摘要
船舶主机功率是预测航行油耗、评估船舶废气排放中的一项重要数据。然而,未知的船舶主机功率数据对基于大数据的船舶油耗及排放预测产生了障碍。为了解决这一问题,本文提出基于高斯混合模型(GMM)和深度神经网络(DNN)的大型船舶主机功率预测方法。首先对船舶特征进行相关性分析,选择与主机功率相关系数较大的船舶特征作为GMM-DNN混合模型的输入,然后使用GMM对船舶特征进行聚类,将聚类结果作为标签和船舶特征一起作为DNN的输入,最后使用Adam-Dropout优化DNN,并用DNN对船舶功率进行预测。为了探究方法的有效性,本文比较了多元线性回归分析、非线性回归、DNN、GMM-DNN在船舶主机功率上的预测效果。实验表明,GMM-DNN模型在船舶主机功率预测上表现最好,其平均绝对误差MAPE为14.57%,比多元线性回归、非线性回归和DNN分别低28.27%、23.36%和1.24%。
The marine main engine power is an important data for prediction of fuel consumption during navigation and for evaluation of exhaust emissions. However, the absence of vessel main engine power data hinders the prediction of fuel consumption and emissions of vessels based on large data. A main engine power prediction method for large vessels based on Gaussian mixture model(GMM) and deep neural network(DNN) is proposed in this paper to solve this problem. Firstly, the vessel data are analysed for correlation, and ship features which have large correlation coefficients with the engine power are selected as input of the GMM-DNN hybrid model. Then the clustering algorithm GMM is used to analyse ship characteristics, the result of which is used as an input of DNN. Finally, with DNN optimized by Adam-Dropout, the vessel power is predicted by DNN. In order to explore the effectiveness of the method, the multiple linear regression analysis, nonlinear regression, DNN and GMMDNN with respect to the main engine power prediction of vessels are compared. The experiment shows that the MAPE of GMM-DNN is 14.57%, which is 28.27% lower than that of multiple linear regression, 23.36% lower than that of non-linear regression, and 1.24% lower than that of DNN and shows that the GMM-DNN model is the best in the main engine power prediction for large vessels.
作者
张嘉琦
苏伟
张久文
吴尽昭
蔡川
郭弋平
雷晖
ZHANG Jia-qi;SU Wei;ZHANG Jiu-wen;WU Jin-zhao;CAI Chuan;GUO Yi-ping;LEI Hui(School of Information Science&Engineering,Lanzhou University,Lanzhou 730000,China;College of Electronic Information,Guangxi University for Nationalities,Nanning 530006,China)
出处
《船舶力学》
EI
CSCD
北大核心
2021年第12期1623-1634,共12页
Journal of Ship Mechanics
基金
国家自然科学基金项目(61772006)
广西科技项目(桂科AA17204096
桂科AB17129012
桂科AD16380076)
广西“八桂学者”专项资助。
