인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Growing demands for maritime decarbonization have increased the need for data-driven approaches to improve fuel efficiency. This study develops a main engine fuel-consumption prediction model using Noon Report data from an operating bulk carrier. A systematic framework was constructed, including preprocessing, voyage-based cross-validation, hyperparameter tuning, and hybrid modeling. Ridge regression was selected as the baseline and further optimized using Optuna. To address residual errors, a Gradient Boosting Regressor (GBR) was introduced as a secondary learner. The hybrid Ridge–GBR model achieved the best performance, improving RMSE from 2.9164 to 2.8137 and R² from 0.9263 to 0.9315. Residual and cumulative-error analyses showed that the hybrid approach preserved the overall characteristics of the Ridge model while providing modest reductions in localized errors, particularly in segments with higher fuel-consumption variability. Feature analysis indicated that propulsion-related variables—engine distance and shaft revolution rate—were dominant in both the Ridge estimator and the GBR residual corrector, while course, mid and aft drafts contributed to correcting remaining nonlinear patterns. Weather-related variables exhibited limited influence in residual modeling. These results demonstrate that the proposed hybrid framework effectively combines linear and localized nonlinear patterns, offering a reliable methodology for fuel-efficiency assessment using routine Noon Report data.
본문·목차
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