A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps
This addresses city planning challenges for sustainable building climate control, though it is incremental as it builds on existing simulation data and neural network methods.
The paper tackles the problem of predicting thermal plumes from groundwater heat pumps to optimize city layouts, developing a data-driven surrogate model that captures complex dynamics while being computationally efficient for interactive design tools.
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.