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Data Driven Energy Efficiency of Ships
Högskolan i Halmstad, Akademin för informationsteknologi.
2022 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

Decreasing the fuel consumption and thus greenhouse gas emissions of vessels has emerged as a critical topic for both ship operators and policy makers in recent years. The speed of vessels has long been recognized to have highest impact on fuel consumption. The solution suggestions like "speed optimization" and "speed reduction" are ongoing discussion topics for International Maritime Organization. The aim of this study are to develop a speed optimization model using time-constrained genetic algorithms (GA). Subsequent to this, this paper also presents the application of machine learning (ML) regression methods in setting up a model with the aim of predicting the fuel consumption of vessels. Local outlier factor algorithm is used to eliminate outlier in prediction features. In boosting and tree-based regression prediction methods, the overfitting problem is observed after hyperparameter tuning. Early stopping technique is applied for overfitted models.In this study, speed is also found as the most important feature for fuel consumption prediction models. On the other hand, GA evaluation results showed that random modifications in default speed profile can increase GA performance and thus fuel savings more than constant speed limits during voyages. The results of GA also indicate that using high crossover rates and low mutations rates can increase fuel saving.Further research is recommended to include fuel and bunker prices to determine more accurate fuel efficiency.

sted, utgiver, år, opplag, sider
2022. , s. 66
Serie
Halmstad University Dissertations
Emneord [en]
Local outlier factor, k-nearest neighbors, random forest, gradient boosting, support vector machines, ensemble learning, ship speed optimization, genetic algorithm, DEAP, HyperOpt
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-47442OAI: oai:DiVA.org:hh-47442DiVA, id: diva2:1677761
Eksternt samarbeid
RISE
Fag / kurs
Computer science and engineering
Utdanningsprogram
Master's Programme in Information Technology, 120 credits
Veileder
Tilgjengelig fra: 2022-06-25 Laget: 2022-06-28 Sist oppdatert: 2022-06-29bibliografisk kontrollert

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