transported by container shipping has been increasing over the past decades [5]. Of the total operating costs, respectively [6]. Another side effect of the significant volume of maritime transportation is an increase in ship-induced greenhouse gas emissions. The literature proves that greenhouse gases emitted by ships mainly include SO2 , NOx , CO2 , PM2.5 , PM10 [8] and some technical research aiming at greenhouse gas emissions reduction is being worked on by some experts, such as concerning seawater desulphurization [9,10,11]
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