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

Li, Xinyu; Zuo, Yi; Jiang, Junhao

doi:10.3390/su15010380

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…In this paper, it is assumed that a ship is in sailing condition when its speed is more than 3 knots. Therefore, in this study, only data with speed greater than or equal to 3 knots are considered [24]. The total number of processed data is 4061, and some data samples are shown in Table 8.…”

Section: Calculation Of Cumulative Carbon Intensity Of Shipsmentioning

confidence: 99%

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Zhang,

Lu,

Wang

et al. 2023

JMSE

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The Carbon Intensity Index (CII) exerts a substantial impact on the operations and valuation of international shipping vessels. Accurately predicting the CII of ships could help ship operators dynamically evaluate the possible CII grate of a ship at the end of the year and choose appropriate methods to improve its CII grade to meet the IMO requirement with minimum cost. This study developed and compared five CII predicting models with multiple data sources. It integrates diverse data sources, including Automatic Identification System (AIS) data, sensor data, meteorological data, and sea state data from 2022, and extracts 21 relevant features for the vessel CII prediction. Five machine learning methods, including Artificial Neural Network (ANN), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are employed to construct the CII prediction model, which is then applied to a 2400 TEU container ship. Features such as the mean period of total swell, mean period of wind waves, and seawater temperature were considered for inclusion as inputs in the model. The results reveal significant correlations between cumulative carbon emissions intensity and features like cumulative distance, seawater temperature, wave period, and swell period. Among these, the strongest correlations are observed with cumulative distance and seawater temperature, having correlation coefficients of 0.45 and 0.34, respectively. Notably, the ANN model demonstrates the highest accuracy in CII prediction, with an average absolute error of 0.0336, whereas the LASSO model exhibits the highest error of 0.2817. Similarly, the ANN model provides more accurate annual CII ratings for the vessel. Consequently, the ANN model proves to be the most suitable choice for cumulative CII prediction.

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Section: Calculation Of Cumulative Carbon Intensity Of Shipsmentioning

confidence: 99%

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Zhang,

Lu,

Wang

et al. 2023

JMSE

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“…This is a transmission and communication technology that enables a ship to transmit AIS information to other ships. This information includes the ship identity, location, speed, and course; that is, the ship navigation behavior and status [2][3][4]. Based on these data, ships can effectively avoid collisions with other ships.…”

Section: Introductionmentioning

confidence: 99%

Application of an Encoder–Decoder Model with Attention Mechanism for Trajectory Prediction Based on AIS Data: Case Studies from the Yangtze River of China and the Eastern Coast of the U.S

Zhao,

Zuo,

et al. 2023

JMSE

Self Cite

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With the rapid growth of shipping volumes, ship navigation and path planning have attracted increased attention. To design navigation routes and avoid ship collisions, accurate ship trajectory prediction based on automatic identification system data is required. Therefore, this study developed an encoder–decoder learning model for ship trajectory prediction, to avoid ship collisions. The proposed model includes long short-term memory units and an attention mechanism. Long short-term memory can extract relationships between the historical trajectory of a ship and the current state of encountered ships. Simultaneously, the global attention mechanism in the proposed model can identify interactions between the output and input trajectory sequences, and a multi-head self-attention mechanism in the proposed model is used to learn the feature fusion representation between the input trajectory sequences. Six case studies of trajectory prediction for ship collision avoidance from the Yangtze River of China and the eastern coast of the U.S. were investigated and compared. The results showed that the average mean absolute errors of our model were much lower than those of the classical neural networks and other state-of-the-art models that included attention mechanisms.

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Towards Alternative Energy Ship’s Design: Sensors’ Energy Efficiency in Switchgears

Vujović,

Kuzmanić,

Kaštelan

et al. 2024

Engineering Design Applications VI

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

Cited by 10 publications

References 29 publications

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Research on Carbon Intensity Prediction Method for Ships Based on Sensors and Meteorological Data

Application of an Encoder–Decoder Model with Attention Mechanism for Trajectory Prediction Based on AIS Data: Case Studies from the Yangtze River of China and the Eastern Coast of the U.S

Towards Alternative Energy Ship’s Design: Sensors’ Energy Efficiency in Switchgears

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