An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments

An accurate prediction of ship fuel consumption is critical for speed,trim,and voyage optimisation etc.While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors,research on the impact of environmental factors on fuel consumption has been lacking.In addition,although recent research efforts have widely focused on machine learning methods to predict fuel consumption,studies on hyperparameter values that are suitable for these prediction models are limited.To compensate for this deficiency in existing literature,an adaptive hyperparameter tuning method is proposed,and the effects of maritime environmental factors on fuel consumption are taken into account.Through experimentation,the proposed adaptive hyperparameter tuning method was validated via artificial neural network(ANN),support vector regression(SVR),random forest(RF),and least absolute shrink-age and selection operator(Lasso).The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination(R 2)of these algorithms.The increase of the amplitude demonstrated the following trend,in the order of the largest increase to the lowest increase:ANN,Lasso,SVM,and RF.The rates of increase were between 0.0773%and 2.1653%.Furthermore,after the environmental factors were considered,the prediction accuracies of the ANN and Lasso increased;however,the opposite was observed for the SVR and RF.As such,we confirmed that the use of Bayesian optimisation for hyper-parameter tuning can effectively improve the fuel consumption prediction accuracy,and our proposed model can therefore serve as a significant reference for calculating fuel consumption.

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.