Prelim. data study of ocean ships fuel consumption (via speed)
By: Raymond Kwok
Tags: Machine Learning, Oceans, Data Analysis, Ship

a quick analysis of how ship’s speed behaves with data

operations decision as a problem of multi-variables

“Maritime Transport Is ‘Backbone of Global Trade and the Global Economy”

Said by the Secretary-General of the United Nation on World Maritime Day in 2016 [1], maritime transport is the ‘Backbone of Global Trade and the Global Economy’. And the rise of the concern of big data science is paving the way for the industry to innovate smarter way of decision-making about its route-planning and daily operations.

This article does not cover extended analysis of detailed correlations affecting the final energy and cost savings, due to limited public dataset, but giving its reader a taste of what data could have informed us of what is affecting our decisions, such that we could make use of that for a better, earlier-than-later decision-making.

The intended focus of this article is about fuel consumption, as it is a direct cause of costs and emissions. However, I could not find free on-route consumption data, so I am taking a workaround by studying the speed of ships instead. Nicolas Bialystocki et. al.[2] showed that the consumption of fuel of a ship is statistically non-linearly related to its speed, as the speed doubles, the rate of consumption goes up by four times. Therefore, the speed speaks for fuel consumption. This analysis will look for the relationship between the speed and other external variables, and in other words, to find the model that predicts speed from other variables.

Primary data source

In this preliminary study, I used 28 cruises’ data in 1998 and 1999, in the Gulf of Maine and Georges Bank area, downloadable from the website [3]of the Biological and Chemical Oceanography Data Management Office. The dataset includes 30 on-route parameters including date-time, lat-lon, wind speed and direction, and etc., and they are recorded with a 1-minute period. It would have been better if real cargo ship data is used, but unluckily that kind of data was not found.

Engineering features to describe our objective: speed

Features to model (or to predict) for the speed of the cruises come from the dataset, and three other ways.

Add features about the Sun and the Moon

Undoubtedly weather conditions such as devastating weather systems (like storms), wind, ocean circulations, sea waves will all resist and sometimes forcibly change the planned route of ships. Thus, weather gets directly involved in route-planning, for a safe and efficient sailing. The primary dataset contains very local wind, pressure, short-wave radiation, temperature, and humidity data, without more regional descriptions about the atmosphere and the ocean, which, from the Physics point of view, are affected by the Sun, and the Moon.

Therefore, the angles of the Sun, and the Moon as viewed by an observer on the ship’s way, and the Sun-Earth, and the Earth-Moon distances are added to the features set. The distances dictates the gravitational pull, while both the distances and angles decide the amount of heat reaching the Earth, and they will all affect the dynamic motions of the atmosphere and the sea, resulting in the change of weather. Apply non-linear functions to features

Reciprocal is used over the distances due to the fact that gravity decays over distance, and cosine is applied for the angles to reflect discounting effects. Combining current features

The speed of ship is obtained from the change of lat-lon position over time, and the cleanliness of sky is interpreted by the ratio of received radiation to the Sun’s distance and angle.

Illustration of angles to the Sun. Image credit: Clay Atchison, www.solarschoolhouse.org

Modeling and results

Linear regression, support-vector regression, and a 3-layer neural network are used for this modeling. Linear regression serves as a benchmark, while the other two add non-linearity which could better generalize our problem.

Performance of neural network (nn) and support vector machine (svr) against linear regression (lr), proportional to the error of the model, so the smaller the better. Column names represent the range of wind angles corresponded to the modeling data. Except for the first and the last column which are 15 degree wide, others are all 30 degree wide.

The whole dataset is divided into different wind angle ranges, basically by 30 degree each (the first and the last one are 15 degree each). It is a headwind when the angle is 180, and a tailwind when it is 0 degree. SVR has a stable performance over the ranges, and NN only performs better in headwind and tailwind scenarios, but not the cross wind.

In all cases, the most important features are always wind speed, which can be as much as 10 times more important than any one of the others, which is reasonable because it is a significant dragging or pushing force. If we focus on other features, as shown in the following heat map, the longitude value, parameters related to the Sun, and the Moon play an important roles as well. While the Sun and the Moon has direct and indirect influence to the sea and the weather over the region, the longitude may relate to region-dependent conditions as the route is more like east-west.

Feature importance. The darker the color, the more important it is. Crosses are placed for the most important features for each angle range.

Conclusion

This work shows that the wind, the Sun, and the Moon are indeed having a relationship towards the actual speed of ships. The significance of this result is to attempt to establish hidden understanding about how external factors are affecting how we have been operating, such that we can make use of these predictable external information to better guide future decisions. More direct evidence such as sea circulation, sea wave, weather systems in the same region, any crossing ships, and even any purely human decision that changed the speed are missing in this study, which may have even more contributions than known features. Currently, this work could only indirectly, partially account for some of them via existing evidence, such as sea wave through the Moon and the wind. With more comprehensive real-world data, we will be able to study the cost of our decision (such as route, speed, and acceleration) under different circumstance, which will teach us how to optimize better.

[1] Press release, https://www.un.org/press/en/2016/sgsm18129.doc.htm

[2] Nicolas Bialystocki et. al., 2016, On the estimation of ship’s fuel consumption and speed curve: A statistical approach

[3] Dataset, https://www.bco-dmo.org/dataset/2320, https://www.bco-dmo.org/dataset/2317