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Πρόβλεψη κατανάλωσης καυσίμου πλοίων με τεχνικές μηχανικής μάθησης

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dc.contributor.author Αλεξιάς, Παύλος el
dc.contributor.author Alexias, Pavlos en
dc.date.accessioned 2022-11-02T07:04:07Z
dc.date.available 2022-11-02T07:04:07Z
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/56056
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.23754
dc.rights Default License
dc.subject Τεχνητή Νοημοσύνη el
dc.subject Ναυτιλία el
dc.subject Γραμμική Παλινδρόμηση el
dc.subject Νευρωνικά Δίκτυα el
dc.subject Πολυωνυμική Παλινδρόμηση el
dc.subject Artificial Intelligence en
dc.subject Maritime en
dc.subject Linear Regression en
dc.subject Polynomial Regression en
dc.subject Neural Network en
dc.title Πρόβλεψη κατανάλωσης καυσίμου πλοίων με τεχνικές μηχανικής μάθησης el
heal.type bachelorThesis
heal.classification Μηχανική Μάθηση el
heal.language el
heal.access campus
heal.recordProvider ntua el
heal.publicationDate 2022-07-14
heal.abstract The goal of this thesis is to calculate the fuel needs of a vessel by using machine learning algorithms. More specifically, by using linear regression as well as polynomial regression, in order to accurately predict the dependent variable of fuel consumption was the main goal of this thesis. As independent variables other metrics were considered, such as the deadweight of the vessel and the power of the vessel's machine. Lastly, in order to avoid the linearity that the above algorithms assume, a neural network was created in order to cross check the results. The inputs of the neural network are the same as the independent variables of the machine learning algorithms. Finally, a Graphical User Interface was created, in order for the user to easily and quickly interact with the models. The user, through the use of simple components, can insert the specifications of the vessel that he is interested in. As an output, the user sees the prediction of fuel consumption - both from the linear and the neural network solutions - as well as graphs containing information with vessels similar to the one the user inserted. en
heal.advisorName Ασκούνης, Δημήτριος el
heal.committeeMemberName Ασκούνης, Δημήτριος el
heal.committeeMemberName Ψαράς, Ιωάννης el
heal.committeeMemberName Δούκας, Χρυσόστομος el
heal.academicPublisher Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Ηλεκτρικών Βιομηχανικών Διατάξεων και Συστημάτων Αποφάσεων el
heal.academicPublisherID ntua
heal.numberOfPages 58 σ. el
heal.fullTextAvailability false


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