Ship Energy Efficiency Management Plan Development Using Machine Learning: Case Study of CO2 Emissions of Ship Activities at Container Port

Abstract

Ship energy management has an effect both on cost efficiency and the environment due to the huge amount of CO2 emission caused by ship activities. Meanwhile, research regarding efforts to save energy consumption in the container terminal area is scarce. This paper aims to estimate CO2 emissions from ship activities in the container port. The influential variable of CO2 emissions is in consideration to Ship Energy Efficiency Management Plan (SEEMP). The estimation of C02 emission starts from the ship activities when the ship approaches the port, which includes ship maneuvering and ship berthing. Ship's energy consumption and CO2 emission were analyzed using random forest regression (RF) at the default setting, and then the effectiveness was verified using k-folds cross-validation. The analysis result showed there are five influential variables to reduce the CO2 emission: (1) main engine power; (2) auxiliary engine power; (3) waiting time in a port basin; (4) maneuvering time; and (5) berthing time. Among those five variables, maneuvering, waiting in a port basin, and berthing have the same position at the top with the same amount of weight importance from the four attribute selection training results. The random forest model training and k-folds cross-validation confirmed that the model has 98.85% of accuracy. Finally, a fuel-efficient operation is discussed, and it can be concluded that by combining several voyage optimizations with a skilled operator and cold ironing when available, it is possible to reduce the CO2 emission by 20%. The findings and proposed plan in this paper can become a reference to develop Ship Energy Efficiency Management Plan.

Developing Machine Learning Model to Predict HVAC System of Healthy Building: A Case Study in Indonesia

“…This opensource data mining tool is known for the versatility and wide range of applications. It can be used as a standalone framework for data analysis or seamlessly integrated into other software for data mining (Dawangi and Budiyanto, 2021;László and Ghous, 2020). Furthermore, the user-friendly interface and high visualization capabilities facilitate data interaction and insight generation without extensive coding.…”

Performance Analysis of Combined Gas-Electric Steam Turbine System as Main Propulsion for Small-scale LNG Carrier Ships

“…The damage assessment analysis compares the output emissions produced by the COGES propulsion system to those produced by other propulsion systems such as the diesel propulsion system or the dual fuel diesel electric (DFDE) system. In such cases, the estimation of carbon emissions begins with the ship's activities as it approaches the port, which includes ship maneuvering and berthing (Dawangi and Budiyanto, 2021).…”

Performance Analysis of Duct Vane Application on Patrol Boats Using Computational Fluid Dynamics

“…The total resistance and increased resistance caused by the hydroelastic body were explored, as well as the hydroelastic body's influence on the magnitude of resistance and added resistance (Baso et al, 2022). Actively reducing ship resistance can have an effect when considering the ship energy efficiency management plan (Dawangi and Budiyanto, 2021).…”