Abstract
Nowadays, the impact of the ships on the World economy is enormous, considering that every ship needs fuel to sail from source to destination. It requires a lot of fuel, and therefore, there is a need to monitor and predict a ship’s average fuel consumption. However, although there are much models available to predict a ship’s consumption, most of them rely on a uniform time set. Here we show the model of predicting external influences to ship’s average fuel consumption based on a non-uniform time set. The model is based on the numeric fitting of recorded data. The first set of recorded data was used to develop the model, while the second set was used for validation. Statistical quality measures have been used to choose the optimal fitting function for the model. According to statistical measures, the Gaussian 7, Fourier 8, and smoothing spline fitting functions were chosen as optimal algorithms for model development. In addition to extensive data analysis, there is an algorithm for filter length determination for the preprocessing of raw data. This research is of interest to corporate logistics departments in charge of ensuring adequate fuel for fleets when and where required.
Highlights
The effects of the marine environment and other causes on fuel consumption can be examined by various parameters and various approaches
There are much models available to predict a ship’s consumption, most of them rely on a uniform time set
As stated in [4], fuel consumption reduction cannot be established without first exploring standard fuel consumption prediction models
Summary
The effects of the marine environment and other causes on fuel consumption can be examined by various parameters and various approaches. Concerning the previously mentioned research problem of ships fuel consumption, the following hypotheses is that the average daily consumption could be predicted seasonally and yearly. The aim of this paper is to find the fitting function to establish the dynamics of the ships daily fuel consumption, using a simple and processing cost-efficient model. The paper is organized as follows: the second section defines quality measures and fitting curves used in the research; the third section explains the methodology, while the fourth presents the results The latter are obtained using the known data that do not pertain to consecutive days, but rather cover an irregular day sequence.
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