From Ethnographic Research to Big Data Analytics—A Case of Maritime Energy-Efficiency Optimization
| Autor: | Scott MacKinnon, Yemao Man, Monica Lundh, Tobias Sturm |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Decision support system
decision support energy management Energy management Computer science 020209 energy Energy (esotericism) knowledge development Big data 02 engineering and technology lcsh:Technology ethnography lcsh:Chemistry big data Ethnography 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences General Materials Science lcsh:QH301-705.5 Instrumentation 050107 human factors Management practices Fluid Flow and Transfer Processes lcsh:T business.industry Process Chemistry and Technology DATA processing & computer science 05 social sciences General Engineering maritime energy efficiency thick data Data science lcsh:QC1-999 Computer Science Applications machine learning lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Fuel efficiency ddc:004 lcsh:Engineering (General). Civil engineering (General) business lcsh:Physics interface design Efficient energy use |
| Zdroj: | Applied Sciences Volume 10 Issue 6 Applied Sciences, Vol 10, Iss 6, p 2134 (2020) Applied Sciences, 10 (6), Article no.: 2134 |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|