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주성분 분석기법을 이용한 선박의 연료소비 예측에 관한 연구

A Study on the Prediction of Fuel Consumption of a Ship Using the Principal Component Analysis

  • Kim, Young-Rong (Graduate School of Korea Maritime and Ocean University) ;
  • Kim, Gujong (Korea Maritime Safety Tribunal) ;
  • Park, Jun-Bum (Div. of Navigation Science, Korea Maritime and Ocean University)
  • 투고 : 2019.07.26
  • 심사 : 2019.09.09
  • 발행 : 2019.12.31

초록

최근 선박의 배기가스 규제가 강화되면서 연료소비량을 저감하기 위한 많은 방안들이 검토되고 있다. 그중에서도 선박으로부터 수집한 데이터를 활용하여 연료소모량을 예측하는 기계학습 모델을 개발하고자 하는 연구가 활발히 수행되고 있다. 하지만 많은 연구들이 학습모델의 주요 변수 선정이나 수집데이터의 처리 방법에 대한 고려가 미흡하였으며, 무분별한 데이터의 활용은 변수 간의 다중공선성 문제를 야기할 수도 있다. 본 연구에서는 이러한 문제점을 해결하기 위하여 주성분 분석을 이용하여 선박의 연료소비를 예측하는 방법을 제시하였다. 13K TEU 컨테이너 선박의 운항데이터에 주성분 분석을 수행하였으며, 추출한 주성분으로 회귀분석을 수행하여 연료소비 예측모델을 구현하였다. 평가용 데이터에 대한 모델의 설명력은 82.99%이었으며, 이러한 예측모델은 항해 계획 수립 시 운항자의 의사결정을 지원하고 항해 중 에너지 효율적인 운항상태 모니터링에 기여할 수 있을 것으로 기대된다.

As the regulations of ship exhaust gas have been strengthened recently, many measures are under consideration to reduce fuel consumption. Among them, research has been performed actively to develop a machine-learning model that predicts fuel consumption by using data collected from ships. However, many studies have not considered the methodology of the main parameter selection for the model or the processing of the collected data sufficiently, and the reckless use of data may cause problems such as multicollinearity between variables. In this study, we propose a method to predict the fuel consumption of the ship by using the principal component analysis to solve these problems. The principal component analysis was performed on the operational data of the 13K TEU container ship and the fuel consumption prediction model was implemented by regression analysis with extracted components. As the R-squared value of the model for the test data was 82.99%, this model would be expected to support the decision-making of operators in the voyage planning and contribute to the monitoring of energy-efficient operation of ships during voyages.

키워드

참고문헌

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