A machine learning approach to energy pile design

Abstract

Incorporating ground heat exchangers (GHEs) into building foundations allows them to also provide thermal energy for space heating and cooling. However, this introduces certain constraints to ground-source heat pump (GSHP) design, such as on the geometry, and thus a different design approach is required. One such approach, introduced in this article, uses machine learning techniques to very quickly and accurately determine the maximum amount of thermal energy that can reasonably be provided. A comprehensive validation of this methodology for energy piles is presented, using different geometries and thermal load distributions, drawing conclusions about how the approach can best be utilised.