과제정보
This research is supported by the national research projects on Initial IMO Strategy on Reduction of GHG Emissions from Ships and Associated Innovative Technologies [2018-473]. The authors would like to gratefully acknowledge the support of those reputable shipping companies, anonymized in this paper as required, for providing such comprehensive statistical data and valuable expertise.
참고문헌
- Acomi, N., Cristian Acomi, O., 2014. The influence of different types of marine fuel over the energy efficiency operational index. Energy Procedia 59, 243-248. https://doi.org/10.1016/j.egypro.2014.10.373.
- Adland, R., Cariou, P., Jia, H., Wolff, F.C., 2018. The energy efficiency effects of periodic ship hull cleaning. J. Clean. Prod. 178, 1-13. https://doi.org/10.1016/j.jclepro.2017.12.247.
- Bowman, A.W., Hall, P., Titterington, D.M., 1984. Cross-validation in nonparametric estimation of probabilities and probability densities. Biometrika 71, 341-351. https://doi.org/10.1093/biomet/71.2.341
- Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis, Forecasting, and Control, third ed. Prentice Hall, Englewood Cliffs. NJ.
- Box, G.E.P., Tiao, G.C., Kennedy, P.E., 1975. Intervention analysis with applications to economic and environmental problems. J. Am. Stat. Assoc. 70, 70-79. https://doi.org/10.1080/01621459.1975.10480264
- Brooks, M.R., Faust, P., 2018. 50 Years of review of Maritime transport, 1968-2018: reflecting on the past. Explor. Fut.
- Coraddu, A., Oneto, L., Baldi, F., Anguita, D., 2017. Vessels fuel consumption forecast and trim optimisation: a data analytics perspective. Ocean. Eng. 130, 351-370. https://doi.org/10.1016/j.oceaneng.2016.11.058.
- Deniz, C., Zincir, B., 2016. Environmental and economical assessment of alternative marine fuels. J. Clean. Prod. 113, 438-449. https://doi.org/10.1016/j.jclepro.2015.11.089.
- Epanechnikov, V.A., 1969. Non-parametric estimation of a multivariate probability density. Theor. Probab. Appl. 14, 153-158. https://doi.org/10.1137/1114019
- Faber, J., Hanayama, S., Zhang, S., Pereda, P., Comer, B., Hauerhof, E., Schim van der Loeff, W., Smith, T., Zhang, Y., Kosaka, H., 2020. Fourth IMO GHG Study. International Maritime Organization, London, UK.
- Faber, J., Hoen, M., Nelissen, D., 2015. Empirical Comparative Analysis of Energy Efficiency Indicators for Ships. CE Delft, Delft.
- Faber, J., Wang, H., Nelissen, D., Russel, B., Amand, D., 2010. Marginal abatement cost and cost effectiveness of energy-efficiency measures. Soc. Naval Arch. Mar. Eng. (SNAME) 161.
- Gkerekos, C., Lazakis, I., Theotokatos, G., 2019. Machine learning models for predicting ship main engine Fuel Oil Consumption: a comparative study. Ocean. Eng. 188, 106282. https://doi.org/10.1016/j.oceaneng.2019.106282.
- Granger, C.W.J., 1980. Testing for causality: a personal viewpoint. J. Econ. Dynam. Contr. 2, 329-352. https://doi.org/10.1016/0165-1889(80)90069-X.
- International Maritime Organization, 2018. Resolution MEPC.304(72), Initial IMO Strategy on Reduction of GHG Emissions from Ships. International Maritime Organization (IMO), London, UK.
- International Maritime Organization, 2011. Resolution MEPC.203(62), Amendments to MARPOL Annex VI (Inclusion of Regulations on Energy Efficiency for Ship. International Maritime Organization (IMO), London, UK.
- International Maritime Organization, 2009. MEPC.1/Circ.684, Guidelines for Voluntary Use of the Ship Energy Efficiency Operational Indicator (EEOI). International Maritime Organization (IMO), London, UK.
- Jensen, S., Lutzen, M., Mikkelsen, L.L., Rasmussen, H.B., Pedersen, P.V., Schamby, P., Lindegaard, L., Barbara, H., Vibsig, P., Schamby, P., 2018. Energy-efficient operational training in a ship bridge simulator. J. Clean. Prod. 171, 175-183. https://doi.org/10.1016/j.jclepro.2017.10.026.
- Kim, J.-H., Choi, J.-E., Choi, B.-J., Chung, S.-H., 2014. Twisted rudder for reducing fueloil consumption. Int. J. Naval Arch. Ocean Eng. 6, 715-722. https://doi.org/10.2478/IJNAOE-2013-0207.
- Kim, S.-H., Roh, M.-I., Oh, M.-J., Park, S.-W., Kim, I.-I., 2020. Estimation of ship operational efficiency from AIS data using big data technology. Int. J. Naval Arch. Ocean Eng. 12, 440-454. https://doi.org/10.1016/j.ijnaoe.2020.03.007.
- Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationarity against the alternative of a unit root. J. Econom. 159-178.
- Liaw, A., Wiener, M., 2002. Classification and Regression by randomForest [WWW Document]. R news.
- Lim, S.-S., Kim, T.-W., Lee, D.-M., Kang, C.-G., Kim, S.-Y., 2014. Parametric study of propeller boss cap fins for container ships. Int. J. Naval Arch. Ocean Eng. 6, 187-205. https://doi.org/10.2478/IJNAOE-2013-0172.
- Man, Diesel, Turbo, 2004. Basic Principles of Ship Propulsion. MAN Diesel & Turbo, Denmark.
- Marine Environment Protection Committee, 2020. REPORT OF THE MARINE ENVIRONMENT PROTECTION COMMITTEE ON ITS SEVENTY-FIFTH SESSION. International Maritime Organization (IMO).
- O'Keeffe, E., Smith, T., 2016. A Case Study of Fuel Monitoring and Efficiency Indicators for INTERTANKO.
- Owen, D., Demirel, Y.K., Oguz, E., Tezdogan, T., Incecik, A., 2018. Investigating the effect of biofouling on propeller characteristics using CFD. Ocean. Eng. 159, 505-516. https://doi.org/10.1016/j.oceaneng.2018.01.087.
- Pankratz, A., 1991. Forecasting with Dynamic Regression Models. John Wiley & Sons, Inc., New York.
- Park, S.H., Lee, I., 2018. Optimization of drag reduction effect of air lubrication for a tanker model. Int. J. Naval Arch. Ocean Eng. 10, 427-438. https://doi.org/10.1016/j.ijnaoe.2017.09.003.
- Parker, S., Raucci, C., Smith, T., Laffineur, L., Association, S., 2015. Understanding the Energy Efficiency Operational Indicator : an Empirical Analysis of Ships from the Royal Belgian. UCL Energy Institute, Royal Belgian Shipowners' Association.
- Parzen, E., 1962. On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065-1076. https://doi.org/10.1214/aoms/1177704472
- RightShip, 2013. Calculating and Comparing CO2 Emissions from the Global Maritime Fleet.
- Rosenblatt, M., 1956. Remarks on some nonparametrics estimates of a density function. Ann. Math. Stat. 27, 832-837. https://doi.org/10.1214/aoms/1177728190
- Rudemo, M., 1982. Empirical choice of histograms and kernel density estimators. Scand. J. Stat. 9, 65-78.
- Shumway, R.H., Stoffer, D.S., 2017. Time Series Analysis and its Applications with R Examples, fourth ed. Springer International Publishing. https://doi.org/10.1007/978-3-319-52452-8.
- Siegel, S., Castellan, N.J.J., 1988. Nonparametric Statistics for the Behavioral Sciences, second ed. Mcgraw-Hill Book Company, New York, England.
- Smith, T., Prakash, V., Aldous, L., Krammer, P., 2015. The Existing Shipping Fleet's CO2 Efficiency. UCL Energy Institute, London, UK.
- Uyanik, T., Karatug, C., Arslano glu, Y., 2020. Machine learning approach to ship fuel consumption: a case of container vessel. Transport. Res. Transport Environ. 84, 102389. https://doi.org/10.1016/j.trd.2020.102389
- Zhang, B.-J., Zhang, Z.-X., 2015. Research on theoretical optimization and experimental verification of minimum resistance hull form based on Rankine source method. Int. J. Naval Arch. Ocean Eng. 7, 785-794. https://doi.org/10.1515/ijnaoe-2015-0055.
- Zhang, J., Yang, S., Liu, J., 2018. Numerical investigation of a novel device for bubble generation to reduce ship drag. Int. J. Naval Arch. Ocean Eng. 10, 629-643. https://doi.org/10.1016/j.ijnaoe.2017.10.009.
- Zhang, S., Li, Y., Yuan, H., Sun, D., 2019. An alternative benchmarking tool for operational energy efficiency of ships and its policy implications. J. Clean. Prod. 240, 118-223. https://doi.org/10.1016/j.jclepro.2019.118223.