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Predicting Seagoing Ship Energy Efficiency from the Operational Data

This paper presents the application of machine learning (ML) methods in setting up a model with the aim of predicting the energy efficiency of seagoing ships in the case of a vessel for the transport of liquefied petroleum gas (LPG). The ML algorithm is learned from shipboard automation system measu...

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Autores principales: Vorkapić, Aleksandar, Radonja, Radoslav, Martinčić-Ipšić, Sanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073942/
https://www.ncbi.nlm.nih.gov/pubmed/33920530
http://dx.doi.org/10.3390/s21082832
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author Vorkapić, Aleksandar
Radonja, Radoslav
Martinčić-Ipšić, Sanda
author_facet Vorkapić, Aleksandar
Radonja, Radoslav
Martinčić-Ipšić, Sanda
author_sort Vorkapić, Aleksandar
collection PubMed
description This paper presents the application of machine learning (ML) methods in setting up a model with the aim of predicting the energy efficiency of seagoing ships in the case of a vessel for the transport of liquefied petroleum gas (LPG). The ML algorithm is learned from shipboard automation system measurement data, noon logbook reports, and related meteorological and oceanographic data. The model is tested with generalized linear model (GLM) regression, multilayer preceptor (MLP), support vector machine (SVM), and random forest (RF). Upon verification of modeling framework and analyzing the results to improve the prediction accuracy, the best numeric prediction algorithm is selected based on standard evaluation metrics for regression, i.e., primarily root mean square error (RMSE) and relative absolute error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant measurement data, RF exhibits the lowest RMSE of 17.2632 and RAE 2.304%. Furthermore, this paper elaborates the selection of measurement data, the analysis of input parameters, and their significance in building the prediction model and selection of suitable output variables by the ship’s energy efficiency management plan (SEEMP). In addition, discretization was introduced to allow the end user to interpret the prediction results, placing them in the context of the actual ship operations. The results presented in this research can assist in setting up a decision support system whenever energy consumption savings in a marine transport are at stake.
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spelling pubmed-80739422021-04-27 Predicting Seagoing Ship Energy Efficiency from the Operational Data Vorkapić, Aleksandar Radonja, Radoslav Martinčić-Ipšić, Sanda Sensors (Basel) Article This paper presents the application of machine learning (ML) methods in setting up a model with the aim of predicting the energy efficiency of seagoing ships in the case of a vessel for the transport of liquefied petroleum gas (LPG). The ML algorithm is learned from shipboard automation system measurement data, noon logbook reports, and related meteorological and oceanographic data. The model is tested with generalized linear model (GLM) regression, multilayer preceptor (MLP), support vector machine (SVM), and random forest (RF). Upon verification of modeling framework and analyzing the results to improve the prediction accuracy, the best numeric prediction algorithm is selected based on standard evaluation metrics for regression, i.e., primarily root mean square error (RMSE) and relative absolute error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant measurement data, RF exhibits the lowest RMSE of 17.2632 and RAE 2.304%. Furthermore, this paper elaborates the selection of measurement data, the analysis of input parameters, and their significance in building the prediction model and selection of suitable output variables by the ship’s energy efficiency management plan (SEEMP). In addition, discretization was introduced to allow the end user to interpret the prediction results, placing them in the context of the actual ship operations. The results presented in this research can assist in setting up a decision support system whenever energy consumption savings in a marine transport are at stake. MDPI 2021-04-17 /pmc/articles/PMC8073942/ /pubmed/33920530 http://dx.doi.org/10.3390/s21082832 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vorkapić, Aleksandar
Radonja, Radoslav
Martinčić-Ipšić, Sanda
Predicting Seagoing Ship Energy Efficiency from the Operational Data
title Predicting Seagoing Ship Energy Efficiency from the Operational Data
title_full Predicting Seagoing Ship Energy Efficiency from the Operational Data
title_fullStr Predicting Seagoing Ship Energy Efficiency from the Operational Data
title_full_unstemmed Predicting Seagoing Ship Energy Efficiency from the Operational Data
title_short Predicting Seagoing Ship Energy Efficiency from the Operational Data
title_sort predicting seagoing ship energy efficiency from the operational data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073942/
https://www.ncbi.nlm.nih.gov/pubmed/33920530
http://dx.doi.org/10.3390/s21082832
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