Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study

Gkerekos, Christos; Lazakis, Iraklis; Theotokatos, Gerasimos

doi:10.1016/j.oceaneng.2019.106282

Cited by 160 publications

(85 citation statements)

References 28 publications

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“…According to the results, the R 2 value of estimation methodology is 90%. Comparing the R 2 values of both studies show that Gkerekos et al, 59 reached a more accurate result most probably due to the more detailed noon report dataset. Wang et al, 60 studied on dynamic optimization of ship energy efficieny.…”

Section: Neuronmentioning

confidence: 78%

“…However, the authors indicated that weather conditions were not taken into consideration, which means that the fuel consumption estimation may not be realized accurately. Gkerekos et al, 59 utilized a machine learning method to predict ship fuel consumption in dynamic conditions such as vessel speed, engine speed, sea current, wind speed, wind direction, daily fuel consumption, daily distance run, sea state, sea direction, slip and draft. The method was developed by using noon reports.…”

Section: Resultsmentioning

confidence: 99%

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Estimation of ship flue gas emissions in dynamic operational conditions with ANN

Bilgili

Çelebi

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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While ship emissions are incomparably less in comparison to land units, the threat of the world’s population and certain regions are in a serious danger due to 70% of these emissions occur in areas up to 400 km from the coast and on certain maritime routes. The formation of ship emissions strongly depends on the dynamically varying travel conditions and it is important to identify the emissions in order to guide the rule-makers to take the necessary measures correctly. In this study, an emission estimation modelling based on dynamic variables, such as voyage duration, engine revolutions per minute, speed, displacement, weather condition, sea conditions and average draught, has been realized by using artificial neural networks (ANN). The difference between the results of ANN and traditional estimation methods was found to be 1.57%. Then, in order to determine the optimum route, the ANN model was implemented for June and January in the Northern and Southern routes of the Atlantic and Pacific Oceans and it was concluded that harder sea and weather conditions produced more emissions. Finally, fuel consumptions, fuel costs and social costs of the emissions for different routes were calculated.

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Section: Neuronmentioning

confidence: 78%

Section: Resultsmentioning

confidence: 99%

Estimation of ship flue gas emissions in dynamic operational conditions with ANN

Bilgili

Çelebi

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…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. Also, [3] concerns the development of data-driven models for the prediction of ship main engine FOC. For this study case, two different strategies for the data acquisition endeavor were considered and compared, namely noon-reports and automated data logging and monitoring (ADLM) systems, each at different sampling rates.…”

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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“…Yang and Chen et al [13] proposed a novel Genetic Algorithm-based GBM (GA-based GBM) for ship fuel consumption prediction. Gkerekos and Lazakis et al [14] presented a comparison of multiple data-driven regression algorithms for predicting the ship main engine FOC (Fuel Oil Consumption), including SVMs (Support Vector Machines), RFRs (Random Forest Regressors), ETRs (Extra Trees Regressors), and ANNs (Artificial Neural Networks). Hu and Jin et al [15] collected two different sets of data showing the fuel consumption of a voyage ship with and without the influence of marine environmental factors and used the machine learning of BPNN (Back-Propagation Neural Network) and GPR (Gaussian Process Regression) to train and predict the ship fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

Zhi

Liu

et al. 2020

IEEE Access

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The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this paper, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R 2 (R-squared) increased to 0.9843. What's more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.

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Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study

Cited by 160 publications

References 28 publications

Estimation of ship flue gas emissions in dynamic operational conditions with ANN

Estimation of ship flue gas emissions in dynamic operational conditions with ANN

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

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

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