Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors
Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors
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考虑航行状况和环境因素的内河船舶燃油消耗拟合分析
DOI：
10.1109/access.2020.3030614
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Zongzhi Li
中科院分区：
文献类型：
--
作者：
Zhi Yuan;Jingxian Liu;Yi Liu;Yuan Yuan;Qian Zhang;Zongzhi Li
中国生态优先、绿色发展战略，让内河船舶油耗受到前所未有的关注。可靠的油耗预测是航海规划、能源监管、效率优化的重要依据。本文以长江干线上航行的一艘货轮为研究对象。对内河船舶油耗进行了综合拟合分析，提出了内河船舶油耗的预测方法。首先，利用多源传感器采集船舶航行状态和环境信息等多源数据。其次，为了对采集到的数据进行详细的分析，提出了数据预处理和轨迹分割的方法，并分析了多源变量与油耗的相关性。第三，采用双隐层反向传播神经网络(DBPNN)建立油耗预测模型。第四，利用实船测量数据对所建立的模型进行了验证。选择不同的输入变量进行油耗预测，结果表明，加入水位、水速、风速、风角和航段等环境特征变量后，预测误差均方根和平均绝对误差分别降低35.31%和30.30%，而R^{2}$(R-平方)增加到0.9843。与Elman、RBF(径向基函数)、三种支持向量回归(SVR)模型、随机森林回归(RFR)模型、广义回归神经网络(GRNN)、递归神经网络(RNN)、门控递归单元(GRU)和长短期记忆(LSTM)等人工神经网络模型相比，DBPNN模型具有更好的预测效果。
The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this article, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the $R^{2}$ (R-squared) increased to 0.9843. What’s more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.
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