A Novel Approach to Enhancing the Accuracy of Prediction in Ship Fuel Consumption

• Published in: Journal of marine science and engineering 2024-11, Vol.12 (11), p.1954
• Main Authors: Zhou, Tianrui, Wang, Jinggai, Hu, Qinyou, Hu, Zhihui
• Format: Article
• Language: English
• Subjects: Accuracy; Back propagation networks; Comparative analysis; Confidence intervals; data-driven; Datasets; Decision trees; Emission standards; Emissions; Energy efficiency; Energy management systems; Energy use; Fuel; Fuel consumption; Gaussian process; interval prediction; maritime carbon emissions reduction; Maritime industry; Neural networks; Predictions; Quantiles; Regression analysis; Regression models; Sensors; ship fuel consumption prediction; Statistical analysis; Variability
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse12111954

Ship fuel consumption plays a crucial role not only in understanding ships’ energy efficiency but also in gaining insights into their emissions. However, enhancing the accuracy of these predictions poses significant challenges due to data limitations and the methods employed. Due to these factors, such as data variability and equipment characteristics, ship fuel consumption exhibits certain fluctuations under specific conditions. Previous fuel consumption prediction methods primarily generate a single specific value, making it difficult to capture the volatility of and variability in fuel consumption. To overcome this limitation, this paper proposes a novel method that integrates Gaussian process prediction with quantile regression theory to perform interval predictions of ship fuel consumption, providing a range of possible outcomes. Through comparative analyses with traditional methods, the possibility of using the method is verified and its results are validated. The results indicate the following: (1) at a 95% confidence level, the proposed method achieves a prediction interval coverage probability of 0.98 and a prediction interval normalized average width of 0.123, which are significantly better than those of the existing backpropagation neural network (BPNN) and gradient boosting decision tree (GBDT) quantile regression models; (2) the prediction accuracy of the proposed method is 92% for point forecasts; and (3) the proposed method is applicable to main datasets, including both noon report and sensor datasets. These findings provide valuable insights into interval predictions of ship fuel consumption and highlight their potential applications in related fields, emphasizing the importance of accurate interval predictions in intelligent energy efficiency optimization.