The emerging demand for electric bicycles in recent years has prompted several bike-sharing systems (BSS) around the world to adapt their service to a new wave of commuters. Many of these systems have incorporated electric bikes into their network while still maintaining the use of regular mechanical bicycles. However, the presence of two types of bikes in a BSS network may impact how rebalancing operations should be conducted in the system. Regular and electric bikes may exhibit distinct demand patterns throughout the day, which can hinder efficient planning of such operations. In this paper, we propose a new model that provides rebalancing recommendations based on the demand prediction for each type of bike. Additionally, we simulate the performance of our model under different scenarios, considering commuters' varying inclination to substitute their preferred bike with one of a different type. In one simulated scenario, our model successfully reduced lost demand by approximately 40% compared to the current rebalancing strategy employed by the real-world BSS studied. Moreover, it decreased the number of rebalancing operations conducted by approximately 12%, resulting in benefits not only in terms of cost reduction but also in reducing greenhouse gas emissions.