Assessing Ships' Environmental Performance Using Machine Learning

被引：2

作者：

Skarlatos, Kyriakos ^{[1
]}

Fousteris, Andreas ^{[1
]}

Georgakellos, Dimitrios ^{[1
]}

Economou, Polychronis ^{[2
]}

Bersimis, Sotirios ^{[1
]}

机构：

[1] Univ Piraeus, Dept Business Adm, Piraeus 18534, Greece

[2] Univ Patras, Dept Civil Engn, Patras 26504, Greece

来源：

ENERGIES | 2023年 / 16卷 / 06期

关键词：

ship's environmental performance; machine learning in shipping; data-driven environmental indices; shipping environmental categorization; ENERGY EFFICIENCY; FUEL CONSUMPTION; SPEED OPTIMIZATION; CARBON EMISSIONS; CONTROL CHARTS; PREDICTION; POWER;

D O I：

10.3390/en16062544

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Environmental performance of ships is a critical factor in the shipping industry due to evolving climate change and the respective regulations imposed by authorities all over the world. As shipping moves towards digitization, a large amount of ships' environmental performance-related data, collected during ships' voyages, provide opportunities to develop and enhance data-driven performance models by using different machine learning algorithms. This paper introduces new indices of ships' environmental performance using machine learning techniques. The new indices are produced by combining clustering algorithms as well as principal component analysis. Based on the analysis of the data (14 variables with operational and design characteristics), the ships are divided into four clusters based on the new suggested indices. These clusters categorize the ships according to their physical dimensions, operating region, and operational environmental efficiency, offering insight into the distinctive traits of each cluster.

引用

页数：21

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