Ship Energy Consumption Prediction with Gaussian Process Metamodel
Ship Energy Consumption Prediction with Gaussian Process Metamodel
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DOI：
10.1016/j.egypro.2018.09.226
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Jun Yuan;V. Nian
中科院分区：
文献类型：
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作者：
Jun Yuan;V. Nian
航运是全球二氧化碳排放的主要贡献者。提高船舶能效、减少船舶排放具有重要意义。为了减少船舶排放，人们提出了各种减排措施。为了评估这些减排措施的有效性，需要评估这些措施的燃料消耗减少量。然而，由于船舶能源系统的复杂性，很难通过物理系统或仿真模型来评估船舶在考虑操作和天气条件的不同场景下的燃油消耗。在本文中，开发了高斯过程元模型来预测不同场景下的船舶燃油消耗。该模型不仅考虑了速度、纵倾等运行条件的影响，还考虑了风浪效应等天气条件的影响。进一步评估这些因素对船舶燃油消耗的影响。案例研究表明使用高斯过程元模型进行船舶能耗预测的准确性和效率。
Shipping is a major contributor to the global CO2emissions. It is important to improve the ship energy efficiency and reduce the ship emissions. Various emission reduction measures have been proposed to reduce the ship emissions. In order to evaluate the efficiency of these emission reduction measures, it is necessary to evaluate the reduction of fuel consumption for these measures. However, due to the complex of ship energy system, it is difficult to assess the ship fuel consumption under different scenarios considering both operational and weather conditions through physical systems or simulation models. In this paper, a Gaussian process metamodel is developed to predict the ship fuel consumption for different scenarios. This model not only considers the effects of operational conditions such as speed and trim, but also takes into account the impacts of weather conditions such as wind and wave effects. The effects of these factors on ship fuel consumption are further evaluated. The case study indicates the accuracy and efficiency of using Gaussian process metamodel for ship energy consumption prediction.