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.
@article{arxiv.2203.14961,
title = {A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps},
author = {Raphael Leiteritz and Kyle Davis and Miriam Schulte and Dirk Pflüger},
journal= {arXiv preprint arXiv:2203.14961},
year = {2022}
}