A framework for the application of shipboard energy efficiency monitoring, operational data prediction and reporting

Aleksandar Vorkapić,Sanda Martinčić-Ipšić,Radoslav Radonja

doi:10.31217/p.35.1.1

Aleksandar Vorkapić, Sanda Martinčić-Ipšić + Show 1 more

Open Access

https://doi.org/10.31217/p.35.1.1

Copy DOI

Journal: Pomorstvo	Publication Date: Jun 30, 2021
Citations: 1	License type: cc-by

Affiliation: University of Rijeka

Abstract

In this study, a framework for the application of shipboard energy efficiency monitoring, operational data prediction and reporting based on the ship’s measurement data and meteorological and oceanographic data by the geographic position and time of navigation is presented. General system theory in synergy with machine learning (ML) is used to construct the framework. The general system theory is utilized for identification and transition of components of the proposed framework of energy efficiency monitoring and prediction. A systematic investigation of the internal and external environment is conducted, and the definition of information flow between the individual components provided. Then, the external opportunities and threats that the system faces were opposed to internal strengths and weaknesses to formulate strategies in which weaknesses and threats of the system are offset by existing strengths and probabilities. After assessing the results of the strengths, weaknesses, opportunities and threats (SWOT) and threats, opportunities, weaknesses and strengths (TOWS) analysis, it can be concluded that the proposed framework is feasible and widely applicable in the maritime industry. The novelty is that the proposed framework is using on-board data processing and is integrated into the existing ship monitoring, decision-making and reporting system, thus satisfying the prerequisites for simple application.

Highlights

IntroductionNowadays ships collect a large amount of data in measuring signals (temperature and pressure sensors, fuel flowmeter, anemometer, etc.) to supervise and manage ship systems
Nowadays ships collect a large amount of data in measuring signals to supervise and manage ship systems
Noticing the shortcomings of the existing system, in which the input variables are corrected with a time lag, and using data that are manually entered into the system, taking into account statutory changes in reporting related to energy consumption and carbon dioxide emissions, as well as insufficient processing of available measurement data, a new framework for the application of shipboard energy efficiency monitoring, operational data prediction and reporting based on the ship’s measurement data and meteorological and oceanographic data by the geographic position and time of navigation is proposed

Summary

Introduction

Nowadays ships collect a large amount of data in measuring signals (temperature and pressure sensors, fuel flowmeter, anemometer, etc.) to supervise and manage ship systems. Measurement data represent a long series of numerical values; they serve for current system monitoring and are rarely used for deep learning and extraction of additional information. Without the help of computer processing, especially with big data over a more extended period, it is difficult to extract knowledge and make quality decisions based on it. The Ship Energy Efficiency Management Plan (SEEMP), adopted by the IMO, is an operational measure that establishes a mechanism to improve the energy efficiency of a ship in a cost-effective manner. Part I provides a sustainable approach to monitoring ship and fleet efficiency over

Methods

Results

Conclusion