Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

Abstract

The International Maritime Organization (IMO) had made effort to reduce the ship’s energy consumption and carbon emission by optimizing the ship’s operational measures such as speed and weather routing. However, existing fuel consumption models were relatively simple without considering the quantified effect of weather conditions. In this paper, a knowledge-based ridge regression-based algorithm is presented for enabling automated fuel consumption estimation under varying weather conditions during voyages. Wind speed, wave height, ship speed, draught, AIS segment distance, and ship’s heading (HDG) are used as input to predict the fuel consumption value from the MRV report. In this work, 3 types of models are tested: AIS-based model, MRV-based model, and MRV-based normalized model. In AIS based model, weather conditions are divided into nine categories based on wind speed, wave height, and wind directions then trained separately. In MRV-based mode, the daily weather condition was used, and the MRV-normalized model used the normalized daily weather data. The proposed ridge regression models (11 models total) were tested with 4 container ships for a period of one year, and the result shows that compared to real fuel consumption, MRV-based model could achieve the best result with an average error less than 3% comparing to real MRV report.

Ship emission projections based on time series forecasting model for sustainable shipping in the strait of Malacca and Singapore

“…As such, numerical methods were employed to predict ship power in order to discover the substantial demand for FOC under different navigational conditions by means of data-driven machine learning methods [18]. Studies by Ren et al [19] also reveal the consistency of utilizing AIS data for FOC estimation in way of predicting ship emissions, compared to direct reference on daily fuel oil measurement data. Similarly, Kaklis et al [4] also demonstrated FOC estimation by a deep-learning model (SplineLSTM) to reflect the associated emissions of a ship.…”

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

“…And the results showed that ANN has a higher prediction accuracy than the polynomial regression and support vector machine in fuel consumption prediction [6]. Ren compared the prediction results under different data sources using a ridge regression model based on AIS data, MRV data, and MRV-normalized data, respectively, and found that the model based on MRV report achieved the best results [7]. Li investigated the results of various prediction models with different combinations of data sources based on a variety of data sources, such as logbooks, meteorological data, and AIS data [8][9][10].…”

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].…”