Abstract
The lack of open-source platforms capable of integrated operational modeling and multi-scenario decarbonization analysis, often hinders data-driven decision-making in the maritime sector. To address this gap, this paper presents an open-source, multi-agent, discrete-event simulator capable of accurately forecasting the energy consumption associated with the diverse assets and activities within a container terminal. The tool's modular architecture enables transparent evaluations of operational strategies and decarbonization alternatives by allowing users to systematically modify inputs or alter embedded energy modules. The tool's capabilities were validated through a case study of a medium-sized Portuguese container terminal. For this particular port, findings indicate that installing three onshore power supply (OPS) units and fully electrifying the internal truck fleet yields the most substantial emission reductions. However, these interventions result in a two-fold increase in daily electricity demand, potentially straining grid capacity. This finding underscores that the effectiveness of terminal electrification as a decarbonization strategy ultimately depends on a simultaneous transition to a decarbonized and secure energy supply.

Abstract
Abstract In the coming years, seaports will undergo significant electrification process, moving away from fossil fuels. In such new reality, obtaining accurate electricity load forecasting is critical for reducing costs, planning infrastructure improvements, and ensuring a stable energy supply. However, studies specifically addressing this need in ports are scarce. This paper presents several novel Long Short-Term memory (LSTM) models for forecasting the electricity demand of a highly electrified port, using the Port of Sines as a case study. These models incorporate operational data, such as vessel arrival schedules and quay crane usage, to enhance forecasting accuracy. Our results show that including these variables significantly improves forecast accuracy, reducing the Mean Absolute Percentage Error (MAPE) from 10.55% to 3.59% compared to models relying solely on historical data. This research provides a robust framework for ports to improve energy management and supports the broader goals of energy efficiency and sustainability in the maritime industry.