Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods

• Published in: Journal of marine science and engineering 2023-04, Vol.11 (4), p.738
• Main Authors: Xie, Xianwei, Sun, Baozhi, Li, Xiaohe, Olsson, Tobias, Maleki, Neda, Ahlgren, Fredrik
• Format: Article
• Language: English
• Subjects: Acceleration; acceleration and deceleration process; Accuracy; Air pollution; Algorithms; Bioenergiteknik/Energi- och Miljöteknik; Bioenergy Technology; Black boxes; black-box model; Cleaning; Computer Science; data cleaning method; Data collection; Datavetenskap; Deceleration; Deep learning; Emission analysis; Emissions control; Energy conservation; Energy consumption; Energy efficiency; Energy use; Fuel; Fuel consumption; Greenhouse gases; Learning algorithms; Machine learning; Maritime Science; Mathematical analysis; Mathematical models; Methods; Model accuracy; Navigation; Neural networks; Operating costs; Optimization; Prediction models; Preprocessing; Sensors; ship fuel consumption prediction; Ship models; Shipping; Shipping industry; Sjöfartsvetenskap; Statistical analysis; Values; white-box model
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11040738

An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R2 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R2 also can reach 0.9954, which can provide decision support for the operation of shipping companies.