Step 3b: Scaling Benchmarking Data

The dataset was simulated in 2D with Feflow using cut-outs from interpolated hydrogeological measurements of the Munich, Germany, region. Heat pump locations are chosen based on realistic positions (but thinned out), and extraction rates are adapted to fit the available groundwater.

To prepare the data for machine learning, it was transformed from unstructured to structured data (3968x3968 cells) with Python. Inputs include hydrogeological parameters such as hydraulic conductivity [m/d], hydraulic head [m a.s.l.], aquifer thickness [m], and Darcy velocities [m/d], as well as 2D-embedded operational pump parameters such as maximum flow rate [m^3/d]. All hydrogeological parameters were extracted prior to heat pump operation.