Prediction of ship fuel consumption by using an artificial neural network

Abstract

A smart ship collects various data with large volume, such as voyage, machinery, and weather data. Thus, big data analysis for smart ships is an important technology that can be widely applied to improve ship maintenance, operational efficiency, and equipment life management. In this study, an accurate regression model for the fuel consumption of the main engine by using an artificial neural network (ANN) was proposed by big data analysis including data collection, clustering, compression, and expansion. To obtain an accurate regression model, various numbers of hidden layers and neurons and different types of activation functions were tested in the ANN, and their effects on the accuracy and efficiency of the regression analysis were studied. The proposed regression model using ANN is a more accurate and efficient model to predict the fuel consumption of the main engine than polynomial regression and support vector machine.