Increasing population and urbanization call for smarter cities where the cycles of matter and energy are optimized, notably in buildings which are actually a source of pollution consuming a lot of energy. The efficiency of building energy has been improved by modelling earth-air heat exchangers, yet selecting the suitable models is challenging. Here we review data-driven earth-air heat exchanger models used for buildings. We discuss issues brought about by assumptions, unmeasured disruptions, and uncertainties in numerical and experimental works. We found that high accuracy can be reached if sufficient data is available. Models are appropriate for real-time activity due to their structure simplicity, yet they display a poor generalization capacity. Model development is limited by the constrained parameters and the complex boundary conditions of the heat exchangers.

不断增长的人口和城市化需要更智能的城市，其中物质和能量的循环得到优化，特别是在建筑物中，这些建筑物实际上是消耗大量能源的污染源。通过对地-气换热器建模，建筑能源的效率得到了提高，但选择合适的模型具有挑战性。在这里，我们回顾了用于建筑物的数据驱动的地气换热器模型。我们讨论数值和实验工作中的假设、未测量的中断和不确定性带来的问题。我们发现如果有足够的数据可以达到高精度。由于结构简单，模型适用于实时活动，但它们的泛化能力较差。