Interval forecasting of photovoltaic power generation on green ship under Multi-factors coupling

Lan, Hai; Gao, Jingjie; Hong, Ying-Yi; Yin, He

doi:10.1016/j.seta.2023.103088

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Extreme learning machines (ELM), kernel density estimation (KDE), and K-mean clustering ELM could provide at least 5.6% more precise predictions [104] Machine learning-based prediction of ship's performance Principal component regression, Partial least square regression, probabilistic ANN, and ANN…”

Section: Objectives ML / Ai Used Main Outcomes Sourcementioning

confidence: 99%

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

Nguyen,

Tran

et al. 2024

JOIV : Int. J. Inform. Visualization

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This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase capability. Throughout this work, the importance of the role that artificial intelligence plays in driving efficiency, safety, and sustainability is emphasized. This work also highlights the urgent need for action to address climate change and environmental degradation in the marine sector. The marine industry can lessen its carbon footprint, decrease pollution, and improve ecosystem health if it shifts to various alternative fuels, renewable energy sources, and technologies powered by artificial intelligence. At the end of this work, an appeal is made to policymakers, industry stakeholders, and technology providers, urging them to prioritize investments in artificial intelligence research and development and to create collaboration to speed up the transition to a marine sector that is more sustainable and resilient.

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Section: Objectives ML / Ai Used Main Outcomes Sourcementioning

confidence: 99%

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

Nguyen,

Tran

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Thus, for a comprehensive study on the control of electric-powered ships, it is essential to study the power load prediction model of the ship, which changes according to sea conditions. Previous research on power prediction investigated various power prediction models, centering on urban power load prediction [20], power load prediction for a specific country [21], solar power generation prediction [22], and building power demand prediction [23]. However, unlike the power load data from the previous research, the power load data of current vessels are characterized by rapid variations in response to changes in the vessel's operational status and external environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Multiple Feature Extraction Long Short-Term Memory Using Skip Connections for Ship Electricity Forecasting

Kim,

2023

JMSE

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The power load data of electric-powered ships vary with the ships’ operational status and external environmental factors such as sea conditions. Therefore, a model is required to accurately predict a ship’s power load, which depends on changes in the marine environment, weather environment, and the ship’s situation. This study used the power data of an actual ship to predict the power load of the ship. The research on forecasting a ship’s power load fluctuations has been quite limited, and the existing models have inherent limitations in predicting these fluctuations accurately. In this paper, A multiple feature extraction (MFE)-long short-term memory (LSTM) model with skip connections is introduced to address the limitations of existing deep learning models. This novel approach enables the analysis and forecasting of the intricate load variations in ships, thereby facilitating the prediction of complex load fluctuations. The performance of the model was compared with that of a previous convolutional neural network-LSTM network with a squeeze and excitation (SE) model and deep feed-forward (DFF) model. The metrics used for comparison were the mean absolute error, root mean squared error, mean absolute percentage error, and R-squared, wherein the best, average, and worst performances were evaluated for both models. The proposed model exhibited a superior predictive performance for the ship’s power load compared to that of existing models, as evidenced by the performance metrics: mean absolute error (MAE) of 55.52, root mean squared error of (RMSE) 125.62, mean absolute percentage error (MAPE) of 3.56, and R-squared (R2) of 0.86. Therefore, the proposed model is expected to be used for power load prediction during electric-powered ship operations.

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Interval forecasting of photovoltaic power generation on green ship under Multi-factors coupling

Cited by 2 publications

References 40 publications

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

Multiple Feature Extraction Long Short-Term Memory Using Skip Connections for Ship Electricity Forecasting

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