A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps

TL;DR

This paper introduces a deep learning model that predicts thermal plumes from groundwater heat pumps, aiding city planners in optimizing heat pump layouts to prevent negative interactions and improve energy efficiency.

Contribution

A novel data-driven convolutional neural network surrogate model for simulating subsurface temperature fields caused by groundwater heat pumps.

Findings

The model captures complex thermal dynamics more accurately than existing models.

It is computationally efficient for interactive city planning tools.

The approach is validated using 2D numerical simulation data.

Abstract

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.