Comprehensive evaluation of machine learning models for predicting ship energy consumption based on onboard sensor data

• Published in: Ocean & coastal management 2024-02, Vol.248, p.106946, Article 106946
• Main Authors: Fan, Ailong, Wang, Yingqi, Yang, Liu, Tu, Xiaolong, Yang, Jian, Shu, Yaqing
• Format: Article
• Language: English
• Subjects: Machine learning; Onboard sensor data; Performance evaluation; Prediction model; Ship energy consumption
• ISSN: 0964-5691; 1873-524X
• DOI: 10.1016/j.ocecoaman.2023.106946

Machine learning models for predicting ship energy consumption are built and their influencing factors are investigated. First, data collected from a real ship is preprocessed. Six machine learning methods are used to establish the prediction models of ship fuel consumption, and the performance of models is evaluated by Mean Absolute Error, Coefficient of Determination and training time. Then, by analysing the correlation and importance of the features, it's studied whether the model established complies with the laws of physics. Finally, the factors affecting the prediction performance of machine learning models are analysed. The results show that Random Forest and Extreme Gradient Boosting are the most suitable algorithms for ship fuel consumption prediction. Data preprocessing, data normalisation, training sample size, model type, ship operating conditions, as well as the thermotechnical parameters of main engine have impact on the prediction performance. In particular, when taking the thermotechnical parameters into consideration, R2 is increased by 0.32%, MAE is reduced by 5.0%. •Shipboard sensor data are used to build machine learning models for fuel consumption.•Performance of machine learning algorithms are evaluated by MAE, R2 and training time.•Impact of data preprocessing, sample size, model type, operating condition, feature type is quantitatively investigated.•Main engine thermotechnical features contribute to predicting ship energy consumption.