From ethnographic research to big data analytics - A case of maritime energy-efficiency optimization
Artikel i vetenskaplig tidskrift, 2020

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 bigdata 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 inter disciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy research. © 2020 by the authors.

Thick data

Knowledge development

Machine learning

Maritime energy efficiency

Energy management

Decision support

Ethnography

Bigdata

Interface design

Författare

Yemao Man

Göteborgs universitet

Tobias Sturm

Karlsruher Institut für Technologie (KIT)

Monica Lundh

Chalmers, Mekanik och maritima vetenskaper, Maritima studier

Scott Mackinnon

Chalmers, Mekanik och maritima vetenskaper, Maritima studier

Applied Sciences (Switzerland)

20763417 (eISSN)

Vol. 10 6 2134

Ämneskategorier

Annan data- och informationsvetenskap

Övrig annan teknik

Miljöanalys och bygginformationsteknik

DOI

10.3390/app10062134

Mer information

Senast uppdaterat

2021-09-16