This chapter proposes an integrated methodology to enhance energy autonomy at the microgrid level and mitigate the challenges associated with reverse power flow. Reverse power flow, which occurs when electricity flows in the opposite direction of standard grid operations, typically arises when distributed energy production exceeds local demand. This phenomenon can lead to risks such as inefficient operations, equipment damage, grid instability, and energy losses. To address reverse power flow and promote energy autonomy, an algorithm based on predictive models is introduced. The proposed framework employs deep learning models to forecast electricity generation from photovoltaic (PV) systems and consumption within the microgrid. These forecasts are subsequently integrated into an optimization algorithm that schedules flexible loads, including electric vehicles (EVs), to align with anticipated energy patterns. A payback study is conducted to evaluate the effects of optimized load scheduling and energy storage on the microgrid’s autonomy. The analysis also examines the impact of varying battery capacities and EV fleet sizes. Results indicate that forecast-based load shifting can significantly reduce reverse power flow, particularly when managing larger flexible loads. Energy storage is found to complement load shifting, further enhancing microgrid performance. Nonetheless, the effectiveness of these strategies is contingent upon the accuracy of forecasts and the capacity of storage systems.