| Abstract | In shipyards, EEOI estimation is required to compare the efficiency of ships and to check the time-varying efficiency of the ships. However, since it is difficult to obtain operating data required for EEOI estimation, it is necessary to estimate EEOI using public data such as Automatic Identification System (AIS) data, ship and engine data, and weather data. In this study, a method for EEOI estimation using public data was proposed. In the proposed method, total resistance and propeller efficiencies are estimated using the Holtrop-Mennen method, additional resistance is estimated following the International Organization for Standardization (ISO)15016:2015, and engine power is estimated using the modified Direct Power Method (DPM) and Holtrop-Mennen method. Since the public data have a large capacity, big data technologies such as Hadoop and Spark were applied. The public data was stored to Hadoop, and the data was processed using Spark. Moreover, to reduce the computation time for EEOI estimation, a surrogate model constructed using deep learning was also applied. To evaluate the effectiveness of the proposed method, it is applied to estimate EEOI of the example ship. The result shows that the method can estimate EEOI effectively and accurately. |
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| Publication Date | 2018-02-26 |
Seong-Hoon Kim, "A Study on the Method for the Estimation of Energy Efficiency Operational Indicator of a Ship Based on Technologies of Big Data and Deep Learning", M.Sc. Thesis, Seoul National University, 2018.02.26
김성훈, "빅데이터와 딥러닝 기술을 기반으로 한 선박 에너지 효율 운항 지표 예측 방법에 대한 연구", 2018.02.26