  Climate control of buildings makes up a significant portion of global energy
consumption, with groundwater heat pumps providing a suitable alternative. To
prevent possibly negative interactions between heat pumps throughout a city,
city planners have to optimize their layouts in the future. We develop a novel
data-driven approach for building small-scale surrogates for modelling the
thermal plumes generated by groundwater heat pumps in the surrounding
subsurface water. Building on a data set generated from 2D numerical
simulations, we train a convolutional neural network for predicting
steady-state subsurface temperature fields from a given subsurface velocity
field. We show that compared to existing models ours can capture more complex
dynamics while still being quick to compute. The resulting surrogate is thus
well-suited for interactive design tools by city planners.
