It is important to reduce \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$CO_{2}$$\end{document} emissions. Accurate power prediction of vessels allows evaluation of performance which is essential for this. Neural networks have been shown to accurately predict powering of vessels in weather, which is not straightforward using traditional methods. In previous applications only 6 intuitively relevant variables are used to predict powering with neural networks, including wave height, which is expensive and time consuming to collect. This study investigates the influence of the wave height variable on prediction, concluding that it increases accuracy of prediction by 0.5%. A high frequency dataset from a merchant vessel containing 100 variables is statistically analysed and a maximal subset of variables is derived. This subset is used for principal component analysis, focussing on redundancy. Investigation into the inclusion of non-intuitively relevant variables to increase prediction accuracy for power prediction with a neural network is performed.


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    Titel :

    Efficient Vessel Power Prediction in Operational Conditions Using Machine Learning


    Weitere Titelangaben:

    Lecture Notes in Civil Engineering


    Beteiligte:
    Okada, Tetsuo (Herausgeber:in) / Suzuki, Katsuyuki (Herausgeber:in) / Kawamura, Yasumi (Herausgeber:in) / Parkes, A. I. (Autor:in) / Savasta, T. D. (Autor:in) / Sobey, A. J. (Autor:in) / Hudson, D. A. (Autor:in)

    Kongress:

    Practical Design of Ships and Other Floating Structures ; 2019 ; Yokohama, Japan September 22, 2019 - September 26, 2019



    Erscheinungsdatum :

    2020-10-06


    Format / Umfang :

    18 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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