Effect of soil spatial variability on the long-term performance and malfunction probability of ground source heat pump systems

Ground source heat pump (GSHP) systems have gained increasing attention due to their high energy efficiency and sustainability for building cooling and heating. However, in subtropical and tropical regions (e.g. Hong Kong and Singapore), soil thermal imbalance poses significant challenges for GSHP systems as it leads to an increase in soil temperature and degradation of system performance. Previous studies often assumed uniform soil conditions and used constant soil properties when evaluating GSHP system performance, without considering spatial variability in natural geo-materials (e.g. soils and rocks) underground. Such simplifications may result in overestimations of soil heat transfer capability and contribute to the possibility of system malfunction. To evaluate the long-term performance and malfunction probability of GSHP systems for building cooling, this study proposes a stochastic analysis model that integrates Monte Carlo simulation (MCS) with a dynamically coupled GSHP system model to consider soil spatial variability. In the stochastic model, spatial variability in soil properties is modelled by random field theory and propagated through MCS to quantify its effects on energy efficiency and malfunction probability of GSHP systems. The effectiveness of the proposed model is demonstrated using an illustrative example of a GSHP system under cooling-dominated building thermal loads. Results show that soil stratigraphy and geo-material variability exhibit significant effects on the system’s long-term performance. This highlights the importance of accurate site characterization when evaluating soil thermal imbalance and the performance of GSHP systems.