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From ethnographic research to big data analytics - A case of maritime energy-efficiency optimization

Man, Yemao; Sturm, Tobias; Lundh, Monica; MacKinnon, Scott N.

Abstract (englisch):
The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a big data approach to achieve energy efficiency (EE). Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this interdisciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy research

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Verlagsausgabe §
DOI: 10.5445/IR/1000118843
Veröffentlicht am 04.05.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2076-3417
KITopen-ID: 1000118843
Erschienen in Applied Sciences
Band 10
Heft 6
Seiten Article no.: 2134
Vorab online veröffentlicht am 21.03.2020
Schlagwörter ethnography; thick data; big data; machine learning; maritime energy efficiency; interface design; decision support; energy management; knowledge development
Nachgewiesen in Web of Science
Scopus
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