Battery life is critical for smart devices, but optimizing it requires cooperation from the entire software ecosystem. Wasteful software affects user perception about devices’ battery quality. Therefore, a large team within a producer of those smart devices is focused on identifying and correcting energy consumption bugs. Since the software ecosystem grows fast, that team faces a lot of suspect issues, from which only a small fraction turns out to be genuine. Our project aims to streamline energy-related bug processing in devices of the company and its partners, by automatically identifying anomalous behaviors related to battery drain using data mining and machine learning.