The port industry is transforming into a smart port thanks to technological advancements and environmental expectations. Developing a sustainable maritime transportation system and its beneficial electrification as a proven approach in emissions reduction are gathering momentum due to technological growth. Global containerization leads to high electricity demand at container terminals, and the electricity demand is highly dynamic and dependent on different operation processes. The approach of this paper is to forecast the hourly peak load demand and short-term electricity demand profile in a container terminal. The correctly forecasted electricity demand profile is crucial for less expensive and reliable power operation and planning. First, Artificial Neural Network (ANN)method is used to predict the container terminal baseload demand. Second, the worst-case simultaneous peak load is estimated. Third, the day-ahead load profile is modeled based on the handling operation scheduled for the day. The approach is implemented at the container terminal in Port of Gävle, and the results, including the baseload forecasting, the peak power demand, and the hourly load profile modeling by 2030, have been used in dialogue with the local energy company for the future predicted need of load.