International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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Estimation of ship operational efficiency from AIS data using big data technology

  • Kim, Seong-Hoon (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Roh, Myung-Il (Department of Naval Architecture and Ocean Engineering, Research Institute of Marine Systems Engineering, Seoul National University) ;
  • Oh, Min-Jae (School of Naval Architecture and Ocean Engineering, University of Ulsan) ;
  • Park, Sung-Woo (Interdisciplinary Program in Offshore Plant Engineering, Seoul National University) ;
  • Kim, In-Il (Smartship Department, R&D Institute, Daewoo Shipbuilding & Marine Engineering Co., Ltd.)
  • Received : 2019.06.13
  • Accepted : 2020.03.03
  • Published : 2020.12.31
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Abstract

To prevent pollution from ships, the Energy Efficiency Design Index (EEDI) is a mandatory guideline for all new ships. The Ship Energy Efficiency Management Plan (SEEMP) has also been applied by MARPOL to all existing ships. SEEMP provides the Energy Efficiency Operational Indicator (EEOI) for monitoring the operational efficiency of a ship. By monitoring the EEOI, the shipowner or operator can establish strategic plans, such as routing, hull cleaning, decommissioning, new building, etc. The key parameter in calculating EEOI is Fuel Oil Consumption (FOC). It can be measured on board while a ship is operating. This means that only the shipowner or operator can calculate the EEOI of their own ships. If the EEOI can be calculated without the actual FOC, however, then the other stakeholders, such as the shipbuilding company and Class, or others who don't have the measured FOC, can check how efficiently their ships are operating compared to other ships. In this study, we propose a method to estimate the EEOI without requiring the actual FOC. The Automatic Identification System (AIS) data, ship static data, and environment data that can be publicly obtained are used to calculate the EEOI. Since the public data are of large capacity, big data technologies, specifically Hadoop and Spark, are used. We verify the proposed method using actual data, and the result shows that the proposed method can estimate EEOI from public data without actual FOC.

Keywords

Acknowledgement

This work was partially supported by (a) Daewoo Shipbuilding & Marine Engineering Co., Ltd., Republic of Korea, (b) MSIP (Ministry of Science and ICT), Republic of Korea, under Development of Ship Design Standard PLM Platform based on Big Data (Grant No. NIPA-2016-S1106-16-1025) supervised by the NIPA (National IT Industry Promotion Agency), and (c) Research Institute of Marine Systems Engineering of Seoul National University, Republic of Korea.

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