# From Ethnographic Research to Big Data Analytics--A Case of Maritime Energy-Efficiency Optimization
## 4 Applying Big Data Analytics for Maritime Energy Efficiency
### Evaluation
#### 4.3.2 General Prediction Benchmark  
We also compare our MLP and SOM models to the ETA-pilot tool. Ground truth for the experiments is the summed measured fuel consumption per journey for the Ship 1 in 2015. A total of 218 journeys could be matched to the captain's log, which includes the estimation of the ETA-pilot that was issued before each journey, depending on the weather forecast. The average over summed relative error per journey for each model is shown in Table 6. One limitation we have is that there is no data specifying, whether journeys were performed using the recommendations of the ETA-pilot or not. The beginning and end of the voyages were not included in the experiments, because the ETA-pilot predictions do not include maneuvering close to port. The results of the models are based on the predicted fuel consumptions for every recorded data point for each journey. All predictions for one journey were summed and then compared to the expected fuel consumption for the given journey. The relative distribution of the difference to the expected fuel consumption is plotted (see Figure 11). Figure 10: Accuracy (acc) and generalization (gen) of MLP and self-organizing map (SOM) models. The average relative error and the standard distribution is calculated from the same result set as the box plots in Figure 11. Boxes mark second quartile, median and third quartile. Whiskers indicate first and fourth quartile. As listed in Table 6 and shown in Figure 11, both the MLP and SOM model on average yield a better prediction than the baseline (ETA-pilot). Only our MLP model shows a smaller spread of the average relative error, compared to the baseline. We conclude that an MLP is better suitable than SOM to estimate the fuel consumption for the given inputs of parameter group D or E in Table 4.  
Overall the experiment and development work presented in this phase of \"second diamond\" shows its feasibility and potential value in using historical big data to predict fuel consumption and support decision making in eco-driving. Although the models, SOMs and MLPs were still not entirely robust, given the data set that we obtained, they are able to outperform the baseline tool used onboard in terms of fuel consumption prediction, as it was essentially using the historical data rather than following the hardcoded programme. By extracting values out of the large amount of data, there is a huge potential to support the development of knowledge of the seafarers and maritime EE optimization operations. At the same time, the impact of using big data analytics may also generate important insights regarding interface design, shore-based management and perhaps future research of ethnography within this domain. The big data analytics approach, which was initially informed by the ethnographic research in the field, builds up a basis to discuss how we can provide decision support by showing real-time fuel consumption prediction. By the time the ethnographic research and the experiments were done, the research team presented the findings to the shipping company and obtained their positive feedback. They were very interested in the interdisciplinary study. In the initial discussion, they raised some sociotechnical questions that point to the needs for further research, for example, how to make the predictions more robust, what data were further needed and considerations regarding how the management and practitioners shall better accommodate the introduction of big data analytics \begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Average** & **Standard Deviation** \\ \hline Baseline (ETA-plot) & 5.4 & \(\pm\)17.8 \\ MLP & 1.4 & \(\pm\)16.3 \\ SOM & 1.1 & \(\pm\)20.5 \\ MLP(Weather) & 1.9 & \(\pm\)15.8 \\ SOM(Weather) & 4.2 & \(\pm\)21.4 \\ \hline \end{tabular}
\end{table} 
Table 6: Average over summed relative error per journey for each model. Figure 11: Relative error to measured fuel consumption per journey for baseline, MLP and SOM. onboard ships to enable improved organizational EE practices.