A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship

Abstract

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

Keywords

• smart ship
• energy management
• prediction of power
• long short-term memory models
• bidirectional long short-term memory models