Vessel-Centric Energy Demand Modelling Framework for Renewable Energy Integration in Inland Ports and Waterways

Decarbonisation of inland waterway transport requires reliable estimates of vessel energy demand under real operating conditions, because these estimates influence the sizing, location, and timing of future energy infrastructure. However, there remains a gap between the availability of public vessel movement data and the ability to convert that data into a physically meaningful, time-resolved estimates of propulsion energy demand. This study presents and validates a behaviour-led framework for estimating propulsion energy consumption and fuel use for inland waterway vessels using sparsely available public data. The proposed framework reconstructs propulsion-related energy consumption as a function of time-resolved operation. Vessel movement derived from Automatic Identification System data is combined with environmental conditions and physically defined relationships to estimate propulsion power, energy consumption, and fuel use at the timestep level. The model is validated using a dataset of six anonymised vessels, which represent varied operational behaviour across the selected inland waterway context, for which time-resolved operator-provided propulsion and fuel consumption data are available. Validation results demonstrate that the framework reproduces the temporal structure of propulsion demand across the analysed vessel operations. Agreement in magnitude is achieved under conditions where vessel resistance characteristics remain relatively stable. In some operating conditions, systematic underestimation of propulsion demand is observed, consistent with unrepresented resistance effects, including those potentially associated with vessel loading. The findings demonstrate that time-resolved propulsion energy consumption can be reconstructed using publicly available data, providing a scalable basis for analysing inland waterway vessel operations and identifying spatial and temporal energy demand patterns to support infrastructure planning.