Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

Abstract

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.

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

“…The actual power reading depends on various uncertain factors, such as dynamic consumer behaviour, ship length, carriage capacity, voyage duration, and speed of steaming. This understanding is supported by another recent publication [13] that considers another set of inputs for power prediction, such as latitude, roll angle, net propulsion power, and speed. This scenario justifies considering a diversity of inputs that may be involved in the power usage of a ship.…”

“…However, this study contains some innovative aspects. Classical processes such as fuel consumption estimation were also carried out in the mentioned study [13]. These methods are useful for the maritime field problems.…”

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

“…Uyanık, T. et al [30,31] proposed a fuel consumption estimation model using an artificial intelligence model by collecting information from measurement data on the ship's main engine, noon report, and engine logbook. Tarelko, W. and Rudzki, K. [32] developed a model for predicting a ship's fuel consumption using an artificial neural network algorithm.…”

Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors

“…Various machine learning algorithms that have been utilized in the attempt to develop the FOC prediction model include linear regression [20], multiple linear regression [21], ridge regression [22], support vector regressor [23], lasso regression [24], K-nearest neighbor regressor [25], extra tree regressor [26], random forest regressor [27], Gaussian process metamodel [28], artificial neural network (ANN) approach [29], and even deep learning [30]. Traditional methods, such as statistical analysis, have initially been used to examine historical consumption patterns, identify key factors influencing consumption, and develop predictive models, but these methods have been found to have low accuracy [31].…”