L2M – PORT-EM: Smart Port Energy & EV Infrastructure Management

The transition to low-carbon ocean economies requires innovative energy solutions for ports and coastal communities, where electrification of ships, ferries, and vehicles is accelerating. Building on prior research in probabilistic EV charging demand forecasting, energy consumption modeling, and emissions analysis, this project proposes a Smart Port Energy and EV Infrastructure Management Platform. The system integrates machine learning algorithms (LSTM, Random Forest, SVR etc.) to forecast highly variable port energy demand while incorporating tidal, offshore wind, and wave energy generation models. Coupled with embedded controllers for real-time load management, the platform enables demand balancing, peak reduction, and emissions minimization. Unlike existing generic energy management tools, this solution is purpose-built for the multi-source, high-variability environment of maritime ports, directly linking EV adoption with the ocean economy. The project will validate both the technical feasibility and market potential of ocean-focused smart grid solutions, advancing sustainable port operations in Atlantic Canada and beyond.

Faculty Supervisor:

Mohsin Jamil

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Energy and Utilities; Ocean Tech; Green/Alternative Energy

University:

Memorial University of Newfoundland

Program: