An ensemble learning model for ship fuel consumption prediction

Jialu Li; Yuanyuan Xu

doi:10.1117/12.3031143

19 July 2024 An ensemble learning model for ship fuel consumption prediction

Jialu Li, Yuanyuan Xu

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318182 (2024) https://doi.org/10.1117/12.3031143
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In order to fulfill the responsibilities of international Maritime environmental protection and respond to the national "dual carbon" strategic policy, as well as to cope with the rising fuel prices, the relevant research on ship energy saving and emission reduction technologies has gradually received the attention of the International Maritime Organization. The focus of relevant organizations such as IMO, national maritime authorities and shipowners. In the context of shipping data, the effective use of ship energy consumption monitoring data to accurately predict ship operation energy consumption is becoming more and more important to achieve shipping energy conservation and emission reduction, and also an effective means to respond to national strategies. From the perspective of energy saving, it relies on the pycharm platform, analyzes and preprocesses multiple types of open source data collected during the actual voyage of a passenger ship, establishes the system input features, and the ship fuel consumption prediction model based on the Stacking model. The prediction results are compared with those single model. The advantage of the integrated learning model of Stacking is proved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jialu Li and Yuanyuan Xu "An ensemble learning model for ship fuel consumption prediction", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318182 (19 July 2024); https://doi.org/10.1117/12.3031143

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