Browse > Article
http://dx.doi.org/10.1016/j.ijnaoe.2021.04.004

Fluctuation in operational energy efficiency of ships and its implications for performance appraisal  

Zhang, Shuang (Institute of Shipping Development, Dalian Maritime University)
Yuan, Haichao (College of Marine Engineering, Dalian Maritime University)
Sun, Deping (College of Marine Engineering, Dalian Maritime University)
Publication Information
International Journal of Naval Architecture and Ocean Engineering / v.13, no.1, 2021 , pp. 367-378 More about this Journal
Abstract
This paper develops a dynamic regression model to quantify the contribution of key external factors to operational energy efficiency of ships. On this basis, kernel density estimation is applied to explore distribution patterns of fluctuations in operational performance. An empirical analysis based on these methods show that distribution of fluctuations in Energy Efficiency Operational Indicator (EEOI) is leptokurtic and fat tailed, rather than a normal one. Around 85% of fluctuations in EEOI can be jointly explained by capacity utilization and sailing speed, while the rest depend on other external factors largely beyond control. The variations in capacity utilization and sailing speed cannot be fully passed on to the energy efficiency performance of ships, due to complex interactions between various external factors. The application of the methods is demonstrated, showing a potential approach to develop a rating mechanism for use in the legally binding framework on operational energy efficiency of ships.
Keywords
Ship operational energy efficiency; Fluctuation distribution; Performance appraisal; Dynamic regression model; Energy efficiency operational indicator (EEOI);
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 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.
2 Siegel, S., Castellan, N.J.J., 1988. Nonparametric Statistics for the Behavioral Sciences, second ed. Mcgraw-Hill Book Company, New York, England.
3 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.   DOI
4 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.   DOI
5 Brooks, M.R., Faust, P., 2018. 50 Years of review of Maritime transport, 1968-2018: reflecting on the past. Explor. Fut.
6 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.   DOI
7 Bowman, A.W., Hall, P., Titterington, D.M., 1984. Cross-validation in nonparametric estimation of probabilities and probability densities. Biometrika 71, 341-351.   DOI
8 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.   DOI
9 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.   DOI
10 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.   DOI
11 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.
12 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.   DOI
13 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.   DOI
14 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.
15 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.
16 International Maritime Organization, 2018. Resolution MEPC.304(72), Initial IMO Strategy on Reduction of GHG Emissions from Ships. International Maritime Organization (IMO), London, UK.
17 Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis, Forecasting, and Control, third ed. Prentice Hall, Englewood Cliffs. NJ.
18 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.   DOI
19 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.   DOI
20 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.   DOI
21 Man, Diesel, Turbo, 2004. Basic Principles of Ship Propulsion. MAN Diesel & Turbo, Denmark.
22 Marine Environment Protection Committee, 2020. REPORT OF THE MARINE ENVIRONMENT PROTECTION COMMITTEE ON ITS SEVENTY-FIFTH SESSION. International Maritime Organization (IMO).
23 Epanechnikov, V.A., 1969. Non-parametric estimation of a multivariate probability density. Theor. Probab. Appl. 14, 153-158.   DOI
24 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.
25 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.
26 Faber, J., Hoen, M., Nelissen, D., 2015. Empirical Comparative Analysis of Energy Efficiency Indicators for Ships. CE Delft, Delft.
27 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.   DOI
28 O'Keeffe, E., Smith, T., 2016. A Case Study of Fuel Monitoring and Efficiency Indicators for INTERTANKO.
29 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.
30 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.   DOI
31 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.   DOI
32 Pankratz, A., 1991. Forecasting with Dynamic Regression Models. John Wiley & Sons, Inc., New York.
33 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.   DOI
34 Parzen, E., 1962. On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065-1076.   DOI
35 RightShip, 2013. Calculating and Comparing CO2 Emissions from the Global Maritime Fleet.
36 Rosenblatt, M., 1956. Remarks on some nonparametrics estimates of a density function. Ann. Math. Stat. 27, 832-837.   DOI
37 Rudemo, M., 1982. Empirical choice of histograms and kernel density estimators. Scand. J. Stat. 9, 65-78.
38 Smith, T., Prakash, V., Aldous, L., Krammer, P., 2015. The Existing Shipping Fleet's CO2 Efficiency. UCL Energy Institute, London, UK.
39 Liaw, A., Wiener, M., 2002. Classification and Regression by randomForest [WWW Document]. R news.
40 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.   DOI
41 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.   DOI