Resumen
In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid development of sensor technologies enables multisource data collection to improve the modeling of the SFC problem. To address the features of such heterogeneous data, this paper proposes an integrated model for the estimation of SFC that includes three modules: a multisource data collection module, a heterogeneous data feature fusion module and a fuel consumption estimation module. First, in the data collection module, data related to SFC are collected by multiple sensors installed aboard the ship. Second, the feature fusion module employs a series of moving overlapped frames to merge different frequency data into small frames so that fusion features can be extracted from the heterogeneous data of multiple sources. Finally, in the fuel estimation module, the fusion features provide a novel way to consider the modeling and estimation of SFC as a classical time-series analysis using various machine learning techniques. Experimentally, linear regression (LR), support vector regression (SVR), and artificial neural network (ANN) were employed as the machine learning methods to train SFC models. Compared with the traditional feature extraction method, the accuracy of LR, SVR, and ANN were improved by 8.5, 0.35 and 51.5%, respectively, using the proposed method. The main contribution of this work is to consider the multisource and heterogeneous problem of sensor-based SFC data and propose an integrated model to extract the information of SFC data. Moreover, the experimental results showed that the estimation accuracy can be greatly improved.