Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand estimation, this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source, the grid and the electric vehicles. The main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous records. Therefore, various forecasters based on Long Short-term Memory, Gated Recurrent Unit, and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University (Italy). The developed forecasters have been evaluated and compared according to different metrics, including R, RMSE, MAE, and MAPE. We found that the obtained R values for both PV power generation and energy demand ranged between 97% and 98%. These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.