We are now witnessing the extensive deployment of drones in a diverse set of applications with Machine Learning (ML) constituting a key enabler empowering the uptake of drone technology. With the advancements of robotics and edge computing, on-board ML is on the uprise. However, testing ML solutions for drones before release to production is a daunting task for ML practitioners. This usually involves the testing on a robotics emulator to collect various key performance indicators ranging from algorithm correctness to resource utilization. Thus, to thoroughly evaluate performance, a true understanding of the ML algorithm impact on the drones most scarce resource is required. Without a doubt, this is the drones battery, which entails continuously monitoring energy consumption. In this paper we introduce HornEt, a modular framework enabling the customization and composition of various monitorable components to produce realistic energy models that can be used during the testing of ML-driven drone applications. To show the wide applicability of our framework, we introduce a proof-of-concept use-case illustrating the energy profiling of a drone application at different levels of granularity.