Abstract
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.
Highlights
As a critical transportation sector which accounts for estimated 80% of the volume of world trade [1], the shipping industry has been striving to reduce the environmental footprint in recognition of climate change challenges
We present the evaluations of the two neural networks by comparing them to each other and to the ETA-pilot, which was described in our ethnographic study
“iteration” or “combination” of the ethnography and big data analytics may illustrate the complementary natures of these two methodologies [44] and the need to develop a richer methodological framework for future maritime research [61]. This interdisciplinary study provides a detailed description of the use of both the ethnographic research method and big data analytics to explore the phenomena under study and proposes a way forward
Summary
As a critical transportation sector which accounts for estimated 80% of the volume of world trade [1], the shipping industry has been striving to reduce the environmental footprint in recognition of climate change challenges. In April 2018, International Maritime Organization (IMO) adopted an Initial IMO Strategy on reduction of Greenhouse Gas (GHG) from ships, aiming that we should reduce the total annual GHG emissions by at least 50% by 2050 compared to 2008 and further strengthen the energy-efficiency (EE) design requirements for ships [2]. EE in shipping is defined as energy used per transported goods and distance, e.g., kg of fuel per tonne cargo and nautical mile [3]. It is a multi-facetted issue and the ability of a vessel to decrease GHG emissions is a coordinated effort between speed control, navigational decisions, engine maintenance, hull resistance, propeller efficiency, scrubber systems etc. The intensification of EE, i.e., decreased fuel consumption while
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