Prediction of ship fuel consumption by using an artificial neural network

Jeon, Miyeon; Noh, Yoojeong; Shin, Yong-Woo; Lim, O-Kaung; Lee, Inwon; Cho, Dae‐Seung

doi:10.1007/s12206-018-1126-4

Cited by 83 publications

(48 citation statements)

References 11 publications

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“…Bayesian optimization is an algorithm that uses the Bayesian theorem to adaptively generate data for hyperparameters and find the optimum hyperparameter values using surrogate models. It can attain a better performance on a test set with less iterations than a random search or a grid search [10]. To avoid the overfitting of the model and to ensure that the chosen hyperparameter combination values are near the optimal values, a k-fold cross-validation [24] technique is applied.…”

Section: Model Hyperparameter Tuningmentioning

confidence: 99%

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Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

et al. 2020

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As oil prices continue to rise internationally, shipping costs are also increasing rapidly. In order to reduce fuel costs, an economical shipping route must be determined by accurately predicting the estimated arrival time of ships. A common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. In this study, a machine learning approach was proposed to predict ship speed over the ground using the automatic identification system (AIS) and noon-report maritime weather data. To train and validate the developed model, the AIS and marine weather data of the seventy-six vessels for a period one year were used. The model accuracy result shows that the proposed data-driven model has a satisfactory capability to predict the ship speed based on the chosen features.

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Section: Model Hyperparameter Tuningmentioning

confidence: 99%

“…Choosing the right machine learning model to evaluate ship speed and assess ship performance while sailing is always challenging, especially when applied to big data [9][10][11]. Applying a simple model, such as a linear regression, may not be precise enough [12].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

et al. 2020

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“…Neural networks can have structures with various shapes. However, the MLP is the most widely used type of neural network model for data analysis [20]. An MLP neural network model consists of an input layer, a hidden layer, and an output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Li et al constructed a model to predict the NOx and smoke emissions of diesel engines. They utilized a multilayer perceptron (MLP) neural network model, which achieved extremely high prediction rates for NOx and smoke emissions [20]. De Cesare et al determined whether a NOx sensor can be replaced with a virtual sensor.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network

et al. 2020

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Facing the reinforced emission regulations and moving toward a clean powertrain, hydrogen has become one of the alternative fuels for the internal combustion engine. In this study, the prediction methodology of hydrogen yield by on-board fuel reforming under a diesel engine is introduced. An engine dynamometer test was performed, resulting in reduced particulate matter (PM) and NOx emission with an on-board reformer. Based on test results, the reformed gas production rate from the on-board reformer was trained and predicted using an artificial neural network with a backpropagation process at various operating conditions. Additional test points were used to verify predicted results, and sensitivity analysis was performed to obtain dominant parameters. As a result, the temperature at the reformer outlet and oxygen concentration is the most dominant parameters to predict reformed gas owing to auto-thermal reforming driven by partial oxidation reforming process, dominantly.

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“…Data analysis for the development of a regression model based on artificial neural networks was employed in [2] to accurately predict fuel-oil consumption (FOC). Since the available dataset was not too large, comprising 4000 samples, 7 input variables, and 1 target feature, we shall not refer to it as big data, and the data preprocessing pipeline proposed is deemed appropriate for effectively manipulating and refining vessel-related data with large capacity.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power

Theodoropoulos

Spandonidis

Themelis

et al. 2021

JMSE

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Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models were compared with values measured onboard. Comparisons between the two types of networks were also performed. Emphasis was placed on the different data pre-processing phases, as well as on the optimal configuration decision process for each of the developed deep-learning models. Factors and parameters that played a significant role in the outcome, such as the number of layers in the neural network, were also evaluated.

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Prediction of ship fuel consumption by using an artificial neural network

Cited by 83 publications

References 11 publications

Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network

Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power

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