A Novel Energy Management System for Cruise Ships Including Forecasting via LSTM

Abstract

The massive scale of the greenhouse gas (GHG) emissions due to the operation of cruise ships creates an acute need to develop cruise ship energy management systems (EMSs) that explicitly assess and mitigate GHG emissions. Renewable resources (RRs)-albeit their ubiquity in recent years-pose key challenges that need to be addressed so as to be efficiently utilized by cruise ship EMSs. To this end, in this paper, we propose a cruise ship EMS that optimizes the operation of controllable generators, battery storage system (BSS), controllable loads, and diesel purchase. The proposed EMS contemplates three objectives: minimization of total costs, mitigation of GHG emissions, and minimization of travel time in the case of an emergency. The proposed EMS harnesses long short-term memory networks (LSTMs) to forecast the generation of integrated PV panels and uncontrollable loads, and utilizes the forecasts to determine the optimal operations. The results illustrate the capabilities and effectiveness of the proposed EMS.