A comprehensive review on the prediction of ship energy consumption and pollution gas emissions

Wang, Kai; Wang, Jianhang; Huang, Lianzhong; Yuan, Yupeng; Wu, Guitao; Xing, Hui; Wang, Zhongyi; Wang, Zhuang; Jiang, Xiaoli

doi:10.1016/j.oceaneng.2022.112826

Cited by 36 publications

(9 citation statements)

References 164 publications

(147 reference statements)

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“…The introduction of EEOI and EEDI has sparked significant research interest in evaluating and enhancing the energy efficiency of ships [22,23]. Interested readers seeking more comprehensive information on these methods can refer to the works of Wang et al [24] and Duan et al [25]. These references provide valuable insights and further details on the evaluation and improvement of ship energy efficiency.…”

Section: Energy Efficiencymentioning

confidence: 99%

Assessing the Potential for Energy Efficiency Improvement through Cold Ironing: A Monte Carlo Analysis with Real Port Data

Tang,

Jiang,

et al. 2023

JMSE

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Ports in China are facing significant pressure to reduce carbon emissions in alignment with carbon peak and carbon neutrality goals. Onshore power supply (OPS) is regarded as a promising approach to accomplish these targets, necessitating a thorough evaluation of its impact for port authorities to make informed decisions regarding its adoption. This research focuses on Ningbo Zhoushan Port, the largest port globally, as a case study. Two metrics are proposed to quantify the energy efficiency of ships powered by onshore energy while berthed. The installation and connection status of OPS in the port area are analyzed. Subsequently, the energy demand of berthed ships is assessed, and the potential for energy efficiency improvement resulting from OPS implementation is evaluated using Monte Carlo methods. The findings reveal untapped potential in the studied port area, with OPS demonstrating the ability to improve energy efficiency of berthed ships at a rate parallel to the connection rate, excluding indirect emissions. However, considering indirect emissions and energy loss diminishes the effectiveness of OPS. The paper discusses practical implications for enhancing the energy efficiency of OPS, enabling port authorities to make well-informed decisions. These findings are invaluable for Chinese port authorities striving to achieve carbon reduction goals and enhance sustainability in the maritime industry.

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Section: Energy Efficiencymentioning

confidence: 99%

Assessing the Potential for Energy Efficiency Improvement through Cold Ironing: A Monte Carlo Analysis with Real Port Data

Tang,

Jiang,

et al. 2023

JMSE

Self Cite

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“…Machine learning algorithms have been applied to ship energy consumption management to optimize the energy efficiency of ships and reduce energy consumption. Wang integrated the data of the ship's primary sensors and used several machine learning algorithms to analyze them, finding that they could realize intelligent prediction of ship energy consumption [5]. BE ŞIKÇI developed a ship operation decision support system using artificial neural networks to output ship fuel consumption and help decision makers plan energy usage rationally [6].…”

Section: Smart Shipsmentioning

confidence: 99%

Digital Intelligence Technology Drives Sustainable Change in Shipping: Concepts, Applications, and Prospects

2023

Advances in Economics, Business and Management Research

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Shipping sustainability refers to the long-term adaptive development of the shipping industry, which involves a series of industries related to the shipping industry. Achieving sustainable development is an essential issue in the modern shipping field. At the same time, the world is undergoing digitalization. From the perspective of successful cases in other industries, the sustainability of shipping is inseparable from the support of digital intelligence. There are already many theories about digital intelligence in shipping at home and abroad, but there need to be more practical results. This article uses the review method to define the sustainability of shipping from the perspective of ships and ports. Combing the pieces in the five aspects of shipping lanes, services, and supervision and analyzing how digital and intelligent technologies can ensure the sustainable development of shipping and internal connections. This paper strengthens the relationship between studies and provides theoretical support for the digital transformation of the shipping industry to achieve sustainable development.

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“…The BBMs entirely rely on data analysis by processing multi-dimensional data and extracting hidden information from complex dataset. Then BBMs can output a reliable basis of ship's energy performance [30]. One commonly used machine learning technique for the prediction of fuel used is the regression models such as linear regression, ridge regression and lasso regression [28] [31], however, it is found that one single regression model may not be sufficient for representing the entire fuel consumptions system thus multi regressions are needed in order to evaluate the whole relationship.…”

Section: Introductionmentioning

confidence: 99%

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Wang¹,

Wang²,

Han³

et al. 2023

JSEA

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Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship's energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship's fuel consumption and carbon emissions under varying weather conditions.

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A comprehensive review on the prediction of ship energy consumption and pollution gas emissions

Cited by 36 publications

References 164 publications

Assessing the Potential for Energy Efficiency Improvement through Cold Ironing: A Monte Carlo Analysis with Real Port Data

Assessing the Potential for Energy Efficiency Improvement through Cold Ironing: A Monte Carlo Analysis with Real Port Data

Digital Intelligence Technology Drives Sustainable Change in Shipping: Concepts, Applications, and Prospects

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

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