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Load survey has become a routine project for shipbuilding and shipping companies to investigate electrical load characteristics to enhance the power system planning and operation of marine vessels. In this brief perspective, we will outline a few steps to feeder losses analysis based on the result conducted by the load survey. The power flow and feeder loss analysis are extracted and used to determine the critical parameters that can significantly affect the system feeder losses used in the electrical load analysis in new ships. Exploring this new research direction will provide a more thorough understanding of feeder losses in marine vessel power systems. In this paper, a case study of container ship power loss analysis using the Newton–Raphson method is presented. The analysis results can provide shipbuilding corporations and ship owners with useful information for planning, designing, operating, and controlling shipboard power systems. As an energy-saving measure for ship microgrids, the frequency converters are widely used by shipyards for seawater and freshwater cooling systems and heating, ventilation, and air conditioning (HVAC) systems, so that these systems can adjust the speed of the motor according to the actual demand of the load, so as to avoid full-load operation during the motor operation. With the proposed method, other measures, such as battery energy storage systems and energy-saving lighting equipment based on LEDs, are also utilized for shipboard power demand management. [ABSTRACT FROM AUTHOR]

In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship's energy efficiency indexes appropriately. The algorithms' prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively. [ABSTRACT FROM AUTHOR]