Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption

Accurately forecasting the fuel consumption of ships is critical for improving their energy efficiency. However, the environmental factors that affect ship fuel consumption have not been researched comprehensively, and most of the relevant studies continue to present efficiency and accuracy issues. In view of such problems, a time-series forecasting model of ship fuel consumption based on a novel regression analysis using broad learning system (BLS) was developed in this study. The BLS was compared to a diverse set of fuel consumption forecasting models based on time-series analyses and machine learning techniques, including autoregressive integrated moving average model with exogenous inputs (ARIMAX), support vector regression (SVR), recurrent neural network (RNN), long short-term memory network (LSTM), and extreme learning machines (ELM). In the experiment, two types of passenger roll-on roll-off (ro-ro) ship and liquefied petroleum gas (LPG) carrierwere used as research objects to verify the proposed method’s generalizability, with data divided among two groups (RM, RB). The experimental results showed that the BLS model is the best choice to forecast fuel consumption in actual navigation, with mean absolute error (MAE) values of 0.0140 and 0.0115 on RM and RB, respectively. For the LPG carrier, it has also been proven that the forecast effect is improved when factoring the sea condition, with MAE reaching 0.0108 and 0.0142 under ballast and laden conditions, respectively. Furthermore, the BLS features the advantages of low computing complexity and short forecast time, making it more suitable for real-world applications. The results of this study can therefore effectively improve the energy efficiency of ships by reducing operating costs and emissions.