Investigation of ship energy consumption based on neural network

被引：0
|
作者：
Shu, Yaqing [1 ,2 ]
Yu, Benshuang [2 ]
Liu, Wei [3 ]
Yan, Tao [4 ]
Liu, Zhiyao [2 ]
Gan, Langxiong [1 ,2 ]
Yin, Jianchuan [5 ]
Song, Lan [6 ]
机构：
[1] Wuhan Univ Technol, State Key Lab Maritime Technol & Safety, Wuhan 430063, Peoples R China
[2] Wuhan Univ Technol, Sch Nav, Wuhan 430063, Peoples R China
[3] CCCC Guangzhou Dredging Co Ltd, Guangzhou 510220, Peoples R China
[4] Minist Transport, Tianjin Res Inst Water Transport Engn, Tianjin 300456, Peoples R China
[5] Guangdong Ocean Univ, Maritime Coll, Guangzhou 510062, Peoples R China
[6] Eastern Inst Technol, Coll Engn, Ningbo 315199, Peoples R China
来源：
OCEAN & COASTAL MANAGEMENT | 2024年 / 254卷
关键词：
BP neural networks; Levenberg-marquardt algorithm; Spearman analysis; Regression analysis; Ship energy consumption model; FUEL CONSUMPTION; WASTE;
D O I：
10.1016/j.ocecoaman.2024.107167
中图分类号：
P7 [海洋学];
学科分类号：
0707 ;
摘要：
Due to fuel price fluctuations and greenhouse gas emissions caused by international shipping, the reduction of ship fuel consumption is important. In this paper, a ship energy consumption prediction model is established based on the BP neural network optimized by the Levenberg-Marquardt (LM) algorithm to predict ship energy consumption. Firstly, the ship navigation data are analyzed. Ship speed, draught and current speed are selected as the main factors affecting ship energy consumption and their weights are determined by the spearman and regression analysis. Then, the ship energy consumption model is established based on the LM-BP algorithm. Finally, ship speed, draught and current velocity are used as the input and ship energy consumption is considered as output for the model to validate the model. The predicted results is compared with the real energy comsumption and the final RMSE is approximately 259.74 kW. For ships sailing with power ranging from 6000 kW to 8500 kW, the model prediction error is 3.06 %. The results of this study could be used to optimize ship operational plans to improve the economics of waterway transport.
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页数：11
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