Toward Realistic Ship Fuel Consumption Prediction Under Chronological Validation

Accurate prediction of ship propulsion fuel consumption from operational data is important for performance assessment and energy efficiency management. This study examines how temporal structure and validation strategy influence the predictive performance of regression-based fuel consumption models using real operational data from a seagoing vessel. A controlled experimental framework is used to isolate the effects of chronological validation, temporal feature augmentation based on operational inputs, and autoregressive target information. Under strict chronological validation, a baseline regression model achieves R2 = 0.788, while temporal feature augmentation improves performance to R2 = 0.845 without using past fuel consumption values. An autoregressive configuration yields R2 = 0.982, reflecting strong short-term persistence in the fuel consumption signal. Additional experiments show that random data partitioning can inflate reported R2 by up to 0.19 compared with chronological evaluation. The results demonstrate that reported predictive accuracy depends strongly on evaluation design and temporal information structure, highlighting the importance of chronological validation for realistic operational prediction.