This paper introduces a comprehensive approach for calculating energy-efficient flight paths for unmanned aerial vehicles (UAVs) operating in urban environments. The primary objective is to minimize energy consumption by exploiting local wind phenomena, specifically targeting upwind and tailwind regions resulting from the airflow around buildings. The flight path planning algorithm uses a precalculated wind field to optimize the flight path. To achieve optimized flight trajectories, a customized A-star-Algorithm, enhanced with path smoothing techniques, is applied. A novel energy-distance map forms the base for the A-star heuristic function, which incorporates the key influential factors. The proposed approach is demonstrated using a benchmark scenario involving a delivery UAV, where energy-efficient flight paths are compared against the shortest way trajectories across 12 distinct scenarios. The results demonstrate significant energy savings potential when flying in urban areas by exploiting knowledge of the current wind conditions.