The TwinPort methodology integrates data acquisition, forecasting, and decision support within a unified Digital Twin framework for port energy and operations. The approach combines historical and real-time data streams with interpretable machine-learning models to support operational planning and evaluation.
Data are collected from distributed energy resources, full-electric/hybrid-electric tugboats, onshore power supply systems, and port microgrid infrastructure. These data are processed through feature scaling and machine-learning-driven forecasting pipelines designed to produce transparent and explainable outputs. Forecasting models support electricity demand estimation and vessel coordination, while allowing expert review and feedback during model validation and performance assessment.
TwinPort develops a Digital Twin platform tailored to full-electric/hybrid-electric tug berths within the Grand Harbour (Malta). The platform enables real-time simulation, forecasting, and coordination across energy and logistics domains. The project builds on prior research by the UM team in port infrastructure electrification, port microgrids, and hybrid tugboat propulsion systems, extending this work through the integration of electricity demand forecasting and operational decision support within a single Digital Twin environment.
Through the development and validation of this Digital Twin prototype, TwinPort establishes Malta as a living testbed for sustainable port digitalisation, aligned with the EU Digital Strategy and the European Green Deal. The methodology emphasises data-driven operation, transparency, and replicability to support wider adoption in similar port contexts.
