As Germany phases out nuclear and coal-fired power, a power shortage is emerging in northern Germany, while southern Germany has a power surplus. The shift from centralized to decentralized renewable energy generation poses a challenge for the power grid, as it must integrate many small generators and transport electricity across regions. 

The “PrognoNetz” project has as a target to develop a smart, weather-dependent electricity load capacity forecast. A dense sensor network collects high-resolution weather data along the power lines to model weather conditions in 3D, as well as over time and along the sag of the lines. Based on historical weather data, the power capacity for the next two days can be predicted more accurately, allowing for more efficient use of the grid. This makes it easier for grid operators to plan power generation and electricity trading in advance.

In her work, Na Shen is investigating efficient, UAV-assisted concepts for the installation and maintenance of the sensor stations. This includes a gripper arm for securing, mounting, and removing the sensor systems. In addition, computer vision algorithms have been developed that detect the relative position and orientation of the UAV relative to the installation site, thereby supporting control and installation.