In this paper, we consider observations from a series of smart meters that are either completely or partially aggregated, and our aim is to estimate the metering hierarchy. We propose to estimate this important metadata through a novel adaptation of the Chow–Liu tree learning procedure. Our approach takes into account prior knowledge from a set of dominance conditions that are easily elicited from the consumption data. In addition to more traditional correlation-based approaches we also introduce a distance-correlation-based method for detecting edges. Synthetic experiments show the benefits of distance correlation and the dominance conditions in recovering tree structure. The paper concludes with a real-world application of the method to infer energy metering hierarchies in a library building.