Energy efficiency is an increasingly important quality attribute for software, particularly for mobile apps. Just like any other software attribute, energy behavior of mobile apps should be properly tested prior to their release. However, mobile apps are riddled with energy defects, as currently there is a lack of proper energy testing tools. Indeed, energy testing is a fledgling area of research and recent advances have mainly focused on test input generation. This paper presents ACETON, the first approach aimed at solving the oracle problem for testing the energy behavior of mobile apps. ACETON employs Deep Learning to automatically construct an oracle that not only determines whether a test execution reveals an energy defect, but also the type of energy defect. By carefully selecting features that can be monitored on any app and mobile device, we are assured the oracle constructed using ACETON is highly reusable. Our experiments show that the oracle produced by ACETON is both highly accurate, achieving an overall precision and recall of 99%, and efficient, detecting the existence of energy defects in only 37 milliseconds on average.