Accurate fault detection in district heating and cooling systems remains a challenge due to limitations in traditional simulation models. This paper explores a hybrid modelling approach that combines physics-based models with artificial neural networks to address this issue. Two methods of integrating these models are tested on simulated scenarios that replicate complex component behaviour. The first method applies corrections after full system simulation, while the second corrects outputs at the component level in real time. Both approaches reduce the discrepancy between simulated and reference data, but only the second method shows clear architectural changes based on component disturbances. This suggests a stronger potential for identifying faulty behaviour without the need for labelled datasets. The results demonstrate the value of integrating data-driven corrections at the component level to improve simulation accuracy and support fault detection in district heating and cooling systems.