Estimation of ship operational efficiency from AIS data using big data technology
0
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (21)
- Abbasian, N. S., "Improving early OSV design robustness by applying ‘Multivariate Big Data Analytics’ on a ship's life cycle", J. Ind. Inf. Integr, pp.002~ (2018)[DOI]
- Bernitsas, M. M., "Kt, Kq and Efficiency Curves for the Wageningen B-Series Propellers", Naval Architecture and Marine Engineering, pp.1981
- Bouman, E. A., "State-of-the-art technologies, measures, and potential for reducing GHG emissions from shipping - a review", Transp. Res. Part D Transp. Environ, 52, pp.408~421 (2017)[DOI]
- Charchalis, A., "Estimation of Hull's resistance at preliminary phase of designing", J. KONES Powertrain Transp, 24 (2017)[DOI]
- Chi, H., "An AIS-based framework for real time monitoring of vessels efficiency", IEEE International Conference on Industrial Engineering and Engineering Management, pp.1218~1222 (2015)
- Dobrkovic, A., "Maritime pattern extraction from AIS data using a genetic algorithm", Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, pp.642~651 (2016), IEEE
- Holtrop, J., "A statistical resistance prediction method with a speed dependent form factor", Proceedings of Scientific and Methodological Seminar on Ship Hydrodynamics, pp.1~7 (1988)
- Third IMO Greenhouse Gasses Study 2014: Executive Summary, "International Maritime Organization", ~ (2014)
- Guidance for the Development of a Ship Energy Efficiency Management Plan (SEEMP), "International Maritime Organization", ~ (2009)
- International Maritime Organization, "Guidelines for the Installation of a Shipborne Automatic Identification System(AIS)", ~ (2003)
- International Organization Standardization, "ISO 15016, 2015. - Ships and Marine Technology – Guidelines for the Assessment of Speed and Power Performance by Analysis of Speed Trial Data", ~ (2015)
- Park, S. W., "Association analysis of piping materials of an offshore structure using big data technology", J. Ship Prod. Des, 35(3), pp.220~230 (2019)[DOI]
- Perez, H. M., "Automatic identification systems (AIS) data use in marine vessel emission estimation", 18th Annual International Emissions Inventory Conference (2009)
- Rakke, S. G., "Ship Emissions Calculation from AIS", pp.2016
- Schneekluth, H., "Ship Design for Efficiency and Economy" (1998), Butterworth-Heinemann
- Smith, T., "Assessment of shipping ’ s efficiency using satellite AIS data aldous and Paolo agnolucci prepared for", The International Council on Clean Transportation March 2013 CONTENTS (2013)
- Tichavska, M., "Air emissions from ships in port : does regulation make a difference? Transp", Policy, 75, pp.128~140 (2019)[DOI]
- Tsou, M. -C., "Online analysis process on Automatic Identification System data warehouse for application in vessel traffic service", Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, pp.199~215 (2016)
- Wen, Y., "Green routing design in short seas", Int. J. Shipp. Transp. Logist. (IJSTL), 9, pp.371~390 (2017)[DOI]
- Xiao, F., "Comparison study on AIS data of ship traffic behavior", Ocean Eng, 95, pp.84~93 (2015)[DOI]
- Zhang, S., "Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity", Ocean Eng, 155, pp.240~250 (2018)[DOI]
