一种利用船舶在役数据进行功率预测的新方法——以某普通货船为例

IF 1.4 Q3 ENGINEERING, MARINE
Ehsan Esmailian, Young-Rong Kim, Sverre Steen, Kourosh Koushan
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引用次数: 0

摘要

为了提高航运业的能源效率和减少温室气体(GHG)排放,准确预测船舶在海上的性能至关重要。本文提出了一种基于最小化归一化均方根误差(NRMSE)的功率预测方法,该方法通过将功率预测模型的结果与给定船舶的在役数据进行比较来定义。结果是一个功率预测模型,该模型经过调整以适应应用在役数据的船舶。一艘普通货船被用作测试案例。用人工神经网络(ANN)方法和传统的功率预测模型对该方法在不同场景下的性能进行了评估。在所有研究场景中,所提出的方法都显示出较好的船舶功率预测效果。最佳和最差功率预测模型的nrmse之间也有高达86%的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new power prediction method using ship in-service data: a case study on a general cargo ship
To increase energy efficiency and reduce greenhouse gas (GHG) emissions in the shipping industry, an accurate prediction of the ship performance at sea is crucial. This paper proposes a new power prediction method based on minimizing a normalized root mean square error (NRMSE) defined by comparing the results of the power prediction model with the ship in-service data for a given vessel. The result is a power prediction model tuned to fit the ship for which in-service data was applied. A general cargo ship is used as a test case. The performance of the proposed approach is evaluated in different scenarios with the artificial neural network (ANN) method and the traditional power prediction models. In all studied scenarios, the proposed method shows better performance in predicting ship power. Up to 86% percentage difference between the NRMSEs of the best and worst power prediction models is also reported.
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来源期刊
Ship Technology Research
Ship Technology Research ENGINEERING, MARINE-
CiteScore
4.90
自引率
4.50%
发文量
10
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