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.
Re-examination of the Two-Body Problem Using Our New General System Theory
